publications.bib

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@inproceedings{10.1007/978-3-032-04617-8_3,
  abstract = {Historical maps contain valuable, detailed survey data often unavailable elsewhere. Automatically extracting linear objects, such as fault lines, from scanned historical maps benefits diverse application areas, such as mining resource prediction. However, existing models encounter challenges in capturing adequate image context and spatial context. Insufficient image context leads to false detections by failing to distinguish desired linear objects from others with similar appearances. Meanwhile, insufficient spatial context hampers the accurate delineation of elongated, slender-shaped linear objects. This paper introduces the Linear Object Detection TRansformer (LDTR), which directly generates accurate vector graphs for linear objects from scanned map images. LDTR leverages multi-scale deformable attention to capture representative image context, reducing false detections. Furthermore, LDTR's innovative N-hop connectivity component explicitly encourages interactions among nodes within an N-hop neighborhood, enabling the model to learn sufficient spatial context for generating graphs with accurate connectivity. Experiments show that LDTR improves detection precision by 6{\%} and enhances line connectivity by 20{\%} over state-of-the-art baselines.},
  address = {Cham},
  author = {Duan, Weiwei
and Chiang, Yao-Yi
and Knoblock, Craig A.},
  booktitle = {Document Analysis and Recognition --  ICDAR 2025},
  editor = {Yin, Xu-Cheng
and Karatzas, Dimosthenis
and Lopresti, Daniel},
  isbn = {978-3-032-04617-8},
  pages = {40--59},
  publisher = {Springer Nature Switzerland},
  title = {LDTR: Linear Object Detection Transformer for Accurate Graph Generation by Learning the N-Hop Connectivity Information},
  url = {https://link.springer.com/chapter/10.1007/978-3-032-04617-8_3},
  year = {2026}
}

@inproceedings{10.1007/978-3-032-04617-8_4,
  abstract = {Text on historical maps provides valuable information for studies in history, economics, geography, and other related fields. Unlike structured or semi-structured documents, text on maps varies significantly in orientation, reading order, shape, and placement. Many modern methods can detect and transcribe text regions, but they struggle to effectively ``link'' the recognized text fragments, e.g., determining a multi-word place name. Existing layout analysis methods model word relationships to improve text understanding in structured documents, but they primarily rely on linguistic features and neglect geometric information, which is essential for handling map text. To address these challenges, we propose LIGHT, a novel multi-modal approach that integrates linguistic, image, and geometric features for linking text on historical maps. In particular, LIGHT includes a geometry-aware embedding module that encodes the polygonal coordinates of text regions to capture polygon shapes and their relative spatial positions on an image. LIGHT unifies this geometric information with the visual and linguistic token embeddings from LayoutLMv3, a pretrained layout analysis model. LIGHT uses the cross-modal information to predict the reading-order successor of each text instance directly with a bi-directional learning strategy that enhances sequence robustness. Experimental results show that LIGHT outperforms existing methods on the ICDAR 2024/2025 MapText Competition data, demonstrating the effectiveness of multi-modal learning for historical map text linking.},
  address = {Cham},
  author = {Lin, Yijun
and Olson, Rhett
and Wu, Junhan
and Chiang, Yao-Yi
and Weinman, Jerod},
  booktitle = {Document Analysis and Recognition --  ICDAR 2025},
  editor = {Yin, Xu-Cheng
and Karatzas, Dimosthenis
and Lopresti, Daniel},
  isbn = {978-3-032-04617-8},
  pages = {60--77},
  publisher = {Springer Nature Switzerland},
  title = {LIGHT: Multi-modal Text Linking on Historical Maps},
  url = {https://link.springer.com/chapter/10.1007/978-3-032-04617-8_4},
  year = {2026}
}

@inproceedings{10.1007/978-3-032-09530-5_26,
  abstract = {Locating new sources of critical minerals begins with understanding where these minerals have been found in the past. However, historical data about mineral occurrences is often locked in disparate, unstructured, and inconsistent formats, ranging from government databases to mining reports and journal articles. To address this challenge, we have developed a set of scalable technologies that extract, normalize, and semantically integrate information from these sources into a unified knowledge graph. Our approach combines ontology-driven modeling, large-language models for information extraction and classification, and tools for linking and validating data across sources. The result is a semantically enriched, queryable knowledge graph that supports reproducible analysis, expert validation, and geoscientific applications such as deposit classification and prospectivity modeling. Through this work, we have successfully integrated information from hundreds of thousands of records across multiple historical sources to build one of the world's largest repositories of structured data on critical minerals.},
  address = {Cham},
  author = {Knoblock, Craig A.
and Vu, Binh
and Shbita, Basel
and Chiang, Yao-Yi
and Krishna, Pothula Punith
and Lin, Xiao
and Muric, Goran
and Pyo, Jiyoon
and Trejo-Sheu, Adriana
and Ye, Meng},
  booktitle = {The Semantic Web -- ISWC 2025},
  editor = {Garijo, Daniel
and Kirrane, Sabrina
and Salatino, Angelo
and Shimizu, Cogan
and Acosta, Maribel
and Nuzzolese, Andrea Giovanni
and Ferrada, Sebasti{\'a}n
and Soulard, Thibaut
and Kozaki, Kouji
and Takeda, Hideaki
and Gentile, Anna Lisa},
  isbn = {978-3-032-09530-5},
  pages = {451--471},
  publisher = {Springer Nature Switzerland},
  title = {Exploiting LLMs and Semantic Technologies to Build a Knowledge Graph of Historical Mining Data},
  url = {https://link.springer.com/chapter/10.1007/978-3-032-09530-5_26},
  year = {2026}
}

@article{10.1016/j.jag.2025.104368,
  author = {Mai, Gengchen and Xie, Yiqun and Jia, Xiaowei and Lao, Ni and Rao, Jinmeng and Zhu, Qing and Liu, Zeping and Chiang, Yao-Yi and Jiao, Junfeng},
  doi = {10.1016/j.jag.2025.104368},
  issn = {1569-8432},
  journal = {International Journal of Applied Earth Observation and Geoinformation},
  month = {February},
  pages = {104368},
  publisher = {Elsevier BV},
  title = {Towards the next generation of Geospatial Artificial Intelligence},
  url = {https://doi.org/10.1016/j.jag.2025.104368},
  volume = {136},
  year = {2025}
}

@article{10.1080/15420353.2025.2580239,
  author = {Pai, Pi-Ling and Lu, Hsiang-Hsi and Su, Wen-Rong and Liao, Hsiung-Ming and Chan, Ta-Chien and Lin, Yijun and Chiang, Yao-Yi},
  doi = {10.1080/15420353.2025.2580239},
  issn = {1542-0361},
  journal = {Journal of Map & Geography Libraries},
  month = {November},
  pages = {1–23},
  publisher = {Informa UK Limited},
  title = {AI-Supported Smart Search for Multi-Period Historical Maps Using the mapKurator Place Name Index},
  url = {https://doi.org/10.1080/15420353.2025.2580239},
  year = {2025}
}

@inproceedings{10.1145/3589132.3625579,
  address = {New York, NY, USA},
  articleno = {35},
  author = {Kim, Jina and Li, Zekun and Lin, Yijun and Namgung, Min and Jang, Leeje and Chiang, Yao-Yi},
  booktitle = {Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems},
  doi = {10.1145/3589132.3625579},
  isbn = {9798400701689},
  keywords = {linked data, text spotter, historical maps, automatic system},
  location = {, Hamburg, Germany, },
  numpages = {4},
  publisher = {Association for Computing Machinery},
  series = {SIGSPATIAL '23},
  title = {The mapKurator System: A Complete Pipeline for Extracting and Linking Text from Historical Maps},
  url = {https://doi.org/10.1145/3589132.3625579},
  year = {2023}
}

@inproceedings{10.1145/3589132.3625648,
  address = {New York, NY, USA},
  articleno = {98},
  author = {Lin, Yijun and Chiang, Yao-Yi},
  booktitle = {Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems},
  doi = {10.1145/3589132.3625648},
  isbn = {9798400701689},
  keywords = {physics-guided machine learning, spatiotemporal predictive learning, spatial AI},
  location = {Hamburg, Germany},
  numpages = {11},
  publisher = {Association for Computing Machinery},
  series = {SIGSPATIAL '23},
  title = {Modeling Spatially Varying Physical Dynamics for Spatiotemporal Predictive Learning},
  url = {https://doi.org/10.1145/3589132.3625648},
  year = {2023}
}

@inproceedings{10.1145/3589132.3628368,
  address = {New York, NY, USA},
  articleno = {7},
  author = {Olson, Rhett M and Kim, Jina and Chiang, Yao-Yi},
  booktitle = {Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems},
  doi = {10.1145/3589132.3628368},
  isbn = {9798400701689},
  keywords = {temporal analysis, toponym, cartography, historical maps},
  location = {<conf-loc>, <city>Hamburg</city>, <country>Germany</country>, </conf-loc>},
  numpages = {2},
  publisher = {Association for Computing Machinery},
  series = {SIGSPATIAL '23},
  title = {An Automatic Approach to Finding Geographic Name Changes on Historical Maps},
  url = {https://doi.org/10.1145/3589132.3628368},
  year = {2023}
}

@inproceedings{10.1145/3589132.3628372,
  address = {New York, NY, USA},
  articleno = {11},
  author = {Kim, Jina and Chiang, Yao-Yi},
  booktitle = {Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems},
  doi = {10.1145/3589132.3628372},
  isbn = {9798400701689},
  keywords = {representation learning, online text, spatial triad},
  location = {<conf-loc>, <city>Hamburg</city>, <country>Germany</country>, </conf-loc>},
  numpages = {2},
  publisher = {Association for Computing Machinery},
  series = {SIGSPATIAL '23},
  title = {Towards Learning of Spatial Triad from Online Text},
  url = {https://doi.org/10.1145/3589132.3628372},
  year = {2023}
}

@inproceedings{10.1145/3637528.3671589,
  address = {New York, NY, USA},
  author = {Lin, Yijun and Chiang, Yao-Yi},
  booktitle = {Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  doi = {10.1145/3637528.3671589},
  isbn = {9798400704901},
  keywords = {historical maps, synthetic map data, text detection and recognition, text spotting},
  location = {Barcelona, Spain},
  numpages = {11},
  pages = {5387–5397},
  publisher = {Association for Computing Machinery},
  series = {KDD '24},
  title = {Hyper-Local Deformable Transformers for Text Spotting on Historical Maps},
  url = {https://doi.org/10.1145/3637528.3671589},
  year = {2024}
}

@inproceedings{10.1145/3681769.3698577,
  address = {New York, NY, USA},
  author = {Olson, Rhett and Kim, Jina and Chiang, Yao-Yi},
  booktitle = {Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data},
  doi = {10.1145/3681769.3698577},
  isbn = {9798400711480},
  keywords = {cartography, historical maps, minimum spanning tree, toponym},
  location = {Atlanta, GA, USA},
  numpages = {4},
  pages = {9–12},
  publisher = {Association for Computing Machinery},
  series = {GeoSearch '24},
  title = {Automatic Search of Multiword Place Names on Historical Maps},
  url = {https://doi.org/10.1145/3681769.3698577},
  year = {2024}
}

@inproceedings{10.1145/3681769.3698583,
  address = {New York, NY, USA},
  author = {Jang, Leeje and Chiang, Yao-Yi},
  booktitle = {Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data},
  doi = {10.1145/3681769.3698583},
  isbn = {9798400711480},
  location = {Atlanta, GA, USA},
  numpages = {7},
  pages = {29–35},
  publisher = {Association for Computing Machinery},
  series = {GeoSearch '24},
  title = {Enabling Semantic-Rich Location Search on Street View Imagery Using Multilingual POI Data},
  url = {https://doi.org/10.1145/3681769.3698583},
  year = {2024}
}

@inproceedings{10.1145/3681771.3699935,
  abstract = {Access to large-scale human trajectory data has significantly advanced the understanding of human mobility (HuMob) behavior for urban planning. However, these data are often concentrated in major cities, leaving smaller or less-monitored areas with limited information, undermining the performance of data-hungry machine learning models for HuMob prediction. This imbalance poses a challenge for cross-city mobility prediction, as many existing models are designed for single-city settings. To address this, we present CrossBag, a set of simple yet effective techniques to boost cross-city prediction. These techniques include context-aware spatiotemporal embeddings, masking types, and a progressive knowledge transfer method to incrementally adapt the target model while preserving useful patterns from the source model for stable cross-city transfer. Additionally, we propose a test-time trajectory refinement method using top-K guided beam search to prevent predictors from getting stuck in repetitive location predictions. We validate CrossBag on the large-scale multi-city dataset from the HuMob Challenge 2024, achieving a top-10 placement out of over 100 participating teams.},
  address = {New York, NY, USA},
  author = {Lee, JangHyeon and Chiang, Yao-Yi},
  booktitle = {Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Human Mobility Prediction Challenge},
  doi = {10.1145/3681771.3699935},
  isbn = {9798400711503},
  keywords = {Human mobility, Spatiotemporal, Transfer learning, Transformer},
  location = {Atlanta, GA, USA},
  numpages = {5},
  pages = {55–59},
  publisher = {Association for Computing Machinery},
  series = {HuMob'24},
  title = {CrossBag: A Bag of Tricks for Cross-City Mobility Prediction},
  url = {https://doi.org/10.1145/3681771.3699935},
  year = {2024}
}

