Abstract
The timely detection of disasters is essential for effective emergency response. Traditional satellite-based monitoring provides accurate hazard observations but suffers from acquisition delays and weather-dependent imaging conditions. Therefore, recent research increasingly uses rapidly available digital data such as social media, news, and weather observations. However, most approaches analyse these sources in isolation and lack standardised evaluation. We address this gap using a grid-based framework that quantifies disaster detection accuracy relative to satellite-derived reference data. Within this framework, we introduce a multimodal geospatial reasoning method that employs generative Language Models (LMs) to interpret heterogeneous information. The method integrates Bluesky social media posts, GDELT news headlines, and weather observations through structured prompts and relevance-based data retrieval, framing detection as a binary classification problem on an H3 grid. Across two case studies on the 2024 Central Europe floods and the 2025 Southern California wildfires, LM-based detection outperformed traditional hotspot and anomaly detection while requiring only ten content items per prediction. Results were robust across prompt variants, and Automatic Prompt Optimisation (APO) provided only moderate gains. Overall, this research offers the first systematic evaluation of Bluesky, GDELT, and weather data for disaster detection and shows that Foundation Models (FMs) can act as efficient zero-shot or few-shot detectors of natural-hazard-induced disasters.