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学术共同体：会议、团队与期刊

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学术共同体：会议、团队与期刊

相关会议、Workshop、研究团队、GIScience 奠基学者与期刊入口。

来源与版本

本页整理自 AutoGeoAI4Sci/awesome-autonomous-geoai，上游 commit 2437033（2026-07-26）。 GIStudioNote 只调整文档结构与导航；资源描述和外部链接来自上游，时效性与准确性请以原项目及链接目标为准。

Conferences

Relevant conferences where the geospatial, GeoAI, and AI4Science communities (or parts thereof) meet.

The order of entries within categories is alphabetical.
Workshops and special sessions are included, where they play a significant role in the GeoAI and AI4Science communities.

Major AI Conferences (Host Venues)

  • ACM TRUST 2027: Trustworthy Spatial AI Track
    Call for Papers for the “Trustworthy Spatial AI: Advancing Responsible Intelligence for Spatial Data and Applications” track at the 2027 ACM International Conference on Trustworthy and Responsible AI and Computing Systems. The conference will be held March 7-9, 2027, in Washington, DC. The track covers responsible spatial AI, including privacy-preserving spatial algorithms, bias detection and mitigation, explainable spatial models, and governance for spatial data and applications. Track chairs: Zhe Zhang (Texas A&M University), Shaowen Wang (University of Illinois Urbana-Champaign), Tianbao Yang (Texas A&M University), and Zhe Jiang (University of Florida).
    https://eigtrust.acm.org/trust2027/

  • ACM SIGSPATIAL OASIS 2026 Student Challenge: Open Agents with Spatial Intelligence for Social Good — The first ACM SIGSPATIAL student challenge on open-source GeoAI agents for social good. Student teams design autonomous agents that retrieve, clean, and analyze open geospatial data while supporting human-in-the-loop oversight for responsible, value-aligned spatial decision-making.
    Important dates (Anywhere on Earth): team early registration by July 31, 2026; short paper and code submission by September 4, 2026; finalist presentation and demo on November 3, 2026.
    Track A: Disaster Resilience & Vulnerability Analysis focuses on automated risk analysts for natural hazards such as earthquakes, floods, wildfires, and severe storms. Suggested problems include earthquake impact modeling in coastal cities, wildfire evacuation vulnerability in wildland-urban interfaces, and flood risk assessment for critical urban infrastructure. Key challenges include raster-vector hazard overlays, multiscale spatial joins, CRS mismatch resolution during ingestion, and trade-off analysis between physical economic loss, social marginalization, life-safety evacuation, and long-term recovery support. HITL controls can let emergency planners or community representatives adjust scenario assumptions and priority weights.
    Track B: Public Health & Spatial Equity focuses on public-health intervention siting and resource allocation, such as emergency cooling centers, mobile vaccine clinics, and environmental monitoring stations. Key challenges include location-allocation and coverage optimization, network travel time analysis under changing traffic or transit conditions, and trade-off analysis between financial cost, geographic coverage, population density, and equity for historically underserved or isolated communities. HITL controls can let public-health decision-makers tune equity constraints and coverage priorities.
    https://rsvp.withgoogle.com/events/oasis-2026

  • GeoAI Conference (2026) — A conference dedicated to GeoAI, spatial intelligence, and AI for geographic data science; includes workshops, tutorials, and industry sessions.
    https://geoaiconference.org/

  • 3rd Workshop on Computer Vision for Earth Observation (CV4EO) Applications (WACV 2026 Workshop) — Computer vision + Earth observation research.
    https://wacv.thecvf.com/Conferences/2026/Workshops

  • 2nd Workshop on Computer Vision for Geospatial Image Analysis (WACV 2026 Workshop) — Geospatial imagery + AI.
    https://wacv.thecvf.com/Conferences/2026/Workshops

  • CVPR – MORSE: Foundation and Large Vision Models in Remote Sensing (CVPR 2026 Workshop) — Focuses on foundation models, large vision models, and multimodal learning for remote sensing and Earth observation.
    https://sites.google.com/view/cvpr-morse/home

