Unbox Responsible GeoAI: Navigating Climate Extreme and Disaster Mapping

A position paper examines Geospatial Artificial Intelligence (GeoAI) deployment in climate and disaster mapping, arguing that purely performance-driven AI models risk amplifying spatial inequalities and environmental harm. The authors propose a governance framework centered on representativeness, explainability, sustainability, and ethics to ensure responsible GeoAI development.

This academic position paper addresses a critical gap in how artificial intelligence is deployed for climate resilience and disaster response. As extreme weather events intensify globally, GeoAI technologies offer significant promise for rapid mapping and early warning systems. However, the paper's core argument—that algorithmic performance optimization alone is insufficient—reflects growing concerns in the AI governance community about unintended consequences of technological deployment.

The framework proposed here extends beyond traditional machine learning metrics. By emphasizing representativeness, the authors highlight how training data biases can disadvantage vulnerable regions with limited historical disaster records. Explainability concerns focus on making AI decisions transparent to emergency responders who must act on predictions. The sustainability dimension addresses the computational carbon cost of large-scale GeoAI inference, particularly problematic when deployed in climate-vulnerable regions with limited energy infrastructure.

For the broader geospatial technology and climate-tech sectors, this research validates market demand for responsible AI solutions. Organizations developing disaster mapping tools increasingly face pressure from stakeholders—governments, NGOs, and communities—to demonstrate ethical governance. This creates competitive advantages for companies that embed accountability mechanisms early rather than retrofitting them later.

The proposed governance model spanning data, application, and society scopes provides practical guidance for practitioners. As climate adaptation becomes central to corporate and government strategy, responsible GeoAI deployment could become a differentiator in procurement decisions. The paper signals that future climate resilience infrastructure will require not just algorithmic innovation but robust governance ecosystems—a shift that favors organizations thinking systemically about technology deployment consequences.

• →GeoAI shows transformative potential for disaster mapping but risks amplifying spatial inequalities without responsible governance frameworks.
• →The proposed model addresses four dimensions: representativeness, explainability, sustainability, and ethics in climate-focused AI applications.
• →Uncontrolled algorithmic optimization can worsen environmental carbon footprint and prevent effective emergency decision-making.
• →Responsible GeoAI governance requires coordination across data practices, application design, and societal impact assessment.
• →Climate resilience infrastructure increasingly demands responsible AI deployment as a foundational requirement, not an optional consideration.