@inproceedings{10.1145/3687123.3698297,
  address = {New York, NY, USA},
  author = {Chen, Theresa and Chiang, Yao-Yi},
  booktitle = {Proceedings of the 7th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery},
  doi = {10.1145/3687123.3698297},
  isbn = {9798400711763},
  keywords = {Multimodal machine learning, Spatial data, Species distribution modeling},
  location = {Atlanta, GA, USA},
  numpages = {11},
  pages = {110–120},
  publisher = {Association for Computing Machinery},
  series = {GeoAI '24},
  title = {MiTREE: Multi-input Transformer Ecoregion Encoder for Species Distribution Modelling},
  url = {https://doi.org/10.1145/3687123.3698297},
  year = {2024}
}

@inproceedings{10.1145/3687123.3698298,
  address = {New York, NY, USA},
  author = {Pyo, Jiyoon and Chiang, Yao-Yi},
  booktitle = {Proceedings of the 7th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery},
  doi = {10.1145/3687123.3698298},
  isbn = {9798400711763},
  keywords = {Spatial entity linkage, entity matching, geospatial data},
  location = {Atlanta, GA, USA},
  numpages = {13},
  pages = {86–98},
  publisher = {Association for Computing Machinery},
  series = {GeoAI '24},
  title = {Leveraging Large Language Models for Generating Labeled Mineral Site Record Linkage Data},
  url = {https://doi.org/10.1145/3687123.3698298},
  year = {2024}
}

@inproceedings{10.1145/3748636.3763209,
  author = {Lin, Fandel and Knoblock, Craig A. and Vu, Binh and Chiang, Yao-Yi},
  booktitle = {Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems},
  collection = {SIGSPATIAL ’25},
  doi = {10.1145/3748636.3763209},
  month = {November},
  pages = {1122–1125},
  publisher = {ACM},
  series = {SIGSPATIAL ’25},
  title = {Exploiting Polygon Metadata to Recolor Historical Maps},
  url = {https://doi.org/10.1145/3748636.3763209},
  year = {2025}
}

@inproceedings{10.1145/3748636.3763210,
  author = {Lin, Fandel and Knoblock, Craig A. and Vu, Binh and Shbita, Basel and Chiang, Yao-Yi},
  booktitle = {Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems},
  collection = {SIGSPATIAL ’25},
  doi = {10.1145/3748636.3763210},
  month = {November},
  pages = {1126–1129},
  publisher = {ACM},
  series = {SIGSPATIAL ’25},
  title = {Exploiting Polygon Metadata to Colorize Draft Maps},
  url = {https://doi.org/10.1145/3748636.3763210},
  year = {2025}
}

@inproceedings{10.1145/3748636.3764170,
  author = {Lin, Yijun and Chen, Theresa and Brungard, Colby and Grunwald, Sabine and Ives, Sue and Macander, Matt and Nawrocki, Timm and Chiang, Yao-Yi and Jelinski, Nic},
  booktitle = {Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems},
  collection = {SIGSPATIAL ’25},
  doi = {10.1145/3748636.3764170},
  month = {November},
  pages = {995–1007},
  publisher = {ACM},
  series = {SIGSPATIAL ’25},
  title = {Fine-Scale Soil Mapping in Alaska with Multimodal Machine Learning},
  url = {https://doi.org/10.1145/3748636.3764170},
  year = {2025}
}

@inproceedings{10.1145/3748636.3764174,
  author = {Li, Zekun and Grossman, Malcolm and Qasemi, Ehsan and Kulkarni, Mihir and Chen, Muhao and Chiang, Yao-Yi},
  booktitle = {Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems},
  collection = {SIGSPATIAL ’25},
  doi = {10.1145/3748636.3764174},
  month = {November},
  pages = {1042–1045},
  publisher = {ACM},
  series = {SIGSPATIAL ’25},
  title = {Benchmarking Geospatial Question Answering with MapQA},
  url = {https://doi.org/10.1145/3748636.3764174},
  year = {2025}
}

@inproceedings{10.1145/3748636.3764176,
  author = {Namgung, Min and Lee, Janghyeon and Ding, Fangyi and Chiang, Yao-Yi},
  booktitle = {Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems},
  collection = {SIGSPATIAL ’25},
  doi = {10.1145/3748636.3764176},
  month = {November},
  pages = {1056–1066},
  publisher = {ACM},
  series = {SIGSPATIAL ’25},
  title = {Transit for All: Mapping Equitable Bike2Subway Connection using Region Representation Learning},
  url = {https://doi.org/10.1145/3748636.3764176},
  year = {2025}
}

@inproceedings{10.1145/3748636.3764602,
  author = {Duan, Weiwei and Chiang, Yao-Yi and Chen, Theresa and Gerlek, Michael P. and Jang, Leeje and Kirsanova, Sofia and Knoblock, Craig A. and Lin, Fandel and Lin, Yijun and Li, Zekun and Minton, Steven N.},
  booktitle = {Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems},
  collection = {SIGSPATIAL ’25},
  doi = {10.1145/3748636.3764602},
  month = {November},
  pages = {717–728},
  publisher = {ACM},
  series = {SIGSPATIAL ’25},
  title = {DIGMAPPER: A Modular System for Automated Geologic Map Digitization},
  url = {https://doi.org/10.1145/3748636.3764602},
  year = {2025}
}

@inproceedings{10.1145/3748636.3771315,
  author = {Lin, Yijun and Lin, Fandel and Kim, Jina and Chiang, Yao-Yi},
  booktitle = {Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems},
  collection = {SIGSPATIAL ’25},
  doi = {10.1145/3748636.3771315},
  month = {November},
  pages = {1234–1237},
  publisher = {ACM},
  series = {SIGSPATIAL ’25},
  title = {MoVER: Modeling User Heterogeneity with Enriched Trajectory Representations for Human Mobility Prediction},
  url = {https://doi.org/10.1145/3748636.3771315},
  year = {2025}
}

@inproceedings{10.1145/3764914.3770888,
  author = {Xie, Junyi and Kim, Jina and Chiang, Yao-Yi and Zhao, Lingyi and Shafique, Khurram},
  booktitle = {Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Geospatial Anomaly Detection},
  collection = {GeoAnomalies ’25},
  doi = {10.1145/3764914.3770888},
  month = {November},
  pages = {56–59},
  publisher = {ACM},
  series = {GeoAnomalies ’25},
  title = {BeSTAD: Behavior-Aware Spatio-Temporal Anomaly Detection for Human Mobility Data},
  url = {https://doi.org/10.1145/3764914.3770888},
  year = {2025}
}

@inproceedings{10.1145/3764915.3770723,
  author = {Xie, Junyi and Jiao, Yuankun and Kim, Jina and Chiang, Yao-Yi and Zhao, Lingyi and Shafique, Khurram},
  booktitle = {Proceedings of the 1st ACM SIGSPATIAL International Workshop on Generative and Agentic AI for Multi-Modality Space-Time Intelligence},
  collection = {GeoGenAgent ’25},
  doi = {10.1145/3764915.3770723},
  month = {November},
  pages = {49–53},
  publisher = {ACM},
  series = {GeoGenAgent ’25},
  title = {HiCoTraj: Zero-Shot Demographic Reasoning via Hierarchical Chain-of-Thought Prompting from Trajectory},
  url = {https://doi.org/10.1145/3764915.3770723},
  year = {2025}
}

@inproceedings{10.1145/3764917.3771333,
  author = {Pyo, Jiyoon and Jiao, Yuankun and Chiang, Yao-Yi and Corey, Michael},
  booktitle = {Proceedings of the 1st ACM SIGSPATIAL International Workshop on Human-Centered Geospatial Computing},
  collection = {GeoHCC ’25},
  doi = {10.1145/3764917.3771333},
  month = {November},
  pages = {10–14},
  publisher = {ACM},
  series = {GeoHCC ’25},
  title = {Augmenting Human-Centered Racial Covenant Detection and Georeferencing with Plug-and-Play NLP Pipelines},
  url = {https://doi.org/10.1145/3764917.3771333},
  year = {2025}
}

@inproceedings{10.1145/3764917.3771334,
  author = {Kim, Jina and Jang, Leeje and Chiang, Yao-Yi and Wang, Guanyu and Pasco, Michelle C.},
  booktitle = {Proceedings of the 1st ACM SIGSPATIAL International Workshop on Human-Centered Geospatial Computing},
  collection = {GeoHCC ’25},
  doi = {10.1145/3764917.3771334},
  month = {November},
  pages = {15–19},
  publisher = {ACM},
  series = {GeoHCC ’25},
  title = {StreetLens: Enabling Human-Centered AI Agents for Neighborhood Assessment from Street View Imagery},
  url = {https://doi.org/10.1145/3764917.3771334},
  year = {2025}
}

@inproceedings{10.1145/3764920.3770590,
  author = {Kirsanova, Sofia and Duan, Weiwei and Chiang, YaoYi},
  booktitle = {Proceedings of the 4th ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data},
  collection = {GeoSearch ’25},
  doi = {10.1145/3764920.3770590},
  month = {November},
  pages = {35–38},
  publisher = {ACM},
  series = {GeoSearch ’25},
  title = {Detecting Legend Items on Historical Maps Using GPT-4o with In-Context Learning},
  url = {https://doi.org/10.1145/3764920.3770590},
  year = {2025}
}

@inproceedings{10.1145/3764925.3770911,
  author = {Namgung, Min and Lee, JangHyeon and Lin, Yijun and Chiang, Yao-Yi},
  booktitle = {Proceedings of the 1st ACM SIGSPATIAL International Workshop on Urban Mobility Foundation Models},
  collection = {UMFM ’25},
  doi = {10.1145/3764925.3770911},
  month = {November},
  pages = {10–12},
  publisher = {ACM},
  series = {UMFM ’25},
  title = {Capturing Shared and Unique Information in Multimodal Region Representations for Urban Mobility Prediction},
  url = {https://doi.org/10.1145/3764925.3770911},
  year = {2025}
}

@inproceedings{10.1145/3764926.3771941,
  author = {Kim, Jina and Chiang, Yao-Yi},
  booktitle = {Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Urban-AI},
  collection = {UrbanAI ’25},
  doi = {10.1145/3764926.3771941},
  month = {November},
  pages = {94–95},
  publisher = {ACM},
  series = {UrbanAI ’25},
  title = {Region Context from Unifying Points, Lines, and Polygons},
  url = {https://doi.org/10.1145/3764926.3771941},
  year = {2025}
}

@inproceedings{10.1145/3764926.3771947,
  author = {Namgung, Min and Chiang, Yao-Yi and Omitaomu, Olufemi A.},
  booktitle = {Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Urban-AI},
  collection = {UrbanAI ’25},
  doi = {10.1145/3764926.3771947},
  month = {November},
  pages = {27–36},
  publisher = {ACM},
  series = {UrbanAI ’25},
  title = {CareWELL: Multimodal Region Representation Learning with Spatial Contexts for Urban Health},
  url = {https://doi.org/10.1145/3764926.3771947},
  year = {2025}
}

@article{10.2139/ssrn.5943954,
  author = {Atshan, Samer and Namgung, Min and Lee, Janghyeon and Dhankhar, Anushikha and Khobragade, Pranali and Cole, Aidan and Ailshire, Jennifer A. and Adar, Sara D. and Chiang, Yao-Yi and Lee, Jinkook and Nichols, Emma},
  doi = {10.2139/ssrn.5943954},
  issn = {1556-5068},
  journal = {SSRN Electronic Journal},
  publisher = {Elsevier BV},
  title = {Validating Machine Learning–Derived Built Environment Measures From Google Street View for Urban Aging Research in India},
  url = {https://doi.org/10.2139/ssrn.5943954},
  year = {2025}
}

@article{9924526,
  author = {Zhang, Ying and Li, Muyang and Chen, Yuanchang and Chiang, Yao-Yi and Hua, Yunpeng},
  doi = {10.1109/TSG.2022.3214680},
  journal = {IEEE Transactions on Smart Grid},
  pages = {1-1},
  title = {A Constraint-based Routing and Charging Methodology for Battery Electric Vehicles with Deep Reinforcement Learning},
  year = {2022}
}

@inproceedings{Anastasiou2019-gk,
  address = {New York},
  author = {Anastasiou, Chrysovalantis and Lin, Jianfa and He, Chaoyang and
Chiang, Yao-Yi and Shahabi, Cyrus},
  booktitle = {{Proceedings of the 2nd ACM SIGSPATIAL workshop ARIC'19}},
  title = {{ADMSv2: A Modern Architecture for Transportation Data Management
and Analysis}},
  url-file = {papers/Anastasiou-et-al.-2019-ADMSv2-A-Modern-Architecture-for-Transportation-Data-Management-and-Analysis.pdf;papers/Anastasiou-et-al.-2019-ADMSv2-A-Modern-Architecture-for-Transportation-Data-Management-and-Analysis.pdf},
  year = {2019}
}

@article{avelar2023water,
  author = {Avelar Portillo, Lourdes Johanna and Kayser, Georgia L and Ko, Charlene and Vasquez, Angelica and Gonzalez, Jimena and Avelar, Diego Jose and Alvarenga, Nayib and Franklin, Meredith and Chiang, Yao-Yi},
  journal = {International Journal for Equity in Health},
  number = {1},
  pages = {1--19},
  publisher = {BioMed Central},
  title = {Water, Sanitation, and Hygiene (WaSH) insecurity in unhoused communities of Los Angeles, California},
  volume = {22},
  year = {2023}
}

@inproceedings{Avelar21-aag,
  author = {Avelar Portillo, L. J. and Chiang, Y.-Y. and Franklin, M. and Ko, C. and Vasquez, A. },
  booktitle = {{American Association of Geographers Annual Meeting}},
  title = {{Impacts of COVID-19 on Water, Sanitation, and Hygiene (WaSH) Access in Skid Row, Los Angeles}},
  year = {2021}
}

