Geospatial Artificial Intelligence (GeoAI) represents an exciting new research area that incorporates machine intelligence and data-driven approaches for geospatial problem solving. Rapid advances in AI methods, the proliferation of spatial big data, and immense computing power are transforming the way research is conducted and prompts new discoveries. This project develops a new Geospatial Artificial Intelligence (GeoAI) solution to enable large-scale, automated, intelligent, and highly accurate landform feature detection and terrain analysis. Conventional approaches to terrain analysis have been limited to the use of pixel/object-based image analysis and shallow machine learning, which suffer from significant performance challenges when dealing with big data in complex classification tasks. This research, which spatializes deep (machine) learning by incorporating spatial principles and spatial relational data, represents a methodological breakthrough in GeoAI and spatial data science more broadly. Leveraging GeoAI for landform feature recognition enriches spatial knowledge and enhances the understanding of land-surface processes on Earth and other planets. It also benefits geospatial applications that have societal benefit, including anomaly detection that can be used in search and rescue operations and by recognizing landform features indicative of environmental change. The investigators intend on developing the GeoAI community using the model used to establish the Geographic Information Science (GISci) community in the past. GeoAI-related symposia will be organized to serve as an important venue for researchers from diverse disciplines and organizations to discuss new advances and open challenges in GeoAI. The project includes a postdoctoral scholar and trains undergraduate students as interdisciplinary scientists. Both investigators are female scientists and they actively mentor scientists from underrepresented groups. All data and code developed during this project will be open-sourced to benefit the broader geospatial community.

Significant challenges exist in successfully applying GeoAI to terrain analysis, including the complexity and diversity in landform features, the dearth of training data, the lack of spatial knowledge in model design, and the limited understanding of machine inferential processes. This research will tackle such challenges by developing (1) a comprehensive terrain dataset GeoNat to support terrain analysis and various machine learning tasks; (2) a machine learning model TerrainAI that injects key spatial principles (spatial autocorrelation and spatial heterogeneity) to enable cross-scale deep learning from multi-source, georeferenced data; and (3) an interactive visualization tool that opens up the "black box" of the machine's learning and decision process. Relying on these tools, three research questions will be answered: (I) What are the unique spatial structures, patterns, and spatial scale that a machine learns to differentiate landform features? (II) How do human and machine recognition processes compare? And (III) How can the underlying geomorphological processes that yield certain forms of a feature in different landscapes be predicted? The identification of process-form relationships significantly impacts the agenda of geographical, spatial scientific and related sciences such as geomorphology in fostering the creation of a community-consensus landform classification system. The TerrainAI model is not limited to the study of landforms but is generalizable and applicable to detect any geographical objects, both natural and human-made.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

National Science Foundation (NSF)
Division of Behavioral and Cognitive Sciences (BCS)
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Scott Freundschuh
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Arizona State University
United States
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