| --- |
| language: |
| - en |
| license: apache-2.0 |
| size_categories: |
| - 1K<n<10K |
| task_categories: |
| - text-generation |
| - visual-question-answering |
| - time-series-forecasting |
| - text-classification |
| tags: |
| - climate-change |
| - natural-disasters |
| - causal-reasoning |
| - multimodal |
| - earth-observation |
| - sentinel |
| - open-meteo |
| - em-dat |
| --- |
| |
| # DisasterChain: a multimodal global disaster dataset linking Earth observation, meteorology and structured narratives |
|
|
| ## Dataset Description |
|
|
| - **Homepage:** [http://disasterchain.icar.cnr.it](http://disasterchain.icar.cnr.it) |
| - **Paper:** [Coming Soon...] |
| - **Repository:** https://github.com/Franco7Scala/DisasterChain |
| - **Point of Contact:** [Francesco Scala](francesco.scala@icar.cnr.it) |
|
|
| ### Dataset Summary |
|
|
| **DisasterChain** is a high-fidelity, multimodal dataset designed to advance research in causal reasoning, environmental monitoring, and disaster impact assessment. Building upon the EM-DAT database, it provides a comprehensive reconstruction of over 1,400 global extreme environmental events from 2014 onwards. |
|
|
| Unlike traditional disaster catalogs that only provide static metadata, this dataset introduces **Grounded Causal Impact Chains**. Using advanced Large Language Models (Qwen 2.5 72B and Llama 3 70B), we extract step-by-step causal sequences of physical and socio-economic impacts (e.g., *Extreme Precipitation -> River Overflow -> Flood -> Infrastructure Damage -> Displacement*). Crucially, every step in the causal chain is strictly grounded to real-world news reports via verified supporting quotes. |
|
|
| Furthermore, each event is enriched with extensive multimodal data, including 21-day weather time series and multispectral satellite imagery availability, making it a unique resource for bridging Natural Language Processing (NLP) and Earth Observation (EO). |
|
|
| ### Supported Tasks and Applications |
|
|
| - **Causal Reasoning & Event Extraction:** Training models to identify cascading effects in unstructured text; |
| - **Multimodal Disaster Assessment:** Combining satellite imagery (Sentinel-1, Sentinel-2), weather data, and text to predict physical damage or human casualties; |
| - **Climate Impact Analysis:** Analyzing the socio-economic downstream impacts of specific weather triggers across different global regions. |
|
|
| ## Dataset Structure |
|
|
| The dataset is provided in `JSON` format. The core JSON structure contains the following nested objects for each disaster event: |
|
|
| ### 1. Root Metadata & Impact Data |
| Standardized identifiers, geographical coordinates, dates, and quantitative human impact metrics (deaths, affected population, financial damage) sourced from EM-DAT. |
|
|
| ### 2. `weather_data` |
| Retrieved via the Open-Meteo API, containing: |
| - Pre-event and post-event statistical summaries (mean rainfall, snowfall, min/max temperatures); |
| - A **21-day daily time series** capturing the meteorological evolution before, during, and after the event. |
| |
| ### 3. `satellite_data` |
| Sourced via Copernicus Data Space Ecosystem (CDSE) / Sentinel Hub: |
| - Precise bounding boxes (`bbox`) for the affected areas; |
| - Temporal availability arrays for Sentinel-1 (SAR), Sentinel-2 (Optical/False Color), and Sentinel-3 (Thermal); |
| - Integration with ESA WorldCover land use data. |
|
|
| ### 4. `news_data` |
| Metadata regarding the information retrieval process: |
| - Sources queried (Google News, ReliefWeb, IFRC, Wikipedia); |
| - Relevance scores and filtering penalties ensuring only high-quality, event-specific journalistic text is retained. |
| |
| ### 5. `summary` & `causal_chain` (The LLM Reasoning Layer) |
| - **Summary:** A concise, factual narrative of the event generated by Llama 3 70B. |
| - **Causal Chain:** Generated by Qwen 2.5 72B-Instruct. A normalized JSON array containing the chronological steps of the disaster. Each step includes: |
| - `type_event`: A semantically normalized category (e.g., *Flood*, *Casualties*, *Infrastructure Damage*); |
| - `description`: The specific manifestation of the impact; |
| - `supporting_quote`: An exact or fuzzy-matched quote from the retrieved news context, ensuring a **zero-hallucination rate** in the causal extraction; |
| - `token_usage`: Computational footprint tracking. |
|
|
| ## Methodology and Data Pipeline |
|
|
| The construction of this dataset followed a rigorous, multi-stage pipeline designed to eliminate LLM hallucinations and ensure physical consistency: |
|
|
| 1. **Event Anchoring:** Sourcing base events from EM-DAT (post-2014) and geocoding locations via Nominatim; |
| 2. **Multimodal Retrieval:** Automated fetching of historical weather data and satellite imagery footprints; |
| 3. **Textual Context Gathering:** Scraping, parsing, and algorithmically scoring news articles for relevance; |
| 4. **Causal Extraction:** Using Qwen-72B to extract causal graphs. A strict Python-based fuzzy-matching algorithm (threshold ≥ 0.80) was applied to cross-reference every LLM-generated quote against the raw news text. Any unverified causal step was systematically dropped; |
| 5. **Semantic Normalization:** Over 500 raw event types were algorithmically normalized into a clean taxonomy of ~390 standardized categories. |
|
|
| ## Bias, Risks, and Limitations |
|
|
| - **Media Coverage Bias:** The richness of the causal chains and the availability of news data are inherently biased toward events that received significant international or English-language media coverage. Events in remote areas may have shorter causal chains. |
| - **Satellite Data Availability:** Cloud cover heavily impacts the usability of Sentinel-2 optical imagery during severe weather events (e.g., hurricanes). Sentinel-1 SAR is provided to mitigate this. |
|
|
| ## Citation |
|
|
| If you use this dataset in your research, please cite our paper: |
|
|
| ```bibtex |
| Coming soon... |