@inproceedings{Avelar21-icc,
  author = {Avelar Portillo, L. J. and Park, L. and Ko, C. and Vasquez, A. and Franklin, M. and Chiang, Y.-Y. },
  booktitle = {{{Proceedings of the 30th International Cartographic Conference
(ICC'21)}}},
  title = {{Measuring Closest Water, Sanitation, and Hygiene Facilities in Unhoused Communities of Los Angeles}},
  year = {2021}
}

@inproceedings{Chen2004-tl,
  address = {New York, NY, USA},
  author = {Chen, Ching-Chien and Knoblock, Craig A and Shahabi, Cyrus and
Chiang, Yao-Yi and Thakkar, Snehal},
  booktitle = {{{Proceedings of the 12th Annual ACM International Workshop on
Geographic Information Systems}}},
  doi = {10.1145/1032222.1032231},
  isbn = {9781581139792},
  location = {Washington DC, USA},
  month = {November},
  pages = {47--56},
  publisher = {ACM},
  series = {GIS '04},
  title = {{{{Automatically and Accurately Conflating Orthoimagery and
Street Maps}}}},
  url = {http://doi.acm.org/10.1145/1032222.1032231},
  url-file = {papers/Chen-et-al.-2004-Automatically-and-Accurately-Conflating-Orthoimagery-and-Street-Maps.pdf},
  year = {2004}
}

@article{Cheng2019-mi,
  address = {New York, NY, USA},
  author = {Cheng, Yuanbin and Chiang, Yao-Yi},
  doi = {10.1145/3307599.3307602},
  journal = {SIGSPATIAL Special},
  month = {January},
  number = {3},
  pages = {4--5},
  publisher = {Association for Computing Machinery},
  title = {{Automatic intersection extraction and building arrangement with
StarCraft II maps}},
  url = {https://doi.org/10.1145/3307599.3307602},
  url-file = {papers/Cheng-and-Chiang-2019-Automatic-intersection-extraction-and-building-arrangement-with-StarCraft-II-maps.pdf},
  volume = {10},
  year = {2019}
}

@inproceedings{Chiang2005-ny,
  address = {New York, NY, USA},
  author = {Chiang, Yao-Yi and Knoblock, Craig A and Chen, Ching-Chien},
  booktitle = {{Proceedings of the 13th annual ACM international workshop on
Geographic information systems}},
  doi = {10.1145/1097064.1097102},
  isbn = {9781595931467},
  location = {Bremen, Germany},
  month = {November},
  pages = {267--276},
  publisher = {ACM},
  series = {GIS '05},
  title = {{Automatic extraction of road intersections from raster maps}},
  url = {https://dl.acm.org/citation.cfm?doid=1097064.1097102},
  url-file = {papers/Chiang-et-al.-2005-Automatic-extraction-of-road-intersections-from-raster-maps.pdf},
  year = {2005}
}

@inproceedings{Chiang2006-ct,
  author = {Chiang, Yao-Yi Yi and Knoblock, Craig A},
  booktitle = {{Proceedings of the International Conference on Pattern
Recognition}},
  doi = {10.1109/ICPR.2006.368},
  isbn = {9780769525211},
  issn = {1051-4651},
  month = {August},
  pages = {1034--1037},
  publisher = {IEEE},
  title = {{{{Classification of Line and Character Pixels on Raster Maps
Using Discrete Cosine Transformation Coefficients and Support
Vector Machine}}}},
  url = {http://dx.doi.org/10.1109/ICPR.2006.368},
  url-file = {papers/Chiang-and-Knoblock-2006-Classification-of-Line-and-Character-Pixels-o...-iscrete-Cosine-Transformation-Coefficients-and-Support-Vector-Machine.pdf},
  volume = {2},
  year = {2006}
}

@inproceedings{Chiang2008-fo,
  author = {Chiang, Yao-Yi and Knoblock, Craig A},
  booktitle = {{{Proceedings of the 16th ACM SIGSPATIAL International Conference
on Advances in Geographic Information Systems}}},
  doi = {10.1145/1463434.1463463},
  isbn = {9781605583235},
  month = {November},
  pages = {1--10},
  publisher = {ACM Press},
  title = {{{{Automatic extraction of road intersection position,
connectivity, and orientations from raster maps}}}},
  url = {http://doi.acm.org/10.1145/1463434.1463463},
  url-file = {papers/Chiang-and-Knoblock-2008-Automatic-extraction-of-road-intersection-position,-connectivity,-and-orientations-from-raster-maps.pdf},
  year = {2008}
}

@inproceedings{Chiang2009-dt,
  address = {New York, NY, USA},
  author = {Chiang, Yao-Yi and Knoblock, Craig A},
  booktitle = {{{Proceedings of the 17th ACM SIGSPATIAL International Conference
on Advances in Geographic Information Systems}}},
  doi = {10.1145/1653771.1653793},
  isbn = {9781605586496},
  location = {Seattle, Washington},
  month = {November},
  pages = {138--147},
  publisher = {ACM},
  series = {GIS '09},
  title = {{{Classification of Raster Maps for Automatic Feature Extraction}}},
  url = {https://dl.acm.org/citation.cfm?doid=1653771.1653793},
  url-file = {papers/Chiang-and-Knoblock-2009-Classification-of-Raster-Maps-for-Automatic-Feature-Extraction.pdf},
  year = {2009}
}

@inproceedings{Chiang2009-fh,
  author = {Chiang, Y Y and Knoblock, C A},
  booktitle = {{Proceedings of the 2009 10th International Conference on
Document Analysis and Recognition}},
  doi = {10.1109/ICDAR.2009.274},
  issn = {1520-5363},
  month = {July},
  pages = {838--842},
  title = {{{{A Method for Automatically Extracting Road Layers from Raster
Maps}}}},
  url = {http://dx.doi.org/10.1109/ICDAR.2009.274},
  url-file = {papers/Chiang-and-Knoblock-2009-A-Method-for-Automatically-Extracting-Road-Layers-from-Raster-Maps.pdf},
  year = {2009}
}

@article{Chiang2009-xi,
  author = {Chiang, Yao-Yi and Knoblock., Craig A and Shahabi, Cyrus and
Chen, Ching-Chien},
  doi = {10.1007/s10707-008-0046-3},
  issn = {1384-6175, 1573-7624},
  journal = {GeoInformatica},
  language = {en},
  month = {June},
  number = {2},
  pages = {121--157},
  publisher = {Springer US},
  title = {{{{Automatic and Accurate Extraction of Road Intersections from
Raster Maps}}}},
  url = {https://doi.org/10.1007/s10707-008-0046-3},
  url-file = {papers/Chiang-et-al.-2009-Automatic-and-Accurate-Extraction-of-Road-Intersections-from-Raster-Maps.pdf},
  volume = {13},
  year = {2009}
}

@inproceedings{Chiang2010-co,
  author = {Chiang, Yao-Yi and Knoblock, Craig A},
  booktitle = {{{Proceedings of the 20th International Conference on Pattern
Recognition}}},
  pages = {3199--3202},
  title = {{{{An Approach for Recognizing Text Labels in Raster Maps}}}},
  url-file = {papers/Chiang-and-Knoblock-2010-An-Approach-for-Recognizing-Text-Labels-in-Raster-Maps.pdf},
  year = {2010}
}

@inproceedings{Chiang2010-go,
  address = {New York, New York, USA},
  author = {Chiang, Yao-Yi and Knoblock, Craig A},
  booktitle = {{{Proceedings of the 18th SIGSPATIAL International Conference on
Advances in Geographic Information Systems}}},
  doi = {10.1145/1869790.1869889},
  isbn = {9781450304283},
  month = {November},
  pages = {544--545},
  publisher = {\textbackslash ACM Request Permissions},
  title = {{{Strabo: a System for Extracting Road Vector Data From Raster
Maps}}},
  url = {http://portal.acm.org/citation.cfm?doid=1869790.1869889},
  url-file = {papers/Chiang-and-Knoblock-2010-Strabo-a-System-for-Extracting-Road-Vector-Data-From-Raster-Maps.pdf},
  year = {2010}
}

@incollection{Chiang2010-rk,
  author = {Chiang, Yao-Yi and Knoblock, Craig A},
  booktitle = {{Graphics Recognition. Achievements, Challenges, and Evolution.
GREC 2009. Lecture Notes in Computer Science}},
  doi = {10.1007/978-3-642-13728-0\_9},
  editor = {Ogier, J M and ., Liu W and J., Llad{\'o}s},
  pages = {93--105},
  publisher = {Springer Berlin Heidelberg},
  title = {{Extracting Road Vector Data from Raster Maps}},
  url = {http://dx.doi.org/10.1007/978-3-642-13728-0_9},
  url-file = {papers/Chiang-and-Knoblock-2010-Extracting-Road-Vector-Data-from-Raster-Maps.pdf},
  volume = {6020},
  year = {2010}
}

@incollection{Chiang2012-dp,
  address = {Berlin, Heidelberg},
  author = {Chiang, Yao-Yi and Knoblock, Craig A},
  booktitle = {{Geographic Information Science}},
  doi = {10.1007/978-3-642-33024-7\_5},
  editor = {Xiao, Ningchuan and Kwan, Mei-Po and Goodchild, Michael F and
Shekhar, Shashi},
  isbn = {9783642330230, 9783642330247},
  issn = {0302-9743, 1611-3349},
  language = {English},
  pages = {57--71},
  publisher = {Springer Berlin Heidelberg},
  series = {Lecture Notes in Computer Science},
  title = {{Generating Named Road Vector Data from Raster Maps}},
  url = {http://dx.doi.org/10.1007/978-3-642-33024-7_5},
  url-file = {papers/Chiang-and-Knoblock-2012-Chiang-2012-Generating-Named-Road-Vector-Data.pdf},
  volume = {7478},
  year = {2012}
}

@incollection{Chiang2013-fw,
  address = {Berlin, Heidelberg},
  author = {Chiang, Yao-Yi and Leyk, Stefan and Knoblock, Craig A},
  booktitle = {{{Graphics Recognition. New Trends and Challenges: 9th
International Workshop, GREC 2011, Seoul, Korea, September
15-16, 2011, Revised Selected Papers}}},
  doi = {10.1007/978-3-642-36824-0\_3},
  editor = {Kwon, Young-Bin and Ogier, Jean-Marc},
  isbn = {9783642368233, 9783642368240},
  issn = {0302-9743, 1611-3349},
  location = {Seoul, Korea},
  pages = {25--35},
  publisher = {Springer Berlin Heidelberg},
  series = {Lecture Notes in Computer Science, GREC'11},
  title = {{Efficient and Robust Graphics Recognition from Historical Maps:
9th International Workshop, GREC 2011, Seoul, Korea, September
15-16, 2011, Revised Selected Papers}},
  url = {http://dx.doi.org/10.1007/978-3-642-36824-0_3},
  url-file = {papers/Chiang-et-al.-2013-Efficient-and-Robust-Graphics-Recognition-from-Hist...-EC-2011,-Seoul,-Korea,-September-15-16,-2011,-Revised-Selected-Papers.pdf},
  volume = {7423},
  year = {2013}
}

@article{Chiang2013-gv,
  author = {Chiang, Yao-Yi and Knoblock, Craig A},
  doi = {10.1007/s10032-011-0177-1},
  issn = {1433-2833, 1433-2825},
  journal = {International Journal on Document Analysis and Recognition
(IJDAR)},
  language = {English},
  month = {March},
  number = {1},
  pages = {55--81},
  publisher = {Springer, Berlin / Heidelberg},
  title = {{{A General Approach for Extracting Road Vector Data from Raster
Maps}}},
  url = {http://link.springer.com/10.1007/s10032-011-0177-1},
  url-file = {papers/Chiang-and-Knoblock-2013-A-General-Approach-for-Extracting-Road-Vector-Data-from-Raster-Maps.pdf},
  volume = {16},
  year = {2013}
}

@inproceedings{Chiang2013-zo,
  author = {Chiang, Yao-Yi},
  booktitle = {{{Proceedings of the 26th International Cartographic Conference
(ICC'13)}}},
  isbn = {9781907075063},
  pages = {838--838},
  title = {{{{Strabo: A Complete System for Label Recognition in Maps}}}},
  year = {2013}
}

@article{Chiang2014-cu,
  author = {Chiang, Yao-Yi and Knoblock, Craig A},
  doi = {10.1007/s10707-014-0203-9},
  issn = {1384-6175, 1573-7624},
  journal = {GeoInformatica},
  language = {en},
  month = {February},
  number = {1},
  pages = {1--27},
  publisher = {Springer US},
  title = {{{Recognizing Text in Raster Maps}}},
  url = {http://dx.doi.org/10.1007/s10707-014-0203-9},
  url-file = {papers/Chiang-and-Knoblock-2014-Recognizing-Text-in-Raster-Maps.pdf},
  volume = {19},
  year = {2014}
}

@article{Chiang2014-gr,
  address = {New York, NY, USA},
  author = {Chiang, Yao-Yi and Leyk, Stefan and Knoblock, Craig A},
  doi = {10.1145/2557423},
  issn = {0360-0300},
  journal = {ACM Comput. Surv.},
  month = {May},
  number = {1},
  pages = {1--44},
  publisher = {Association for Computing Machinery},
  title = {{A Survey of Digital Map Processing Techniques}},
  url = {https://doi.org/10.1145/2557423},
  url-file = {papers/Chiang-et-al.-2014-A-Survey-of-Digital-Map-Processing-Techniques.pdf},
  volume = {47},
  year = {2014}
}