  • ICLR – Machine Learning for Remote Sensing (ML4RS, ICLR 2026 Workshop) — Advances in machine learning methods for satellite imagery, Earth observation, and geospatial analysis.
    https://ml-for-rs.github.io/iclr2026/

  • AAAI – AI for Urban Planning (AAAI 2026 Workshop) — AI, GeoAI, and data-driven methods for urban planning, smart cities, and spatial decision-making.
    https://ai-for-urban-planning.github.io/AAAI26-workshop/

  • KDD – AI for Sciences Track (KDD 2026 Track) — A new peer-reviewed track at KDD 2026 focusing on AI for scientific discovery, interdisciplinary AI applications (including climate, environmental science, and related data-driven research); accepted papers are included in the ACM Digital Library and indexed by Google Scholar.
    https://kdd2026.kdd.org/ai4sciences-track-call-for-papers/?utm_source=chatgpt.com

  • SensAI Hack — An AI-focused hackathon bringing together developers, researchers, and builders to prototype applied AI projects; a venue for hands-on experimentation with autonomous and agentic systems.
    https://sensaihack.com/

  • CVPR – EarthVision: Large Scale Computer Vision for Remote Sensing Imagery (CVPR Workshop) — A long-running workshop on machine learning and computer vision for Earth observation and remote sensing; accepted workshop papers are published in the official CVPR workshop proceedings, searchable via Google Scholar.
    https://cvpr.thecvf.com/virtual/2025/workshop/32300

Research Groups

Representative research groups advancing GeoAI, GIScience, spatial intelligence, and AI-driven Earth and environmental sciences. It should be strongly emphasized that these research groups are not listed in any particular order; they are all excellent. This list is not exhaustive, and community suggestions are welcome.

Foundational Scholars in GIScience

  • Michael F. Goodchild — University of California, Santa Barbara
    A pioneer and intellectual architect of GIScience. His work on spatial uncertainty, geographic information theory, discrete global grids, and volunteered geographic information laid the conceptual foundation for modern GIScience and, by extension, GeoAI and spatially aware AI systems.
    https://geog.ucsb.edu/people/faculty/michael-goodchild

  • John P. Wilson — Founder, Spatial Sciences Institute, University of Southern California
    (https://johnwilson.usc.edu/), University of Southern California.
    A leading scholar in spatial sciences, Dr. Wilson founded USC’s Spatial Sciences Institute and has advanced interdisciplinary spatial education and research integrating GIS, remote sensing, spatial analysis, and geospatial technologies across engineering, public health, sociology, and environmental sciences. His work has significantly shaped the institutional development of spatial sciences as a cross-cutting academic field.
    https://spatial.usc.edu/

  • CyberGIS Center — Led by Dr. Shaowen Wang
    (https://cybergis.illinois.edu/), University of Illinois Urbana–Champaign.
    Research focuses on CyberGIS, high-performance geospatial computing, scalable spatial data science, and geospatial artificial intelligence (GeoAI) for sustainability and complex environmental challenges. The CyberGIS Center develops advanced geospatial cyberinfrastructure integrating AI, supercomputing, and large-scale spatial analytics to enable next-generation geospatial discovery and decision support systems.
    https://cybergis.illinois.edu/

  • Urban Artificial Intelligence Lab — Led by Dr. Xinyue Ye
    (https://engineering.tamu.edu/cse/profiles/ye-xinyue.html), Texas A&M University.
    Research focuses on geospatial artificial intelligence, urban informatics, smart cities, urban data science, and digital twins for the built environment. The lab develops AI-enabled urban analytics, simulation platforms, and open-source tools to support resilient, sustainable, and climate-aware urban planning and decision-making.
    https://urbanai.tamids.tamu.edu/

