This repository contains the reproduction code and materials for the paper: Hanny, D., Dastidar, K.G., Wieland, M., Granitzer, M. & Resch, B. (2026). Towards Multimodal Geospatial Reasoning: A Foundation Model Approach for Disaster Detection from Social Media, News, and Weather Data. [Accepted for publication in Natural Hazards] 📄 Overview This research introduces a grid-based framework that quantifies disaster detection accuracy relative to satellite-derived reference data. We employ generative Language Models (LMs) to interpret heterogeneous information from Bluesky social media posts, GDELT news headlines, and weather observations through structured prompts and relevance-based data retrieval. The method frames detection as a binary classification problem on an H3 grid. Our analysis pipeline includes: Data collection: Custom keyword-based crawling of Bluesky posts, GDELT news, and weather observations Data aggregation: Structured aggregation of multimodal data to H3 grid cells Methodology Statistical anomaly detection: Statistical hotspot and anomaly detection as baseline methods Foundation Model inference: LM-based interpretation of heterogeneous information sources Evaluation: Systematic comparison against satellite-derived reference Case studies: 2024 Central Europe floods and 2025 Southern California wildfires 📁 Repository Structure The analysis pipeline is spread across several scripts and Jupyter/Marimo notebooks. A full overview is available below. ├── data/ # Data files │ ├── raw/ # Raw Bluesky, GDELT, and weather data │ │ ├── 2020_california_wildfires/ │ │ ├── 2024_central_europe_floods/ │ │ ├── 2025_socal_wildfires/ │ │ ├── auxiliary/ # Additional reference data │ │ └── dlr/ # Satellite reference data │ ├── processed/ # Processed datasets │ ├── results/ # Evaluation results │ └── mapping_data/ # Geospatial reference data │ ├── notebooks/ # Analysis workflow (run in ascending order) │ ├── 01_bsky_data_collection.ipynb # Bluesky data collection │ ├── 02_esda/ # Exploratory spatial data analysis │ ├── 02_ground_truth_prep.py # Ground truth preparation │ ├── 03_data_aggregation.py # Data aggregation to H3 grid │ ├── 04_statistical_baseline.py # Statistical baseline methods │ ├── 05_prompt_optimisation.py # Prompt optimization experiments │ ├── 06_few_shot_selection.py # Few-shot example selection │ ├── 07_in_context_learning.py # Main LLM inference pipeline │ ├── 08_ablation_study.py # Ablation experiments │ ├── 09_mixed_ensemble.py # Ensemble methods │ ├── 10_visualisation.py # Result visualization │ ├── 11_data_anonymisation.py # Data anonymization │ └── 13_rev_*.py # Additional experiments during paper revisions │ ├── scripts/ # Non-interactive scripts │ ├── crawling/ # Bluesky and GDELT data collection scripts │ ├── geoparsing/ # Location extraction from text │ ├── get_weather_data.py # Weather data retrieval │ └── in_context_inference.py # LLM inference helper │ ├── src/ # Helper modules and reusable functions │ ├── bsky_search.py # Bluesky crawling algorithm │ ├── ensemble.py # Model ensemble methods │ ├── eval_metrics.py # H3 grid-based evaluation metrics │ ├── helpers.py # Data processing utilities │ ├── validation.py # Validation functions │ ├── visualisation.py # Visualization functions │ ├── hotspot/ # Hotspot detection baselines │ ├── in_context_learning/ # LLM prompt templates │ ├── irchel_geoparser/ # Geoparsing tools │ └── nlp/ # NLP processing utilities │ ├── prompts/ # LLM prompt templates ├── figures/ # Generated visualizations ├── logs/ # Log files ├── Dockerfile ├── docker-compose.yml ├── requirements.txt └── README.md ⚙️ Getting Started To reproduce the experiments, we recommend using Docker for a consistent environment. The individual notebooks can be run using marimo as follows: docker compose run --rm --service-ports marimo This will start a Marimo notebook server at localhost:8080. Alternatively, you can run the notebooks directly as Python scripts, though the marimo interface is recommended. For LM-based inference, a running Ollama instance on localhost:11434 or an OpenAI key stored as OPENAI_API_KEY environment variable are furthermore required. Please pull all desired models before running the script. 📊 Data Availability The primary datasets supporting the conclusions of this article are available in the repository on Zenodo under the DOI 10.5281/zenodo.20038116. 📖 Citation If you use this code or material in your research, please cite our work accordingly. @article{Hanny.2026, title = {Towards Multimodal Geospatial Reasoning: A Foundation Model Approach for Disaster Detection from Social Media, News, and Weather Data}, author = {Hanny, David and Dastidar, Kanishka Ghosh and Wieland, Marc and Granitzer, Michael and Resch, Bernd}, journal = {Natural Hazards}, year = {2026} }