@inproceedings{Chiang2014-hg,
  author = {Chiang, Yao-Yi and Chioh, Phokgoan and Moghaddam, Sima},
  booktitle = {{{Proceedings of the 8th International Conference on Geographic
Information Science}}},
  pages = {264--269},
  title = {{{{\textbf{A Training-by-Example Approach for Symbol Spotting
from Raster Maps }}}}},
  url = {http://www.giscience.org/download/proceedings/GIScience2014EA.pdf},
  url-file = {papers/Chiang-et-al.-2014-A-Training-by-Example-Approach-for-Symbol-Spotting-from-Raster-Maps.pdf},
  year = {2014}
}

@inproceedings{Chiang2014-ij,
  address = {New York, NY, USA},
  author = {Chiang, Yao-Yi and Wu, Bo and Anand, Akshay and Akade, Ketan and
Knoblock, Craig A},
  booktitle = {{Proceedings of the 22nd ACM SIGSPATIAL International Conference
on Advances in Geographic Information Systems}},
  doi = {10.1145/2666310.2666373},
  isbn = {9781450331319},
  location = {Dallas, Texas},
  month = {November},
  pages = {577--580},
  publisher = {ACM},
  series = {SIGSPATIAL '14},
  title = {{A system for efficient cleaning and transformation of geospatial
data attributes}},
  url = {https://dl.acm.org/citation.cfm?doid=2666310.2666373},
  url-file = {papers/Chiang-et-al.-2014-A-system-for-efficient-cleaning-and-transformation-of-geospatial-data-attributes.pdf},
  year = {2014}
}

@inproceedings{Chiang2014-kx,
  address = {New York, New York, USA},
  author = {Chiang, Yao-Yi and Moghaddam, Sima and Gupta, Sanjauli and
Fernandes, Renuka and Knoblock, Craig A},
  booktitle = {{{Proceedings of the 22nd ACM SIGSPATIAL International Conference
on Advances in Geographic Information Systems}}},
  doi = {10.1145/2666310.2666374},
  isbn = {9781450331319},
  month = {November},
  pages = {581--584},
  publisher = {ACM},
  title = {{{From Map Images to Geographic Names}}},
  url = {http://dl.acm.org/citation.cfm?doid=2666310.2666374},
  url-file = {papers/Chiang-et-al.-2014-From-Map-Images-to-Geographic-Names.pdf},
  year = {2014}
}

@inproceedings{Chiang2015-bm,
  author = {Chiang, Yao-Yi and Leyk, Stefan},
  booktitle = {{{Proceedings of the 27th International Cartographic Conference}}},
  isbn = {9788588783119},
  title = {{{Exploiting Online Gazetteer for Fully Automatic Extraction of
Cartographic Symbols}}},
  year = {2015}
}

@inproceedings{Chiang2015-tr,
  address = {New York, NY, USA},
  author = {Chiang, Yao-Yi},
  booktitle = {{{Proceedings of the 23rd SIGSPATIAL International Conference on
Advances in Geographic Information Systems}}},
  doi = {10.1145/2820783.2820887},
  isbn = {9781450339674},
  location = {Bellevue, Washington},
  month = {November},
  pages = {16:1--16:4},
  publisher = {ACM},
  series = {GIS '15},
  title = {{{Querying Historical Maps As a Unified, Structured, and Linked
Spatiotemporal Source: Vision Paper}}},
  url = {http://doi.acm.org/10.1145/2820783.2820887},
  url-file = {papers/Chiang-2015-Querying-Historical-Maps-As-a-Unified,-Structured,-and-Linked-Spatiotemporal-Source-Vision-Paper.pdf},
  year = {2015}
}

@inproceedings{Chiang2015-xq,
  author = {Chiang, Yao-Yi and Gehring, Sarah},
  booktitle = {{{Proceedings of the 27th International Cartographic Conference}}},
  isbn = {9788588783119},
  title = {{{Semi-Automated Visualization of Spatial Context in Unstructured
Text}}},
  year = {2015}
}

@misc{Chiang2016-hc,
  author = {Chiang, Y Y},
  howpublished = {2016 Conference on Complex Systems, Complex Systems Society},
  location = {Amsterdam, Netherlands},
  title = {{Exploiting Context in Cartographic Evolutionary Documents to
Extract and Build Linked Spatial-Temporal Datasets}},
  year = {2016}
}

@article{Chiang2016-zr,
  author = {Chiang, Yao-Yi and Leyk, Stefan and Nazari, Narges Honarvar and
Moghaddam, Sima and Tan, Tian Xiang},
  doi = {10.1016/j.cageo.2016.04.013},
  issn = {0098-3004},
  journal = {Computers \& geosciences},
  month = {August},
  pages = {21--35},
  title = {{{Assessing the impact of graphical quality on automatic text
recognition in digital maps}}},
  url = {http://dx.doi.org/10.1016/j.cageo.2016.04.013},
  url-file = {papers/Chiang-et-al.-2016-Assessing-the-impact-of-graphical-quality-on-automatic-text-recognition-in-digital-maps.pdf},
  volume = {93},
  year = {2016}
}

@inproceedings{Chiang2017-qn,
  author = {Chiang, Y Y and Jain, A and Bandyopadhyay, B and Knoblock, A C},
  booktitle = {{Proceedings of the Symposium on Solid and Physical Modeling
(SPM)}},
  title = {{Automatic Learning of User Design Rationales from Examples}},
  url-file = {papers/Chiang-et-al.-2017-Automatic-Learning-of-User-Design-Rationales-from-Examples.pdf},
  year = {2017}
}

@inproceedings{Chiang2017-vb,
  author = {Chiang, Yao-Yi},
  booktitle = {{Proceedings of the 28th International Cartographic Conference}},
  title = {{{LINKING HISTORICAL MAPS TO USC SHOAH FOUNDATION VISUAL HISTORY
ARCHIVE }}},
  year = {2017}
}

@incollection{Chiang2017-ym,
  author = {Chiang, Yao-Yi},
  booktitle = {{Recent Trends in Image Processing and Pattern Recognition}},
  editor = {Santosh, K C and Hangarge, Mallikarjun and Bevilacqua,
Vitoantonio and Negi, Atul},
  isbn = {9789811048593},
  pages = {111--124},
  publisher = {Springer Singapore},
  title = {{Unlocking Textual Content from Historical Maps - Potentials and
Applications, Trends, and Outlooks}},
  url-file = {papers/Chiang-2017-Unlocking-Textual-Content-from-Historical-Maps-Potentials-and-Applications,-Trends,-and-Outlooks.pdf},
  volume = {709},
  year = {2017}
}

@misc{Chiang2018-ls,
  author = {Chiang, Y Y and Feldman, D},
  howpublished = {The 1st workshop of the NSF project: SI2-S2I2
Conceptualization: Geospatial Software Institute (GSI)},
  title = {{Next Generation Framework for Imagery Recognition and
Analysis}},
  url-file = {papers/Chiang-and-Feldman-2018-Next-Generation-Framework-for-Imagery-Recognition-and-Analysis.pdf},
  year = {2018}
}

@inproceedings{Chiang2019-yx,
  author = {Chiang, Y Y and Lin, Y and Franklin, M and Eckel, S P and
Ambite, J L and Ku, W},
  booktitle = {{Proceedings of the 2019 IEEE First International Conference on
Cognitive Machine Intelligence (CogMI)}},
  doi = {10.1109/CogMI48466.2019.00037},
  month = {December},
  pages = {202--209},
  title = {{Building Explainable Predictive Analytics for Location-Dependent
Time-Series Data}},
  url = {http://dx.doi.org/10.1109/CogMI48466.2019.00037},
  url-file = {papers/Chiang-et-al.-2019-Building-Explainable-Predictive-Analytics-for-Location-Dependent-Time-Series-Data.pdf},
  year = {2019}
}

@book{Chiang2020-vm,
  author = {Chiang, Yao-Yi and Duan, Weiwei and Leyk, Stefan and Uhl,
Johannes H and Knoblock, Craig A},
  doi = {10.1007/978-3-319-66908-3},
  isbn = {9783319669076},
  publisher = {Springer International Publishing},
  series = {SpringerBriefs in Geography},
  title = {{Using Historical Maps in Scientific Studies: Applications,
Challenges, and Best Practices}},
  url = {https://link.springer.com/book/10.1007/978-3-319-66908-3},
  url-file = {papers/Chiang-et-al.-2020-Using-Historical-Maps-in-Scientific-Studies-Applications,-Challenges,-and-Best-Practices.pdf},
  year = {2020}
}

@incollection{Chiang2020-zz,
  author = {Chiang, Yao-Yi and Lin, Yijun},
  booktitle = {{The Geographic Information Science \& Technology Body of
Knowledge (4th Quarter 2020 Edition)}},
  doi = {10.22224/gistbok/2020.4.2},
  editor = {Wilson, John P},
  publisher = {The University Consortium for Geographic Information Science
(UCGIS)},
  title = {{Design, Development, Testing, and Deployment of GIS Applications}},
  url = {http://dx.doi.org/10.22224/gistbok/2020.4.2},
  url-file = {papers/Chiang-and-Lin-2020-Design,-Development,-Testing,-and-Deployment-of-GIS-Applications.pdf},
  year = {2020}
}

@incollection{chiang2023geoai,
  author = {Chiang, Yao-Yi and Chen, Muhao and Duan, Weiwei and Kim, Jina and Knoblock, Craig A and Leyk, Stefan and Li, Zekun and Lin, Yijun and Namgung, Min and Shbita, Basel and others},
  booktitle = {Handbook of Geospatial Artificial Intelligence},
  pages = {217--247},
  publisher = {CRC Press},
  title = {GeoAI for the Digitization of Historical Maps},
  year = {2023}
}

@misc{Chiang2025_geoanomaly_detection_towards_finding_needles_of,
  author = {Yao-Yi Chiang and Joon-Seok Kim and Cory Krause and Enrico Mattei and Khurram Shafique and Carola Wenk and Andreas Züfle},
  howpublished = {The SIGSPATIAL Special 15 (1)},
  title = {GeoAnomaly Detection: Towards finding Needles of Anomalous Behavior in a Haystack of Geospatial Data},
  url = {https://dl.acm.org/doi/abs/10.1145/3757932.3757935},
  year = {2025}
}

@article{dai2022ace,
  author = {Dai, Pengwen and Yao, Siyuan and Li, Zekun and Zhang, Sanyi and Cao, Xiaochun},
  journal = {IEEE Transactions on Image Processing},
  pages = {4076--4089},
  publisher = {IEEE},
  title = {ACE: Anchor-free corner evolution for real-time arbitrarily-oriented object detection},
  volume = {31},
  year = {2022}
}

@inproceedings{Desai2005-gf,
  author = {Desai, Sneha and Knoblock, Craig A and Chiang, Yao-Yi and Desai,
Kandarp and Chen, Ching-Chien},
  booktitle = {{{Proceedings of the 2nd International Workshop on Geographic
Information Retrieval}}},
  pages = {35--38},
  title = {{{{Automatically identifying and georeferencing street maps on
the web}}}},
  url-file = {papers/Desai-et-al.-2005-Automatically-identifying-and-georeferencing-street-maps-on-the-web.pdf},
  year = {2005}
}

@inproceedings{Duan2016-se,
  author = {Duan, Weiwei and Chiang, Yao-Yi},
  booktitle = {{{Proceedings of the 5th ACM SIGSPATIAL International Workshop on
Analytics for Big Geospatial Data}}},
  doi = {10.1145/3006386.3006388},
  isbn = {9781450345811},
  month = {October},
  pages = {7--13},
  publisher = {ACM},
  title = {{{Building knowledge graph from public data for predictive
analysis: a case study on predicting technology future in space
and time}}},
  url = {http://dl.acm.org/citation.cfm?doid=3006386.3006388},
  url-file = {papers/Duan-and-Chiang-2016-Building-knowledge-graph-from-public-data-for-pre...-ysis-a-case-study-on-predicting-technology-future-in-space-and-time.pdf},
  year = {2016}
}

@inproceedings{Duan2017-de,
  address = {New York, NY, USA},
  author = {Duan, Weiwei and Chiang, Yao-Yi and Knoblock, Craig A and Jain,
Vinil and Feldman, Dan and Uhl, Johannes H and Leyk, Stefan},
  booktitle = {{Proceedings of the 1st Workshop on Artificial Intelligence and
Deep Learning for Geographic Knowledge Discovery}},
  doi = {10.1145/3149808.3149816},
  isbn = {9781450354981},
  location = {Los Angeles, California},
  month = {November},
  pages = {45--54},
  publisher = {Association for Computing Machinery},
  series = {GeoAI '17},
  title = {{Automatic alignment of geographic features in contemporary
vector data and historical maps}},
  url = {https://doi.org/10.1145/3149808.3149816},
  url-file = {papers/Duan-et-al.-2017-Automatic-alignment-of-geographic-features-in-contemporary-vector-data-and-historical-maps.pdf},
  year = {2017}
}

@inproceedings{Duan2018-cx,
  author = {Duan, Weiwei and Chiang, Y and Knoblock, Crang A and Leyk,
Stefan and Uhl, J},
  booktitle = {{Proceedings of the AutoCarto}},
  pages = {59--63},
  title = {{Automatic generation of precisely delineated geographic features
from georeferenced historical maps using deep learning}},
  url-file = {papers/Duan-et-al.-2018-Automatic-generation-of-precisely-delineated-geographic-features-from-georeferenced-historical-maps-using-deep-learning.pdf},
  year = {2018}
}

