Unbox Responsible GeoAI: Navigating Climate Extreme and Disaster Mapping
責任あるGeoAIを解き放つ：気候極端現象と災害マッピングをナビゲートする (AI 翻訳)
Hao Li, Steffen Knoblauch
🤖 gxceed AI 要約
日本語
本ポジションペーパーは、気候極端現象・災害マッピングにおけるGeoAIの責任ある利用を批判的GISの視点から検討する。代表性、説明可能性、持続可能性、倫理の4次元を提起し、データ、アプリケーション、社会の3つのガバナンススコープを持つモデルを提案する。将来的な気候レジリエンスのためには、アルゴリズム改善だけでなく、責任あるガバナンス生態系の構築が重要と主張する。
English
This position paper examines responsible GeoAI for climate extreme and disaster mapping from a critical GIS perspective. It proposes four theoretical dimensions (Representativeness, Explainability, Sustainability, Ethics) and a governance model covering Data, Application, and Society scopes. It argues that climate resilience requires not just better algorithms but a governance ecosystem for responsible, ethical, and sustainable GeoAI.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本は地震・洪水など災害大国であり、GeoAIの活用が進む一方、倫理・持続可能性の議論は不十分。本論文のガバナンスモデルは、日本の防災・気候変動適応策におけるGeoAI導入の指針となり得る。また、AIの環境負荷（カーボンフットプリント）への言及は、GXの観点からも注目に値する。
In the global GX context
As global climate disasters intensify, the governance of AI in disaster mapping becomes critical. This paper addresses the ethical, representational, and sustainability dimensions often overlooked in AI-driven climate adaptation. It contributes to global discourse on responsible AI and environmental sustainability, relevant to TCFD/ISSB discussions on climate risk management.
👥 読者別の含意
🔬研究者:This paper provides a conceptual framework for responsible GeoAI governance, useful for researchers in AI ethics, GIS, and climate adaptation.
🏢実務担当者:Practitioners deploying GeoAI for disaster mapping should consider the governance dimensions (data, application, society) to avoid ethical pitfalls and ensure sustainability.
🏛政策担当者:Policymakers should develop regulatory frameworks that embed responsible AI principles in disaster management and climate adaptation.
📄 Abstract（原文）
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.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.48550/arxiv.2605.00315first seen 2026-07-13 07:23:16
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。