  • Geospatial Exploration and Resolution (GEAR) Lab — Led by Dr. Lei Zou
    (https://artsci.tamu.edu/geography/contact/profiles/lei-zou.html), Texas A&M University.
    Research focuses on GeoAI, GIScience, spatial data science, and geospatial modeling, with applications in disaster resilience, climate change, environmental health, and public health.
    https://www.geoearlab.com/

  • City Analytics & Informatics Research Group — Led by Dr. Heng Cai
    (https://artsci.tamu.edu/geography/contact/profiles/heng-cai.html), Texas A&M University.
    Research focuses on GIScience, geospatial analytics, spatial decision support, and resilience-oriented applications addressing natural hazards, climate change, and long-term environmental risks.
    https://gis-resilience.info/

  • GeoDSLab@UW-Madison — Led by Dr. Song Gao
    (https://geography.wisc.edu/staff/gao-song/), University of Wisconsin–Madison.
    Research focuses on GIScience, GeoAI, geospatial data science, human mobility, social sensing, spatial networks, and urban informatics, with strong emphasis on large-scale spatial data analytics, multimodal data integration, and AI-driven understanding of human–environment systems.
    https://geography.wisc.edu/geods/research

  • Spatial Computing and Data Mining (SCDM) Lab — Led by Dr. Qunying Huang
    (https://geography.wisc.edu/staff/huang-qunying/), University of Wisconsin–Madison.
    Research focuses on GIScience, spatial big data analytics, GeoAI, spatial computing, and remote sensing. The lab integrates physical sensing (e.g., Earth observation) and social sensing (e.g., mobile phone and social media data) to study natural hazards, environmental justice, human mobility, and social inequality, leveraging cloud computing, GPU acceleration, and large-scale geospatial data mining techniques.
    https://scdm.geography.wisc.edu/

  • GeoAI Lab@UB — Led by Dr. Yingjie Hu
    (https://www.acsu.buffalo.edu/~yhu42/), University at Buffalo, The State University of New York.
    Research focuses on GeoAI, geospatial data science, artificial intelligence for disaster management, Earth observation, remote sensing, and large-scale spatial analytics.
    https://geoai.geog.buffalo.edu/

  • Spatially Explicit Artificial Intelligence (SEAI) Lab — Led by Dr. Gengchen Mai
    (https://gengchenmai.github.io/), University of Texas at Austin.
    Research focuses on spatially explicit machine learning, GeoAI, GIScience, geographic question answering, spatial knowledge representation, and AI models that leverage spatial inductive bias for geospatial reasoning.
    https://sites.utexas.edu/seai/

  • Geoinformation and Big Data Research Laboratory (GIBD) — Led by Dr. Zhenlong Li
    (https://www.geog.psu.edu/directory/zhenlong-li), Pennsylvania State University.
    Research focuses on GIScience, GeoAI, geospatial big data analytics, spatial computing, and autonomous GIS, with applications in natural hazards, public health, population mobility, environmental and climate change, and data-driven spatial decision-making.
    https://sites.psu.edu/giscience/

  • CyberInfrastructure and Computational Intelligence (CICI) Lab — Led by Dr. Wenwen Li
    (https://wenwenspatial.github.io/), Arizona State University.
    Research focuses on geospatial cyberinfrastructure, spatial data infrastructure (SDI), geospatial knowledge discovery, AI-driven geospatial analytics, spatial web services (OGC), and scalable geocomputation. The lab develops intelligent geospatial platforms such as PolarHub and AI-enabled cyberinfrastructure systems to support large-scale Earth observation data integration, distributed geospatial services, and next-generation GIScience.
    https://cici.lab.asu.edu/

  • Geospatial Responsible AI for Nature–Human Dynamics (GRIND) Lab — Led by Dr. Bing Zhou
    (https://geography.utk.edu/people/instructional-faculty/bing-zhou/), University of Tennessee, Knoxville.
    Research focuses on GIScience, GeoAI, responsible geospatial AI, spatial computing, and data-driven analysis of coupled nature–human systems, with applications in natural disasters, climate resilience, environmental health, and social vulnerability.
    https://spgbarrett.wixsite.com/bingzhou