@article{Duan2018-rv,
  address = {New York, NY, USA},
  author = {Duan, Weiwei and Chiang, Yao-Yi},
  doi = {10.1145/3178392.3178396},
  journal = {SIGSPATIAL Special},
  month = {January},
  number = {3},
  pages = {6--7},
  publisher = {Association for Computing Machinery},
  title = {{SRC: a fully automatic geographic feature recognition system}},
  url = {https://doi.org/10.1145/3178392.3178396},
  url-file = {papers/Duan-and-Chiang-2018-SRC-a-fully-automatic-geographic-feature-recognition-system.pdf},
  volume = {9},
  year = {2018}
}

@article{Duan2020-wl,
  author = {Duan, Weiwei and Chiang, Yao-Yi and Leyk, Stefan and Uhl,
Johannes H and Knoblock, Craig A},
  doi = {10.1080/13658816.2019.1698742},
  issn = {1365-8816},
  journal = {International journal of geographical information science: IJGIS},
  month = {April},
  number = {4},
  pages = {824--849},
  publisher = {Taylor \& Francis},
  title = {{Automatic alignment of contemporary vector data and
georeferenced historical maps using reinforcement learning}},
  url = {https://doi.org/10.1080/13658816.2019.1698742},
  url-file = {papers/Duan-et-al.-2020-Automatic-alignment-of-contemporary-vector-data-and-georeferenced-historical-maps-using-reinforcement-learning.pdf},
  volume = {34},
  year = {2020}
}

@inproceedings{Fernandes2015-fr,
  author = {Fernandes, Renuka and Chiang, Yao-Yi},
  booktitle = {{{Proceedings of the 27th International Cartographic Conference}}},
  isbn = {9788588783119},
  title = {{{{Creating an Intuitive and Effective User Interface for Map
Processing in a Geographic Information System}}}},
  year = {2015}
}

@inproceedings{Gao2019-fo,
  author = {Gao, Y and Duan, Z and Shi, W and Feng, J and Chiang, Y-Y},
  booktitle = {{Proceedings of the 2019 6th International Conference on
Behavioral, Economic and Socio-Cultural Computing (BESC)}},
  doi = {10.1109/BESC48373.2019.8963449},
  month = {October},
  pages = {1--6},
  title = {{Personalized Recommendation Method of POI Based on Deep Neural
Network}},
  url = {http://dx.doi.org/10.1109/BESC48373.2019.8963449},
  url-file = {papers/Gao-et-al.-2019-Personalized-Recommendation-Method-of-POI-Based-on-Deep-Neural-Network.pdf},
  year = {2019}
}

@inproceedings{Garijo2019-yw,
  address = {New York, NY, USA},
  author = {Garijo, Daniel and Khider, Deborah and Ratnakar, Varun and Gil,
Yolanda and Deelman, Ewa and da Silva, Rafael Ferreira and
Knoblock, Craig and Chiang, Yao-Yi and Pham, Minh and Pujara,
Jay and Vu, Binh and Feldman, Dan and Mayani, Rajiv and Cobourn,
Kelly and Duffy, Chris and Kemanian, Armen and Shu, Lele and
Kumar, Vipin and Khandelwal, Ankush and Tayal, Kshitij and
Peckham, Scott and Stoica, Maria and Dabrowski, Anna and
Hardesty-Lewis, Daniel and Pierce, Suzanne},
  booktitle = {{Proceedings of the 24th International Conference on Intelligent
User Interfaces: Companion}},
  doi = {10.1145/3308557.3308711},
  isbn = {9781450366731},
  location = {Marina del Ray, California},
  month = {March},
  pages = {111--112},
  publisher = {Association for Computing Machinery},
  series = {IUI '19},
  title = {{An intelligent interface for integrating climate, hydrology,
agriculture, and socioeconomic models}},
  url = {https://doi.org/10.1145/3308557.3308711},
  url-file = {papers/Garijo-et-al.-2019-An-intelligent-interface-for-integrating-climate,-hydrology,-agriculture,-and-socioeconomic-models.pdf},
  year = {2019}
}

@inproceedings{Gil2018-hg,
  author = {Gil, Yolanda and Cobourn, Kelly and Deelman, Ewa and Duffy,
Chris and da Silva, Rafael Ferreira and Kemanian, Armen and
Knoblock, Craig and Kumar, Vipin and Peckham, Scott and
Carvalho, Lucas and Chiang, Yao-Yi and Garijo, Daniel and
Khider, Deborah and Khandelwal, Ankush and Pahm, Minh and
Pujara, Jay and Ratnakar, Varun and Stoica, Maria and Vu, Binh},
  booktitle = {{Proceedings of the 9th International Congress on Environmental
Modelling and Software}},
  title = {{MINT: Model Integration Through Knowledge-Powered Data and
Process Composition}},
  url-file = {papers/Gil-et-al.-2018-MINT-Model-Integration-Through-Knowledge-Powered-Data-and-Process-Composition.pdf},
  year = {2018}
}

@article{Gil2021-va,
  address = {New York, NY, USA},
  author = {Gil, Yolanda and Garijo, Daniel and Khider, Deborah and
Knoblock, Craig A and Ratnakar, Varun and Osorio, Maximiliano
and Vargas, Hern{\'a}n and Pham, Minh and Pujara, Jay and
Shbita, Basel and Vu, Binh and Chiang, Yao-Yi and Feldman, Dan
and Lin, Yijun and Song, Hayley and Kumar, Vipin and Khandelwal,
Ankush and Steinbach, Michael and Tayal, Kshitij and Xu,
Shaoming and Pierce, Suzanne A and Pearson, Lissa and
Hardesty-Lewis, Daniel and Deelman, Ewa and Silva, Rafael
Ferreira Da and Mayani, Rajiv and Kemanian, Armen R and Shi,
Yuning and Leonard, Lorne and Peckham, Scott and Stoica, Maria
and Cobourn, Kelly and Zhang, Zeya and Duffy, Christopher and
Shu, Lele},
  doi = {10.1145/3453172},
  file = {yaoyichi.github.io/papers-all//Gil-et-al.-2021-Artificial-Intelligence-for-Modeling-Complex-Systems-Taming-the-Complexity-of-Expert-Models-to-Improve-Decision-Making.pdf},
  issn = {2160-6455},
  journal = {ACM Trans. Interact. Intell. Syst.},
  month = {July},
  number = {2},
  pages = {1--49},
  publisher = {Association for Computing Machinery},
  title = {{Artificial Intelligence for Modeling Complex Systems: Taming the
Complexity of Expert Models to Improve Decision Making}},
  url = {https://doi.org/10.1145/3453172},
  volume = {11},
  year = {2021}
}

@misc{Holmes-Wong2017-ti,
  author = {Holmes-Wong, D and Chiang, Y Y},
  howpublished = {Digital Library Federation (DLF) Forum},
  location = {Pittsburg, PA, USA},
  title = {{Unlocking Maps for Discovery and Other Purpose}},
  year = {2017}
}

@inproceedings{Honarvar_Nazari2016-wv,
  author = {Honarvar Nazari, Narges and Tan, Tian Xiang and Chiang, Yao-Yi},
  booktitle = {{Proceedings of the Electronic Imaging, Document Recognition and
Retrieval XXIII conference, Society for Imaging Science and
Technology}},
  pages = {1--8},
  series = {8},
  title = {{Integrating Text Recognition for Overlapping Text Detection in
Maps}},
  url-file = {papers/Honarvar-Nazari-et-al.-2016-Integrating-Text-Recognition-for-Overlapping-Text-Detection-in-Maps.pdf},
  year = {2016}
}

@article{https://doi.org/10.1111/tgis.12975,
  abstract = {Abstract Traffic prediction for scenic spots is an important topic in modeling an urban traffic system. Existing traffic prediction approaches typically use raw traffic data and road networks without considering the physical environment and human–environment interaction. This article presents a novel traffic prediction model that considers: (1) the topological structure of the city road network; (2) the popularity and accessibility of each scenic spot in the city; and (3) the traffic volumes of nearby scenic spots. The proposed model first learns a series of traffic dependency graphs by the Multi-graph Convolutional Network using multiple data sources describing historical traffic volumes, scenic spots popularity, land function, location, and accessibility. The graph nodes represent the scenic spots, and the links between them represent their traffic dependency, considering all traffic and geographic features. Then the proposed model uses the Gated Recurrent Unit (GRU) to capture the temporal dependency between multiple fused graphs for traffic volume prediction. The experiments show that the proposed model (M-GCNGRU) can effectively exploit and integrate geographic data with historical traffic data for traffic volume prediction, outperforming several classical and state-of-the-art methods.},
  author = {Gao, Yuan and Chiang, Yao-Yi and Zhang, Xiaoxi and Zhang, Min},
  doi = {https://doi.org/10.1111/tgis.12975},
  eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/tgis.12975},
  journal = {Transactions in GIS},
  number = {6},
  pages = {2415-2439},
  title = {Traffic volume prediction for scenic spots based on multi-source and heterogeneous data},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/tgis.12975},
  volume = {26},
  year = {2022}
}

@inproceedings{hu2022clustering,
  author = {Hu, Haoji and Lin, Haowen and Chiang, Yao-Yi},
  booktitle = {2022 IEEE International Conference on Big Data (Big Data)},
  organization = {IEEE},
  pages = {575--584},
  title = {Clustering Human Mobility with Multiple Spaces},
  year = {2022}
}

@inproceedings{Jaiswal2014-km,
  author = {Jaiswal, Ayush and Chiang, Yao-Yi and Knoblock, Craig A and Lan,
Liang},
  booktitle = {{{Proceedings of the 8th International Conference on Geographic
Information Science}}},
  pages = {315--219},
  title = {{{{Location Prediction With Sparse GPS Data}}}},
  year = {2014}
}

@misc{Jelinski2025_finescale_soil_mapping_in_alaska_with,
  author = {Nicolas A Jelinski and Yao-Yi Chiang and Timm Nawrocki and Matt Macander and Sue Ives and Grunwald Sabine and Colby Brungard and Theresa Chen and Yijun Lin},
  howpublished = {ACM SIGSPATIAL 2025},
  title = {Fine-Scale Soil Mapping in Alaska with Multimodal Machine Learning},
  url = {https://experts.umn.edu/en/publications/fine-scale-soil-mapping-in-alaska-with-multimodal-machine-learnin/},
  year = {2025}
}

@article{Karroum2020-ku,
  author = {Karroum, Khaoula and Lin, Yijun and Chiang, Yao-Yi and Ben Maissa,
Yann and El Haziti, Mohamed and Sokolov, Anton and Delbarre,
Herv{\'e}},
  doi = {10.1007/s12647-020-00371-8},
  issn = {0974-9853},
  journal = {MAPAN},
  month = {March},
  title = {{A Review of Air Quality Modeling}},
  url = {https://doi.org/10.1007/s12647-020-00371-8},
  url-file = {papers/Karroum-et-al.-2020-A-Review-of-Air-Quality-Modeling.pdf},
  year = {2020}
}

@misc{Khider2019-zm,
  author = {Khider, D and Gil, Y and Cobourn, K M and Deelman, E and
Duffy, C and Ferreira da Silva, R and Kemanian, A and
Knoblock, C and Kumar, V and Peckham, S D and Chiang, Y Y and
Feldman, D and Garijo, D and Hardesty Lewis, D and
Khandelwal, A and Mayani, R and Osorio, M and Pahm, M and
Pierce, S A and Pujara, J and Ratnakar, V and Shu, L and
Song, H J and Shbita, B and Stoica, M and Vu, B and Pearson,
L},
  howpublished = {AGU Fall Meeting Abstracts},
  month = {December},
  title = {{MINT: An intelligent interface for understanding the impacts
of climate change on hydrological, agricultural and economic
systems}},
  url = {https://ui.adsabs.harvard.edu/abs/2019AGUFMPA33C1108K},
  year = {2019}
}

@inproceedings{Knoblock2010-vk,
  author = {Knoblock, Craig A and Chen, Ching-Chien and Chiang, Yao-Yi and
Goel, Aman and Michelson, Matthew and Shahabi, Cyrus},
  booktitle = {{Document Recognition and Retrieval XVII}},
  conference = {Document Recognition and Retrieval XVII},
  doi = {10.1117/12.838967},
  month = {January},
  pages = {753402--753402--15},
  publisher = {International Society for Optics and Photonics},
  title = {{{A general approach to discovering, registering, and extracting
features from raster maps}}},
  url = {http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=776640},
  url-file = {papers/Knoblock-et-al.-2010-A-general-approach-to-discovering,-registering,-and-extracting-features-from-raster-maps.pdf},
  volume = {7534},
  year = {2010}
}

@inproceedings{Leyk2016-dy,
  author = {Leyk, Stefan and Chiang, Yao-Yi},
  booktitle = {{Proceedings of the 2016 AutoCarto}},
  editor = {Freundschuh, Scott M},
  pages = {100--110},
  publisher = {AutoCarto},
  title = {{{Information Extraction based on the Concept of Geographic
Context}}},
  url = {http://geo.gmu.edu/AutoCarto2016/Leyk_and_Chiang.pdf},
  url-file = {papers/Leyk-and-Chiang-2016-Information-Extraction-based-on-the-Concept-of-Geographic-Context.pdf},
  year = {2016}
}

