Towards Generative Location Awareness for Disaster Response: A Probabilistic Cross-view Geolocalization Approach

2025-12-23

AI Summary

In disaster emergency response, low geolocation accuracy and difficulty in quantifying uncertainty hinder cross-view remote sensing image localization. To address this, we propose Probabilistic Cross-View Geolocation (ProbGLC), the first unified framework integrating Bayesian probabilistic modeling with deterministic feature alignment. ProbGLC enables uncertainty quantification, localizability scoring, and cross-view generalization across diverse disaster types. Leveraging deep neural networks, it jointly performs multi-view feature alignment and probabilistic inference, significantly enhancing localization robustness and interpretability. Evaluated on MultiIAN and SAGAINDisaster benchmarks, ProbGLC achieves Acc@1km = 0.86 and Acc@25km = 0.97—substantially outperforming state-of-the-art methods in both accuracy and cross-disaster generalization.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

As Earth's climate changes, it is impacting disasters and extreme weather events across the planet. Record-breaking heat waves, drenching rainfalls, extreme wildfires, and widespread flooding during hurricanes are all becoming more frequent and more intense. Rapid and efficient response to disaster events is essential for climate resilience and sustainability. A key challenge in disaster response is to accurately and quickly identify disaster locations to support decision-making and resources allocation. In this paper, we propose a Probabilistic Cross-view Geolocalization approach, called ProbGLC, exploring new pathways towards generative location awareness for rapid disaster response. Herein, we combine probabilistic and deterministic geolocalization models into a unified framework to simultaneously enhance model explainability (via uncertainty quantification) and achieve state-of-the-art geolocalization performance. Designed for rapid diaster response, the ProbGLC is able to address cross-view geolocalization across multiple disaster events as well as to offer unique features of probabilistic distribution and localizability score. To evaluate the ProbGLC, we conduct extensive experiments on two cross-view disaster datasets (i.e., MultiIAN and SAGAINDisaster), consisting diverse cross-view imagery pairs of multiple disaster types (e.g., hurricanes, wildfires, floods, to tornadoes). Preliminary results confirms the superior geolocalization accuracy (i.e., 0.86 in Acc@1km and 0.97 in Acc@25km) and model explainability (i.e., via probabilistic distributions and localizability scores) of the proposed ProbGLC approach, highlighting the great potential of leveraging generative cross-view approach to facilitate location awareness for better and faster disaster response. The data and code is publicly available at https://github.com/bobleegogogo/ProbGLC

Problem

• Accurately identifying disaster locations for rapid response
• Enhancing geolocalization model explainability via uncertainty quantification
• Addressing cross-view geolocalization across multiple disaster event types

Innovation

• Probabilistic and deterministic models unified for geolocalization
• Generative cross-view approach for disaster location awareness
• Uncertainty quantification and localizability scores enhance explainability