Recording Plan: 

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Call For Participation

Description:

Description:
As Michael Goodchild puts it, “A model is said to be spatially explicit when it differentiates behaviors and predictions according to spatial locations”. In the context of machine learning (ML) and artificial intelligence (AI), we see the need for spatially explicit models focusing on better ways to design ML/AI model architectures for consuming spatial information by incorporating spatial knowledge and spatial inductive bias such as spatial heterogeneity, spatial autocorrelation, map projection, and so on. The recent decade has witnessed many advancements of spatially explicit ML and AI technology for various GeoAI problems such as geographic question answering, geographic knowledge graph summarization, POI distribution modeling, trajectory prediction, traffic forecasting, geo-aware fine-grained species recognition, building pattern recognition, map generalization, remote sensing image classification, terrain feature recognition, and so on. However, adding spatial inductive bias to make spatially explicit models will yield more complex ML/AI model architectures. Investigating the trade-off between designing a spatially explicit ML/AI architecture versus a more general setup that would have to learn to value space implicitly is an important research question for the spatial data science and GeoAI community. The recent advancement of various foundation models (e.g., ChatGPT, GPT-4o, Gemini, LLAVA, etc) also made us think about what additional benefits spatial thinking can bring to the table if a large foundation model can already achieve superior zero-shot or few-shot performance on various geospatial tasks.

In this session, we welcome submissions broadly contributing to the research on GeoAI and spatially explicit AI/ML. AI/ML algorithm design and developments on geospatial data are particularly welcome.

Topics include (but are not limited to):
1. Spatially Explicit AI for Geospatial Semantics
-- Geo-text Mining
-- Geographic Knowledge Graph and Ontology Engineering
-- Geographic Question Answering
-- Geographic Entity Coreference Resolution and Gazetteer Matching
-- Geographic Ontology Alignment
-- Toponym Recognition and Resolution
-- Geographic Knowledge Graph Summarization
-- Geographic Entity Similarity Measurement
-- Geographic Relation Reasoning
2. Spatially Explicit AI for Remote Sensing
-- Remote Sensing Image Classification
-- Land Use and Land Cover Classification
-- Geospatial Object Recognition and Localization
-- Unsupervised/Self-Supervised Learning on Remote Sensing Data
-- Deep Generative Models on Remote Sensing Images (e.g., Superresolution, Style Transfer)
3. Spatially Explicit AI for Urban Computing
-- Traffic Forecasting
-- Trajectory Prediction and Generation
-- Urban Functional Zone Detection
-- POI Distribution Prediction and Spatially Explicit Location Recommendation Systems
4. Spatially Explicit AI for Earth System Science
-- Weather Forecasting and Extreme Weather Prediction
-- Carbon Monitoring
-- Climate Projection
5. Spatially Explicit AI for Computational Sustainability
-- Economic Development Prediction and Mapping
-- Water Quality Mapping
6. Spatially Explicit AI for Health
-- AI for Medical Equipment Inequality
-- Environmental-Related Health Issue Prediction
7. Geo-Foundation Model
-- Visual Foundation Models for Remote Sensing Imagery
-- Large Language Models for Geospatial Application
-- Multimodal Foundation Models for Geospatial Applications

Organizers:
1. Gengchen Mai (gengchen.mai@austin.utexas.edu), Assistant Professor, Department of Geography and the Environment, University of Texas at Austin
2. Yao-Yi Chiang (yaoyi@umn.edu), Associate Professor, Department of Computer Science & Engineering, University of Minnesota-Twin Cities
3. Zhangyu Wang (zhangyuwang@ucsb.edu), Ph.D. Student, Department of Geography, University of California, Santa Barbara
4. Di Zhu (dizhu@umn.edu), Assistant Professor, Department of Geography, Environment and Society, University of Minnesota-Twin Cities
5. Yiqun Xie (xie@umd.edu), Assistant Professor, Department of Geographical Sciences, University of Maryland, College Park
6. Hao Yang (haoyang@uga.edu), Ph.D. Student, Department of Geography, University of Georgia

Steer Committee:
1. X. Angela Yao (xyao@uga.edu), Full Professor, Department of Geography, University of Georgia
2. Krzysztof Janowicz (krzysztof.janowicz@univie.ac.at), Full Professor, University of Vienna & University of California Santa Barbara

If you are interested in participating in this session, please email an abstract and PIN to Gengchen Mai (gengchen.mai@austin.utexas.edu) and Zhangyu Wang (zhangyuwang@ucsb.edu) by Nov. 15th, 2024.

Non-Presenting Participants