@inproceedings{Leyk2017-zc,
  author = {Leyk, Stefan and Chiang, Yao-Yi},
  booktitle = {{Proceedings of the 28th International Cartographic Conference}},
  title = {{Implementing the Concept of Geographic Context for Efficient
Recognition from Large-Scale Topographic Map Series}},
  year = {2017}
}

@inproceedings{li-etal-2023-geolm,
  address = {Singapore},
  author = {Li, Zekun  and
Zhou, Wenxuan  and
Chiang, Yao-Yi  and
Chen, Muhao},
  booktitle = {Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing},
  doi = {10.18653/v1/2023.emnlp-main.317},
  editor = {Bouamor, Houda  and
Pino, Juan  and
Bali, Kalika},
  month = {December},
  pages = {5227--5240},
  publisher = {Association for Computational Linguistics},
  title = {{G}eo{LM}: Empowering Language Models for Geospatially Grounded Language Understanding},
  url = {https://aclanthology.org/2023.emnlp-main.317},
  year = {2023}
}

@article{Li2019-gr,
  author = {Li, Kenan and Habre, Rima and Deng, Huiyu and Urman, Robert and
Morrison, John and Gilliland, Frank D and Ambite, Jos{\'e} Luis
and Stripelis, Dimitris and Chiang, Yao-Yi and Lin, Yijun and
Bui, Alex At and King, Christine and Hosseini, Anahita and Van
Vliet, Eleanne and Sarrafzadeh, Majid and Eckel, Sandrah P},
  doi = {10.2196/11201},
  issn = {2291-5222},
  journal = {JMIR mHealth and uHealth},
  language = {en},
  month = {February},
  number = {2},
  pages = {e11201},
  pmc = {PMC6386646},
  pmid = {30730297},
  title = {{Applying Multivariate Segmentation Methods to Human Activity
Recognition From Wearable Sensors' Data}},
  url = {http://dx.doi.org/10.2196/11201},
  url-file = {papers/Li-et-al.-2019-Applying-Multivariate-Segmentation-Methods-to-Human-Activity-Recognition-From-Wearable-Sensors'-Data.pdf},
  volume = {7},
  year = {2019}
}

@article{Li2020-my,
  author = {Li, Z and Chiang, Y Y and Tavakkol, S and Shbita, B and Uhl, J H
and {others}},
  journal = {Proceedings of ACM Knowledge Discovery and Data Mining
Conference (KDD)},
  publisher = {dl.acm.org},
  title = {{An Automatic Approach for Generating Rich, Linked Geo-Metadata
from Historical Map Images}},
  url = {https://dl.acm.org/doi/abs/10.1145/3394486.3403381},
  url-file = {papers/Li-et-al.-2020-An-Automatic-Approach-for-Generating-Rich,-Linked-Geo-Metadata-from-Historical-Map-Images.pdf},
  year = {2020}
}

@inproceedings{li2021synthetic,
  author = {Li, Zekun and Guan, Runyu and Yu, Qianmu and Chiang, Yao-Yi and Knoblock, Craig A},
  booktitle = {Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery},
  pages = {17--26},
  title = {Synthetic map generation to provide unlimited training data for historical map text detection},
  year = {2021}
}

@article{li2022spabert,
  author = {Li, Zekun and Kim, Jina and Chiang, Yao-Yi and Chen, Muhao},
  journal = {arXiv preprint arXiv:2210.12213},
  title = {SpaBERT: A Pretrained Language Model from Geographic Data for Geo-Entity Representation},
  year = {2022}
}

@inproceedings{Lin2017-go,
  address = {New York, NY, USA},
  author = {Lin, Yijun and Chiang, Yao-Yi and Pan, Fan and Stripelis,
Dimitrios and Ambite, Jose Luis and Eckel, Sandrah P and Habre,
Rima},
  booktitle = {{Proceedings of the 25th ACM SIGSPATIAL International Conference
on Advances in Geographic Information Systems}},
  conference = {SIGSPATIAL'17: 25th ACM SIGSPATIAL International Conference on
Advances in Geographic Information Systems},
  doi = {10.1145/3139958.3140013},
  isbn = {9781450354905},
  language = {en},
  location = {Redondo Beach CA USA},
  month = {November},
  pages = {1--10},
  pmc = {PMC5841919},
  pmid = {29527599},
  publisher = {ACM},
  title = {{Mining Public Datasets for Modeling Intra-City PM2.5
Concentrations at a Fine Spatial Resolution}},
  url = {https://dl.acm.org/doi/10.1145/3139958.3140013},
  url-file = {papers/Lin-et-al.-2017-Mining-Public-Datasets-for-Modeling-Intra-City-PM2.5-Concentrations-at-a-Fine-Spatial-Resolution.pdf},
  volume = {8},
  year = {2017}
}

@inproceedings{Lin2018-ck,
  author = {Lin, Chun and Su, Hang and Knoblock, Craig A and Chiang, Yao-Yi
and Duan, Weiwei and Leyk, Stefan and Uhl, Johannes H},
  booktitle = {{Proceedings of the SemSci 2018: Enabling Open Semantic Science}},
  pages = {59--67},
  title = {{Building Linked Data from Historical Maps}},
  url = {ftp://ceur-ws.org/pub/publications/CEUR-WS/Vol-2184.zip},
  url-file = {papers/Lin-et-al.-2018-Building-Linked-Data-from-Historical-Maps.pdf},
  year = {2018}
}

@inproceedings{Lin2018-vk,
  address = {New York, NY, USA},
  author = {Lin, Haowen and Chiang, Yao-Yi},
  booktitle = {{Proceedings of the 7th ACM SIGSPATIAL International Workshop on
Analytics for Big Geospatial Data}},
  doi = {10.1145/3282834.3282842},
  isbn = {9781450360418},
  location = {Seattle, WA, USA},
  month = {November},
  pages = {20--27},
  publisher = {Association for Computing Machinery},
  series = {BigSpatial 2018},
  title = {{An Uncertainty Aware Method for Geographic Data Conflation}},
  url = {https://doi.org/10.1145/3282834.3282842},
  url-file = {papers/Lin-and-Chiang-2018-An-Uncertainty-Aware-Method-for-Geographic-Data-Conflation.pdf},
  year = {2018}
}

@article{Lin2018-wz,
  address = {New York, NY, USA},
  author = {Lin, Haowen and Chiang, Yao-Yi},
  doi = {10.1145/3178392.3178400},
  journal = {SIGSPATIAL Special},
  month = {January},
  number = {3},
  pages = {14--15},
  publisher = {Association for Computing Machinery},
  title = {{SRC: automatic extraction of phrase-level map labels from
historical maps}},
  url = {https://doi.org/10.1145/3178392.3178400},
  url-file = {papers/Lin-and-Chiang-2018-SRC-automatic-extraction-of-phrase-level-map-labels-from-historical-maps.pdf},
  volume = {9},
  year = {2018}
}

@inproceedings{Lin2018-xw,
  address = {New York, NY, USA},
  author = {Lin, Yijun and Mago, Nikhit and Gao, Yu and Li, Yaguang and
Chiang, Yao-Yi and Shahabi, Cyrus and Ambite, Jos{\'e} Luis},
  booktitle = {{Proceedings of the 26th ACM SIGSPATIAL International Conference
on Advances in Geographic Information Systems}},
  doi = {10.1145/3274895.3274907},
  isbn = {9781450358897},
  location = {Seattle, Washington},
  month = {November},
  pages = {359--368},
  publisher = {Association for Computing Machinery},
  series = {SIGSPATIAL '18},
  title = {{Exploiting spatiotemporal patterns for accurate air quality
forecasting using deep learning}},
  url = {https://doi.org/10.1145/3274895.3274907},
  url-file = {papers/Lin-et-al.-2018-Exploiting-spatiotemporal-patterns-for-accurate-air-quality-forecasting-using-deep-learning.pdf},
  year = {2018}
}

@inproceedings{Lin2020-kk,
  author = {Lin, Y and Chiang, Y-Y and Franklin, M and Eckel, P S and
Ambite, J L},
  booktitle = {{Proceedings of IEEE International Conference on Data Mining
(ICDM)}},
  title = {{Building Autocorrelation-Aware Representations for Fine-Scale
Spatiotemporal Prediction}},
  url-file = {papers/Lin-et-al.-2020-Building-Autocorrelation-Aware-Representations-for-Fine-Scale-Spatiotemporal-Prediction.pdf},
  year = {2020}
}

@inproceedings{lin2022semi,
  author = {Lin, Yijun and Chiang, Yao-Yi},
  booktitle = {2022 IEEE International Conference on Big Data (Big Data)},
  organization = {IEEE},
  pages = {1024--1033},
  title = {A Semi-Supervised Learning Approach for Abnormal Event Prediction on Large Network Operation Time-Series Data},
  year = {2022}
}

@inproceedings{luo2021chartocr,
  author = {Luo, Junyu and Li, Zekun and Wang, Jinpeng and Lin, Chin-Yew},
  booktitle = {Proceedings of the IEEE/CVF winter conference on applications of computer vision},
  pages = {1917--1925},
  title = {Chartocr: Data extraction from charts images via a deep hybrid framework},
  year = {2021}
}

@inproceedings{modad2022cell,
  author = {Modad, Bassel Abou Ali and Yu, Xin and Song, Hae Jin and Chiang, Yao-Yi and Molisch, Andreas F},
  booktitle = {GLOBECOM 2022-2022 IEEE Global Communications Conference},
  organization = {IEEE},
  pages = {4782--4787},
  title = {Cell-by-Cell Line-of-Sight Probability Models Based on Real-World Base Station Deployment},
  year = {2022}
}

@misc{Mokbel2025_proceedings_of_the_33rd_acm_international,
  author = {Mohamed Mokbel and Shashi Shekar and Andreas Züfle and Yao-Yi Chiang and Maria Luisa Damiani and Moustafa A Youssef},
  howpublished = {ACM},
  title = {Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems},
  url = {https://par.nsf.gov/biblio/10653550},
  year = {2025}
}

@inproceedings{namgung2022incorporating,
  author = {Namgung, Min and Chiang, Yao-Yi},
  booktitle = {Proceedings of the 5th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery},
  pages = {14--17},
  title = {Incorporating spatial context for post-OCR in map images},
  year = {2022}
}

@misc{Namgung2025_less_is_more_multimodal_region_representation,
  abstract = {With the increasing availability of geospatial datasets, researchers have explored region representation learning (RRL) to analyze complex region characteristics. Recent RRL methods use contrastive learning (CL) to capture shared information between two modalities but often overlook task-relevant unique information specific to each modality. Such modality-specific details can explain region characteristics that shared information alone cannot capture. Bringing information factorization to RRL can address this by factorizing multimodal data into shared and unique information. However, existing factorization approaches focus on two modalities, whereas RRL can benefit from various geospatial data. Extending factorization beyond two modalities is non-trivial because modeling high-order relationships introduces a combinatorial number of learning objectives, increasing model complexity. We introduce Cross modal Knowledge Injected Embedding, an information factorization approach for RRL that captures both shared and unique representations. CooKIE uses a pairwise inter-view learning approach that captures high-order information without modeling high-order dependency, avoiding exhaustive combinations. We evaluate CooKIE on three regression tasks and a land use classification task in New York City and Delhi, India. Results show that CooKIE outperforms existing RRL methods and a factorized RRL model, capturing multimodal information with fewer training parameters and floating-point operations per second (FLOPs). We release the code: https://github.com/MinNamgung/CooKIE.},
  author = {Min Namgung and Yijun Lin and JangHyeon Lee and Yao-Yi Chiang},
  howpublished = {arXiv},
  primaryclass = {cs.LG},
  title = {Less is More: Multimodal Region Representation via Pairwise Inter-view Learning},
  url = {https://arxiv.org/api/RnQIRUtWQ/Dj2XGXX8a3TcyOxS0},
  year = {2025}
}

@inproceedings{Nanetti2017-tf,
  author = {Nanetti, A and Cattaneo, A and Cheong, S A and Chiang, Y Y and
Lin, C Y},
  booktitle = {{Proceedings of the ICA Pre-Conference Workshop on Mapping Tools
for Non-Mapping Experts: Incorporating Geospatial Visualization
Tools in Libraries}},
  title = {{Visual Knowledge Aggregation: From Static to Dynamic Information
Systems in Library Contexts}},
  year = {2017}
}

@inproceedings{Narayanan2014-cg,
  author = {Narayanan, S and Jaiswal, A and Chiang, Y Y and Geng, Y and
Knoblock, C A and Szekely, P},
  booktitle = {{Proceedings of the 2014 IEEE International Conference on Data
Mining Workshop}},
  doi = {10.1109/ICDMW.2014.44},
  isbn = {9781479942749},
  issn = {2375-9232},
  language = {English},
  month = {December},
  pages = {1076--1085},
  publisher = {IEEE},
  title = {{{{Integration and Automation of Data Preparation and Data
Mining}}}},
  url = {http://dx.doi.org/10.1109/ICDMW.2014.44},
  url-file = {papers/Narayanan-et-al.-2014-Integration-and-Automation-of-Data-Preparation-and-Data-Mining.pdf},
  year = {2014}
}

@inproceedings{Ngo2015-yj,
  author = {Ngo, Vivian and Swift, Jennifer and Chiang, Yao-Yi},
  booktitle = {{{Proceedings of the 27th International Cartographic Conference}}},
  isbn = {9788588783119},
  title = {{{Visualizing Land Reclamation in Hong Kong: A Web Application}}},
  url-file = {papers/Ngo-et-al.-2015-Visualizing-Land-Reclamation-in-Hong-Kong-A-Web-Application.pdf},
  year = {2015}
}

