Large language models, especially GPT-4 with few-shot prompts, can classify topological spatial relations between WKT-encoded geometries with roughly 0.6 to 0.66 accuracy, though errors cluster near conceptually similar relations.
Philosophical Foundations of GeoAI: Exploring Sustainability, Diversity, and Bias in GeoAI and Spatial Data Science
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abstract
This chapter presents some of the fundamental assumptions and principles that could form the philosophical foundation of GeoAI and spatial data science. Instead of reviewing the well-established characteristics of spatial data (analysis), including interaction, neighborhoods, and autocorrelation, the chapter highlights themes such as sustainability, bias in training data, diversity in schema knowledge, and the (potential lack of) neutrality of GeoAI systems from a unifying ethical perspective. Reflecting on our profession's ethical implications will assist us in conducting potentially disruptive research more responsibly, identifying pitfalls in designing, training, and deploying GeoAI-based systems, and developing a shared understanding of the benefits but also potential dangers of artificial intelligence and machine learning research across academic fields, all while sharing our unique (geo)spatial perspective with others.
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cs.CL 1years
2025 1verdicts
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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations
Large language models, especially GPT-4 with few-shot prompts, can classify topological spatial relations between WKT-encoded geometries with roughly 0.6 to 0.66 accuracy, though errors cluster near conceptually similar relations.