  • Geospatial Intelligent Sensing and Mapping (GISense) Lab — Led by Dr. Yuhao Kang
    (https://liberalarts.utexas.edu/geography/faculty/yk9999), University of Texas at Austin.
    Research focuses on GIScience, GeoAI, human-centered GeoAI, geospatial data science, social sensing, cartography, and spatial analysis, with applications in human mobility, urban planning, environmental psychology, maternal health, and responsible geospatial artificial intelligence.
    https://sites.utexas.edu/gisense/

  • Humanistic GIS Laboratory (HGIS Lab) — Led by Dr. Bo Zhao
    (https://hgis.uw.edu/), University of Washington.
    The Humanistic GIS Laboratory explores the intersection of geospatial technologies, digital geographies, and human-centered spatial analysis. The lab investigates how GIS, social media, and emerging AI technologies shape geographic knowledge, spatial narratives, and public understanding of place. Research topics include humanistic GIS, geospatial misinformation, digital place-making, and the societal implications of geospatial data and mapping technologies.
    https://hgis.uw.edu/

  • Smart Cities for Good (SCG) Lab — Led by Dr. Jungwhan Kim
    (https://geography.vt.edu/people/junghwan-kim.html), Virginia Tech.
    Research focuses on human mobility, accessibility, and travel behavior, environmental health, geospatial data science, and ethical issues in geospatial data and AI, with applications in smart cities, transportation planning, public health, and evidence-based urban policy.
    https://www.junghwankim.org/smart-cities-for-good

  • GeoHealth Lab — Led by Dr. Yoo Min Park
    (https://geography.uconn.edu/person/yoo-min-park/), University of Connecticut.
    Research focuses on environmental health, environmental justice, health disparities, and spatial epidemiology using GIS, geospatial technologies, community-engaged research, and spatial statistical methods. The lab integrates spatial analysis and public health research to understand environmental exposures, social vulnerability, and health inequities across communities.
    https://www2.yoominpark.com/

  • Yue Lin Research Group — Dr. Yue Lin
    (https://linyuehzzz.github.io/), University of Illinois Urbana–Champaign.
    Research explores the intersection of geospatial artificial intelligence, spatial data science, and human values. The group investigates critical societal questions surrounding geospatial technologies, including algorithmic bias, location privacy, and the ethical design of GeoAI systems. Their work emphasizes responsible and human-centered geospatial computing, examining how spatial AI systems influence society, governance, and decision-making in data-driven environments.
    https://linyuehzzz.github.io/

  • Cyberinfrastructure and Spatial Decision Intelligence (CIDI) Research Group — Led by Dr. Zhe (Sarina) Zhang
    (https://artsci.tamu.edu/geography/contact/profiles/zhesarina-zhang.html), Texas A&M University.
    Research focuses on GIScience, CyberGIS, geospatial artificial intelligence (GeoAI), participatory GIS, and spatial decision intelligence. The group develops cyberinfrastructure-enabled hybrid spatial decision support systems integrating high-performance computing, distributed systems, and AI to address disaster management, coastal resilience, critical infrastructure protection, and sustainable development challenges.
    https://cidigis.com/

  • Spatial Data Lab (SDL) — Led by Dr. Shuming Bao
    (https://sdl.gis.harvard.edu/people/), Harvard University Spatial Data Lab@Center for Geographic Analysis, IQSS.
    Research focuses on spatial data science, GIScience, GeoAI, spatiotemporal big data analytics, human mobility and migration, computational social science, and human-centered GeoAI, with applications in urban analytics, public health, digital health geography, and human–environment interactions.
    https://sdl.gis.harvard.edu/