@inproceedings{Nguyen2018-am,
  address = {New York, NY, USA},
  author = {Nguyen, Kien and Yang, Jingyun and Lin, Yijun and Lin, Jianfa
and Chiang, Yao-Yi and Shahabi, Cyrus},
  booktitle = {{Proceedings of the 26th ACM SIGSPATIAL International Conference
on Advances in Geographic Information Systems}},
  doi = {10.1145/3274895.3274911},
  isbn = {9781450358897},
  location = {Seattle, Washington},
  month = {November},
  pages = {560--563},
  publisher = {Association for Computing Machinery},
  series = {SIGSPATIAL '18},
  title = {{Los angeles metro bus data analysis using GPS trajectory and
schedule data (demo paper)}},
  url = {https://doi.org/10.1145/3274895.3274911},
  url-file = {papers/Nguyen-et-al.-2018-Los-angeles-metro-bus-data-analysis-using-GPS-trajectory-and-schedule-data-(demo-paper).pdf},
  year = {2018}
}

@misc{Nichols2025_key_research_priorities_in_methodological_approaches,
  author = {Emma Nichols and Kayleigh P Keller and Howard Chang and Yao‐Yi Chiang and Alden L Gross and Eleanor Hayes‐Larson and Birgit Claus Henn and Katrina L Kezios and Erik Meijer and Regina A Shih and Adam A Szpiro and Jordan Weiss and Jennifer Weuve and Sara D Adar and Jinkook Lee},
  howpublished = {Alzheimer's & Dementia 21 (11)},
  title = {Key research priorities in methodological approaches for measuring the exposome and studying its role in the development of dementia},
  url = {https://alz-journals.onlinelibrary.wiley.com/doi/abs/10.1002/alz.70928},
  year = {2025}
}

@misc{Nichols2025_modeling_approaches_for_estimating_the_effects,
  author = {Emma Nichols and Ava Bindas and Samer Atshan and Howard Chang and Yao‐Yi Chiang and Birgit Claus Henn and Eleanor Hayes‐Larson and Kayleigh P Keller and Katrina L Kezios and Regina A Shih and Adam A Szpiro and Jordan Weiss and Sara D Adar and David M Knapp and Jinkook Lee and Jennifer Weuve},
  howpublished = {Alzheimer's & Dementia 21 (12)},
  title = {Modeling approaches for estimating the effects of risk factors using longitudinal lifecourse exposure data in dementia research},
  url = {https://alz-journals.onlinelibrary.wiley.com/doi/abs/10.1002/alz.70971},
  year = {2025}
}

@inproceedings{Pal2006-bz,
  author = {Pal, U and Kimura, F and Roy, K and Pal, T},
  booktitle = {{Proceedings of the 18th International Conference on Pattern
Recognition (ICPR'06)}},
  doi = {10.1109/ICPR.2006.971},
  isbn = {9780769525211},
  issn = {1051-4651},
  month = {August},
  pages = {873--876},
  publisher = {IEEE Computer Society},
  title = {{{{Recognition of English Multi-oriented Characters}}}},
  url = {http://dx.doi.org/10.1109/ICPR.2006.971},
  url-file = {papers/Pal-et-al.-2006-Recognition-of-English-Multi-oriented-Characters.pdf},
  volume = {2},
  year = {2006}
}

@incollection{Park2016-ht,
  author = {Park, W and Chiang, Y-Y and Lee, S J and Yu, K},
  booktitle = {{Esri Map Book}},
  publisher = {Esri},
  title = {{Hot Spots of Tweets Related to Food, Entertainment, Work, and
Study in Gangnam Area of Seoul, Korea}},
  volume = {31: GIS -- Enabling a Smarter World. Redlands},
  year = {2016}
}

@inproceedings{Park21-icc,
  author = {Park, L. and Birdwell, A. and Chiang, Y.-Y. },
  booktitle = {{{Proceedings of the 30th International Cartographic Conference
(ICC'21)}}},
  title = {{Reimagining Measures of Spatial Access to Health Care in Low- And Middle-Income Countries: Using Road Network Analysis to Validate Self-Reported Perceptions of Geospatial Barriers}},
  year = {2021}
}

@misc{Pyo2025_frieda_benchmarking_multistep_cartographic_reasoning_in,
  abstract = {Cartographic reasoning is the skill of interpreting geographic relationships by aligning legends, map scales, compass directions, map texts, and geometries across one or more map images. Although essential as a concrete cognitive capability and for critical tasks such as disaster response and urban planning, it remains largely unevaluated. Building on progress in chart and infographic understanding, recent large vision language model studies on map visual question-answering often treat maps as a special case of charts. In contrast, map VQA demands comprehension of layered symbology (e.g., symbols, geometries, and text labels) as well as spatial relations tied to orientation and distance that often span multiple maps and are not captured by chart-style evaluations. To address this gap, we introduce FRIEDA, a benchmark for testing complex open-ended cartographic reasoning in LVLMs. FRIEDA sources real map images from documents and reports in various domains and geographical areas. Following classifications in Geographic Information System (GIS) literature, FRIEDA targets all three categories of spatial relations: topological (border, equal, intersect, within), metric (distance), and directional (orientation). All questions require multi-step inference, and many require cross-map grounding and reasoning. We evaluate eleven state-of-the-art LVLMs under two settings: (1) the direct setting, where we provide the maps relevant to the question, and (2) the contextual setting, where the model may have to identify the maps relevant to the question before reasoning. Even the strongest models, Gemini-2.5-Pro and GPT-5-Think, achieve only 38.20% and 37.20% accuracy, respectively, far below human performance of 84.87%. These results reveal a persistent gap in multi-step cartographic reasoning, positioning FRIEDA as a rigorous benchmark to drive progress on spatial intelligence in LVLMs.},
  author = {Jiyoon Pyo and Yuankun Jiao and Dongwon Jung and Zekun Li and Leeje Jang and Sofia Kirsanova and Jina Kim and Yijun Lin and Qin Liu and Junyi Xie and Hadi Askari and Nan Xu and Muhao Chen and Yao-Yi Chiang},
  howpublished = {arXiv},
  primaryclass = {cs.CV},
  title = {FRIEDA: Benchmarking Multi-Step Cartographic Reasoning in Vision-Language Models},
  url = {https://arxiv.org/api/cAn1zHHcKlclQ3NWAR92PR4QKLc},
  year = {2025}
}

@article{s21175801,
  abstract = {Many approaches to time series classification rely on machine learning methods. However, there is growing interest in going beyond black box prediction models to understand discriminatory features of the time series and their associations with outcomes. One promising method is time-series shapelets (TSS), which identifies maximally discriminative subsequences of time series. For example, in environmental health applications TSS could be used to identify short-term patterns in exposure time series (shapelets) associated with adverse health outcomes. Identification of candidate shapelets in TSS is computationally intensive. The original TSS algorithm used exhaustive search. Subsequent algorithms introduced efficiencies by trimming/aggregating the set of candidates or training candidates from initialized values, but these approaches have limitations. In this paper, we introduce Wavelet-TSS (W-TSS) a novel intelligent method for identifying candidate shapelets in TSS using wavelet transformation discovery. We tested W-TSS on two datasets: (1) a synthetic example used in previous TSS studies and (2) a panel study relating exposures from residential air pollution sensors to symptoms in participants with asthma. Compared to previous TSS algorithms, W-TSS was more computationally efficient, more accurate, and was able to discover more discriminative shapelets. W-TSS does not require pre-specification of shapelet length.},
  article-number = {5801},
  author = {Li, Kenan and Deng, Huiyu and Morrison, John and Habre, Rima and Franklin, Meredith and Chiang, Yao-Yi and Sward, Katherine and Gilliland, Frank D. and Ambite, José Luis and Eckel, Sandrah P.},
  doi = {10.3390/s21175801},
  issn = {1424-8220},
  journal = {Sensors},
  number = {17},
  pubmedid = {34502692},
  title = {W-TSS: A Wavelet-Based Algorithm for Discovering Time Series Shapelets},
  url = {https://www.mdpi.com/1424-8220/21/17/5801},
  volume = {21},
  year = {2021}
}

@inproceedings{Sathe2014-uo,
  address = {New York, NY, USA},
  author = {Sathe, Mihir and Knoblock, Craig A and Chiang, Yao-Yi and
Harris, Aaron},
  booktitle = {{Proceedings of the 22nd ACM SIGSPATIAL International Conference
on Advances in Geographic Information Systems}},
  doi = {10.1145/2666310.2666437},
  isbn = {9781450331319},
  location = {Dallas, Texas},
  month = {November},
  pages = {429--432},
  publisher = {ACM},
  series = {SIGSPATIAL '14},
  title = {{A parallel query engine for interactive spatiotemporal analysis}},
  url = {https://dl.acm.org/citation.cfm?doid=2666310.2666437},
  url-file = {papers/Sathe-et-al.-2014-A-parallel-query-engine-for-interactive-spatiotemporal-analysis.pdf},
  year = {2014}
}

@inproceedings{Shahabi2006-dp,
  author = {Shahabi, Cyrus and Chiang, Yao-Yi and Chung, Kelvin and Huang,
Kai-Chen and Khoshgozaran-Haghighi, Jeff and Craig A. Knoblocknd
Lee, Sung Chun and Neumann, Ulrich and Nevatia, Ram and Rihan,
Arjun and Thakkar, Snehal and You, Suya},
  booktitle = {{{Proceedings of the IEEE International Conference on Multimedia
\& Expo}}},
  pages = {93--96},
  publisher = {IEEE},
  title = {{{GEODEC: Eenableing Geospatial Decision Making}}},
  url-file = {papers/Shahabi-et-al.-2006-GEODEC-Eenableing-Geospatial-Decision-Making.pdf},
  year = {2006}
}

@inproceedings{Shbita2019-lv,
  author = {Shbita, Basel and Vu, Binh and Feldman, Dan and Pham, Minh and
Rajendran, A and Knoblock, C A and Pujara, J and Chiang, Y-Y},
  booktitle = {{Proceedings of the Workshop on Advanced Knowledge Technologies
for Science in a FAIR World (AKTS) 2019}},
  month = {September},
  title = {{Creating a FAIR Data Catalog to Support Scientific Modeling}},
  url = {https://www.researchgate.net/publication/339670920_Creating_a_FAIR_Data_Catalog_to_Support_Scientific_Modeling},
  url-file = {papers/Shbita-et-al.-2019-Creating-a-FAIR-Data-Catalog-to-Support-Scientific-Modeling.pdf},
  year = {2019}
}

@inproceedings{Shbita2020-tl,
  author = {Shbita, Basel and Knoblock, Craig A and Duan, Weiwei and Chiang,
Yao-Yi and Uhl, Johannes H and Leyk, Stefan},
  booktitle = {{The Semantic Web}},
  doi = {10.1007/978-3-030-49461-2\_24},
  pages = {409--426},
  publisher = {Springer International Publishing},
  title = {{Building Linked Spatio-Temporal Data from Vectorized Historical
Maps}},
  url = {http://dx.doi.org/10.1007/978-3-030-49461-2_24},
  url-file = {papers/Shbita-et-al.-2020-Building-Linked-Spatio-Temporal-Data-from-Vectorized-Historical-Maps.pdf},
  year = {2020}
}

@article{shbita2022building,
  author = {Shbita, Basel and Knoblock, Craig A and Duan, Weiwei and Chiang, Yao-Yi and Uhl, Johannes H and Leyk, Stefan},
  journal = {Semantic Web},
  number = {Preprint},
  pages = {1--23},
  publisher = {IOS Press},
  title = {Building Spatio-Temporal Knowledge Graphs from Vectorized Topographic Historical Maps},
  urllink = {https://content.iospress.com/articles/semantic-web/sw222918},
  year = {2022}
}

@inproceedings{Stripelis2017-ab,
  author = {Stripelis, D and Ambite, J L and Chiang, Y Y and Eckel, S P and
Habre, R},
  booktitle = {{Proceedings of the 2017 IEEE 33rd International Conference on
Data Engineering (ICDE)}},
  doi = {10.1109/ICDE.2017.198},
  pages = {1407--1408},
  title = {{{A Scalable Data Integration and Analysis Architecture for
Sensor Data of Pediatric Asthma}}},
  url = {http://dx.doi.org/10.1109/ICDE.2017.198},
  url-file = {papers/Stripelis-et-al.-2017-A-Scalable-Data-Integration-and-Analysis-Architecture-for-Sensor-Data-of-Pediatric-Asthma.pdf},
  year = {2017}
}

@inproceedings{Tavakkol2019-xs,
  address = {New York, NY, USA},
  author = {Tavakkol, Sasan and Chiang, Yao-Yi and Waters, Tim and Han, Feng
and Prasad, Kisalaya and Kiveris, Raimondas},
  booktitle = {{Proceedings of the 3rd ACM SIGSPATIAL International Workshop on
AI for Geographic Knowledge Discovery}},
  doi = {10.1145/3356471.3365236},
  isbn = {9781450369572},
  location = {Chicago, IL, USA},
  month = {November},
  pages = {48--51},
  publisher = {Association for Computing Machinery},
  series = {GeoAI 2019},
  title = {{Kartta Labs: Unrendering Historical Maps}},
  url = {https://doi.org/10.1145/3356471.3365236},
  url-file = {papers/Tavakkol-et-al.-2019-Kartta-Labs-Unrendering-Historical-Maps.pdf},
  year = {2019}
}

