Unbox Responsible GeoAI: Navigating Climate Extreme and Disaster Mapping

2026-04-30

AI Summary

This study addresses the frequent neglect of spatial equity, interpretability, sustainability, and ethical considerations in current GeoAI applications for mapping climate extremes and disasters—a gap that risks exacerbating social inequalities and undermining emergency decision-making. Drawing on critical GIS perspectives, the work proposes a four-dimensional theoretical framework centered on representativeness, explainability, sustainability, and ethics, and develops a responsible GeoAI governance model spanning data, application, and societal levels. Departing from mainstream approaches focused primarily on algorithmic optimization, this research emphasizes institutional and ethical architecture design, offering geographic information science a systematic foundation to advance climate resilience toward a more equitable, transparent, and sustainable future.

Abstract

As climate extreme and disaster events become more frequent and intense, Geospatial Artificial Intelligence (GeoAI) has emerged as a transformative approach for large-scale disaster mapping and risk reduction. However, the purely mechanical, performance-driven deployment of GeoAI models can result in amplifying inherent spatial inequalities, preventing effective emergency decision-making, and producing severe environmental carbon footprint. To unbox the concept of responsible GeoAI, this position paper examines its emerging role, e.g., in climate extreme and disaster mapping, from a critical GIS perspective. We address the nexus of responsible GeoAI into four interrelated theoretical dimensions, specifically Representativeness, Explainability, Sustainability, and Ethics, with examples from climate extreme and disaster mapping. Moreover, targeting at the operational practice, we then propose a conceptual governance Model of responsible GeoAI that categorizes its governance practices into Data, Application, and Society scopes. Last, this position paper aims to raise the attention in the broader GIS community that the future of climate resilience relies not just on building better algorithms, but on fostering a governance ecosystem where GeoAI is deployed responsibly, ethically, and sustainably.

Problem

• Responsible GeoAI
• Climate Extreme
• Disaster Mapping
• Spatial Inequality
• Environmental Impact

Innovation

• Responsible GeoAI
• Climate Extreme Mapping
• Disaster Mapping
• AI Governance
• Critical GIS