  • Siqin Wang Research Group — Led by Dr. Siqin Wang
    (https://dornsife.usc.edu/spatial/profile/siqin-sisi-wang/), Spatial Science Institute, University of Southern California.
    Dr. Wang’s research interests are in GIScience, spatiotemporal big data analytics, computational social science, digital health geography, human-centered GeoAI, human mobility and migration, smart cities and human-climate interactions and she has published extensively in these areas.
    https://sdl.gis.harvard.edu/sdl-research-affiliates

  • Xiao Huang Research Group — Led by Dr. Xiao Huang
    (https://winshipcancer.emory.edu/profiles/huang-xiao.php), Emory University.
    Research focuses on GeoAI, GIScience, big data analytics, disaster mapping and mitigation, urban informatics, vulnerability and social inequity, and human–environment interactions, with applications in disaster resilience, public health, and climate risk assessment.
    https://www.xiaohuang116.com/

  • COMPASS Lab (Laboratory of Computational Spatial Science for Sustainability) — Led by Dr. Yi Qiang
    (https://www.usf.edu/arts-sciences/departments/geosciences/people/faculty/qiang-yi.aspx), University of South Florida.
    Research focuses on GIScience, spatial data science, spatio-temporal modeling, geocomputation, and GeoAI applications for sustainability and resilience. The lab develops analytical frameworks and computational tools to support disaster management, infrastructure resilience, environmental justice, and community sustainability through large-scale spatial data analytics and machine learning.
    https://compasslab.org/

  • Urban Spatial Informatics Lab — Led by Dr. Xiaojiang Li
    (https://www.design.upenn.edu/people/xiaojiang-li), University of Pennsylvania.
    Research focuses on urban spatial informatics, GeoAI, street-view analytics, urban sensing, geospatial big data, and data-driven urban science, with strong emphasis on computational modeling of urban form, street greenery, built environment characteristics, and human activities using multimodal geospatial data such as street-view imagery, remote sensing, and human trace data.
    https://www.urbanspatial.info/

  • Geospatial Data Intelligence (GeoDI) Lab — Led by Dr. Di Zhu
    (https://geodi.umn.edu/), University of Minnesota, Twin Cities.
    Research focuses on GIScience, spatial statistics, geospatial artificial intelligence (GeoAI), spatiotemporal big data analytics, and intelligent social sensing. The lab develops explainable spatial models and AI-driven analytical frameworks to understand human–environment interactions, urban dynamics, public health, mobility, and sustainability using large-scale geospatial data.
    https://geodi.umn.edu/

  • Knowledge Computing Lab — Led by Dr. Yao-Yi Chiang
    (https://cse.umn.edu/cs/yao-yi-chiang), University of Minnesota, Twin Cities.
    Research lies at the intersection of computer science and spatial sciences, focusing on spatial machine learning, knowledge computing, heterogeneous data fusion, and AI-driven spatiotemporal prediction. The lab develops intelligent systems that exploit unique spatial data properties and structural knowledge to advance computer vision, natural language processing, and map-based geospatial intelligence.
    https://cse.umn.edu/cs/yao-yi-chiang

  • Ziqi Li Research Group — Florida State University
    (https://sites.google.com/view/ziqi-li), Department of Geography, FSU.
    Research focuses on spatial statistics, geospatial artificial intelligence (GeoAI), explainable artificial intelligence (XAI), and open-source spatial modeling. Developer of GeoShapley and core contributor to PySAL and Multi-scale Geographically Weighted Regression (MGWR).
    https://sites.google.com/view/ziqi-li

  • Mobility Science Lab (MSL) — Led by Dr. Yang Xu
    (https://www.polyu.edu.hk/lsgi/people/academic-staff/yang-xu/), The Hong Kong Polytechnic University.
    Research focuses on GIScience, urban informatics, human mobility analytics, and data-driven urban systems, leveraging large-scale geospatial data to understand human activity patterns and urban dynamics.
    https://mobility-science-lab.com/

Journals

Key journals publishing high-impact research on GeoAI, remote sensing, GIScience, and AI4Science. The following journals are ordered roughly by overall prestige and influence within the field. This list is not exhaustive, and community contributions and suggestions are very welcome.

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