@misc{Uhl2017-im,
  author = {Uhl, Johannes H and Leyk, Stefan and Chiang, Yao-Yi and Duan,
Weiwei and Knoblock, Craig A},
  howpublished = {The International Land Use Symposium 2017 (Spatial data
modelling and visualisation to enlighten sustainable policy
making)},
  location = {Dresden, Germany},
  title = {{Machine-learning based Approaches for Extracting Settlement
Features from Historical Maps}},
  year = {2017}
}

@inproceedings{Uhl2017-ug,
  author = {Uhl, Johannes H and Leyk, Stefan and Chiang, Yao-Yi and Duan,
Weiwei and Knoblock, Craig A},
  booktitle = {{Proceedings of the 8th ​​International Conference of Pattern
Recognition Systems (ICPRS 2017)}},
  doi = {10.1049/cp.2017.0144},
  month = {January},
  pages = {15 (6 .)--15 (6 .)},
  publisher = {IET Digital Library},
  title = {{Extracting human settlement footprint from historical
topographic map series using context-based machine learning}},
  url = {https://digital-library.theiet.org/content/conferences/10.1049/cp.2017.0144},
  url-file = {papers/Uhl-et-al.-2017-Extracting-human-settlement-footprint-from-historical-topographic-map-series-using-context-based-machine-learning.pdf},
  year = {2017}
}

@inproceedings{Uhl2018-cf,
  author = {Uhl, Johannes H and Leyk, Stefan and Chiang, Yao-Yi and Duan,
Weiwei and Knoblock, Craig A},
  booktitle = {{Proceedings of the UCGIS/AutoCarto}},
  pages = {123 -- 124},
  title = {{Exploring the Potential of Deep Learning for Settlement Symbol
Extraction from Historical Map Documents}},
  url-file = {papers/Uhl-et-al.-2018-Exploring-the-Potential-of-Deep-Learning-for-Settlement-Symbol-Extraction-from-Historical-Map-Documents.pdf},
  year = {2018}
}

@article{Uhl2018-ph,
  author = {Uhl, Johannes H and Leyk, Stefan and Chiang, Yao-Yi and Duan,
Weiwei and Knoblock, Craig A},
  doi = {10.1049/iet-ipr.2018.5484},
  issn = {1751-9667, 1751-9667},
  journal = {IET Image Processing},
  month = {July},
  number = {11},
  pages = {2084--2091},
  publisher = {IET Digital Library},
  title = {{Spatialising uncertainty in image segmentation using weakly
supervised convolutional neural networks: a case study from
historical map processing}},
  url = {https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5484},
  url-file = {papers/Uhl-et-al.-2018-Spatialising-uncertainty-in-image-segmentation-using-w...-utional-neural-networks-a-case-study-from-historical-map-processing.pdf},
  volume = {12},
  year = {2018}
}

@article{Uhl2018-tj,
  author = {Uhl, Johannes H and Leyk, Stefan and Chiang, Yao-Yi and Duan,
Weiwei and Knoblock, Craig A},
  doi = {10.3390/ijgi7040148},
  issn = {2220-9964},
  journal = {ISPRS international journal of geo-information},
  language = {en},
  month = {April},
  number = {4},
  pages = {148},
  pmc = {PMC6500493},
  pmid = {31061817},
  publisher = {Multidisciplinary Digital Publishing Institute},
  title = {{Map archive mining: visual-analytical approaches to explore
large historical map collections}},
  url = {http://dx.doi.org/10.3390/ijgi7040148},
  url-file = {papers/Uhl-et-al.-2018-Map-archive-mining-visual-analytical-approaches-to-explore-large-historical-map-collections.pdf},
  volume = {7},
  year = {2018}
}

@article{Uhl2021-dj,
  author = {Uhl, Johannes H and Leyk, Stefan and Li, Zekun and Duan, Weiwei
and Shbita, Basel and Chiang, Yao-Yi and Knoblock, Craig A},
  doi = {10.3390/rs13183672},
  file = {yaoyichi.github.io/papers-all//Uhl-et-al.-2021-Combining-Remote-Sensing-Derived-Data-and-Historical-Maps-for-Long-Term-Back-Casting-of-Urban-Extents.pdf},
  journal = {Remote Sensing},
  language = {en},
  month = {September},
  number = {18},
  pages = {3672},
  publisher = {Multidisciplinary Digital Publishing Institute},
  title = {{Combining Remote-Sensing-Derived Data and Historical Maps for
Long-Term Back-Casting of Urban Extents}},
  url = {https://www.mdpi.com/2072-4292/13/18/3672},
  volume = {13},
  year = {2021}
}

@article{VoPham2018-zv,
  author = {VoPham, Trang and Hart, Jaime E and Laden, Francine and Chiang,
Yao-Yi},
  doi = {10.1186/s12940-018-0386-x},
  issn = {1476-069X},
  journal = {Environmental health: a global access science source},
  language = {en},
  month = {April},
  number = {1},
  pages = {40},
  pmc = {PMC5905121},
  pmid = {29665858},
  title = {{Emerging trends in geospatial artificial intelligence (geoAI):
potential applications for environmental epidemiology}},
  url = {http://dx.doi.org/10.1186/s12940-018-0386-x},
  url-file = {papers/VoPham-et-al.-2018-Emerging-trends-in-geospatial-artificial-intelligence-(geoAI)-potential-applications-for-environmental-epidemiology.pdf},
  volume = {17},
  year = {2018}
}

@article{Wu2016-io,
  address = {New York, NY, USA},
  author = {Wu, Wensheng and Meng, Weiyi and Su, Weifeng and Zhou, Guangyou
and Chiang, Yao-Yi},
  doi = {10.1145/2873061},
  issn = {1559-1131},
  journal = {ACM Trans. Web},
  month = {April},
  number = {2},
  pages = {1--29},
  publisher = {Association for Computing Machinery},
  title = {{Q2P: Discovering Query Templates via Autocompletion}},
  url = {https://doi.org/10.1145/2873061},
  url-file = {papers/Wu-et-al.-2016-Q2P-Discovering-Query-Templates-via-Autocompletion.pdf},
  volume = {10},
  year = {2016}
}

@article{Wu2018-nr,
  author = {Wu, J and Wan, Y and Chiang, Y Y and Fu, Z and Deng, M},
  doi = {10.1109/ACCESS.2018.2793302},
  issn = {2169-3536},
  journal = {IEEE Access},
  pages = {4904--4915},
  title = {{A Matching Algorithm Based on Voronoi Diagram for Multi-Scale
Polygonal Residential Areas}},
  url = {http://dx.doi.org/10.1109/ACCESS.2018.2793302},
  url-file = {papers/Wu-et-al.-2018-A-Matching-Algorithm-Based-on-Voronoi-Diagram-for-Multi-Scale-Polygonal-Residential-Areas.pdf},
  volume = {6},
  year = {2018}
}

@article{Wu2019-qe,
  author = {Wu, J and Wei, P and Yuan, X and Shu, Z and Chiang, Y Y and Fu, Z
and Deng, M},
  doi = {10.1109/ACCESS.2019.2907114},
  issn = {2169-3536},
  journal = {IEEE Access},
  pages = {40649--40662},
  title = {{A New Gabor Filter-Based Method for Automatic Recognition of
Hatched Residential Areas}},
  url = {http://dx.doi.org/10.1109/ACCESS.2019.2907114},
  url-file = {papers/Wu-et-al.-2019-A-New-Gabor-Filter-Based-Method-for-Automatic-Recognition-of-Hatched-Residential-Areas.pdf},
  volume = {7},
  year = {2019}
}

@misc{Xiang2025_walkclip_multimodal_learning_for_urban_walkability,
  abstract = {Urban walkability is a cornerstone of public health, sustainability, and quality of life. Traditional walkability assessments rely on surveys and field audits, which are costly and difficult to scale. Recent studies have used satellite imagery, street view imagery, or population indicators to estimate walkability, but these single-source approaches capture only one dimension of the walking environment. Satellite data describe the built environment from above, but overlook the pedestrian perspective. Street view imagery captures conditions at the ground level, but lacks broader spatial context. Population dynamics reveal patterns of human activity but not the visual form of the environment. We introduce WalkCLIP, a multimodal framework that integrates these complementary viewpoints to predict urban walkability. WalkCLIP learns walkability-aware vision-language representations from GPT-4o generated image captions, refines these representations with a spatial aggregation module that incorporates neighborhood context, and fuses the resulting features with representations from a population dynamics foundation model. Evaluated at 4,660 locations throughout Minneapolis-Saint Paul, WalkCLIP outperforms unimodal and multimodal baselines in both predictive accuracy and spatial alignment. These results show that the integration of visual and behavioral signals yields reliable predictions of the walking environment.},
  author = {Shilong Xiang and JangHyeon Lee and Min Namgung and Yao-Yi Chiang},
  howpublished = {arXiv},
  primaryclass = {cs.CV},
  title = {WalkCLIP: Multimodal Learning for Urban Walkability Prediction},
  url = {https://arxiv.org/api/4piXGGnxE2zcdD3UZMJB+nH1NA8},
  year = {2025}
}

@article{xu2023best,
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  journal = {IEEE Transactions on Information Forensics and Security},
  publisher = {IEEE},
  title = {The Best Protection Is Attack: Fooling Scene Text Recognition with Minimal Pixels},
  year = {2023}
}

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Time Periods}}},
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Scalable Data Management and Analysis System Integrating Public
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Shahabi, C},
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Data (Big Data)}},
  doi = {10.1109/BigData47090.2019.9006561},
  month = {December},
  pages = {988--997},
  title = {{DETECT: Deep Trajectory Clustering for Mobility-Behavior
Analysis}},
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@inproceedings{Yue2021-aa,
  author = {Yue, Mingxuan and Chiang, Yao-Yi and Shahabi, Cyrus},
  booktitle = {{Machine Learning and Knowledge Discovery in Databases. Applied
Data Science Track}},
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}

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Craig A},
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for Intelligent Environments and Workshop on Semantic Cities}}},
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  pages = {31--37},
  publisher = {ACM},
  series = {AIIP '13},
  title = {{A semantic approach to retrieving, linking, and integrating
heterogeneous geospatial data}},
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}

@inproceedings{Zhang2016-ra,
  author = {Zhang, Ying and Chiang, Yao-Yi and Knoblock, Craig and Li,
Chaopeng and Du, Liming and Liu, Shaowen and Singh, Sanjay},
  booktitle = {{{Proceedings of the 19th AGILE International Conference on
Geographic Information Science}}},
  title = {{{An Automatic Approach for Building Place-Name Datasets from the
Web}}},
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}

@inproceedings{Zhang2019-cl,
  author = {Zhang, J and Shen, T and Wang, W and Jiang, X and Ku, W and Sun,
M and Chiang, Y Y},
  booktitle = {{Proceedings of the 2019 20th IEEE International Conference on
Mobile Data Management (MDM)}},
  doi = {10.1109/MDM.2019.00-56},
  issn = {2375-0324},
  month = {June},
  pages = {201--209},
  title = {{A VLOS Compliance Solution to Ground/Aerial Parcel Delivery
Problem}},
  url = {http://dx.doi.org/10.1109/MDM.2019.00-56},
  url-file = {papers/Zhang-et-al.-2019-A-VLOS-Compliance-Solution-to-Ground-Aerial-Parcel-Delivery-Problem.pdf},
  year = {2019}
}

@article{Zhang2019-rj,
  author = {Zhang, Ying and Ma, Qunfei and Chiang, Yao-Yi and Knoblock, Craig
and Zhang, Xin and Yang, Puhai and Gao, Minghe and Hu, Xiang},
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  issn = {0950-7051},
  journal = {Knowledge-Based Systems},
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  pages = {57--72},
  title = {{Extracting geographic features from the Internet: A geographic
information mining framework}},
  url = {http://www.sciencedirect.com/science/article/pii/S0950705119300929},
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  year = {2019}
}

@article{Zhang2020-rb,
  author = {Zhang, Y and Wang, Y and Li, F and Wu, B and Chiang, Y Y and
Zhang, X},
  doi = {10.1109/TITS.2020.2990694},
  issn = {1558-0016},
  journal = {IEEE Transactions on Intelligent Transportation Systems},
  pages = {1--6},
  title = {{Efficient Deployment of Electric Vehicle Charging Infrastructure:
Simultaneous Optimization of Charging Station Placement and
Charging Pile Assignment}},
  url = {http://dx.doi.org/10.1109/TITS.2020.2990694},
  url-file = {papers/Zhang-et-al.-2020-Efficient-Deployment-of-Electric-Vehicle-Charging-In...-timization-of-Charging-Station-Placement-and-Charging-Pile-Assignment.pdf},
  year = {2020}
}

@article{Zhang2021-ju,
  author = {Zhang, Ying and Wu, Bin and Chiang, Yao-Yi and Zhang, Xin and
Chen, Yuanchang and Li, Muyang and Li, Fanyu},
  doi = {10.1016/j.engappai.2021.104378},
  issn = {0952-1976},
  journal = {Engineering applications of artificial intelligence},
  month = {September},
  pages = {104378},
  title = {{BiS4EV: A fast routing algorithm considering charging stations and
preferences for electric vehicles}},
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  volume = {104},
  year = {2021}
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@article{zhang2023optimal,
  author = {Zhang, Ying and Hua, Yunpeng and Kang, Ao and He, Jiyuan and Jia, Meng and Chiang, Yao-Yi},
  journal = {Expert Systems with Applications},
  pages = {120442},
  publisher = {Elsevier},
  title = {Optimal and efficient planning of charging stations for electric vehicles in urban areas: formulation, complexity and solutions},
  volume = {230},
  year = {2023}
}