# Mesogeos: A multi-purpose dataset for data-driven wildfire modeling in the Mediterranean

arXiv: https://arxiv.org/abs/2306.05144 · HTML: https://mlsanity.com/paper/2306.05144/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 77 references that are in the map.
- First posted on arXiv 8 Jun 2023.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [A Cookbook of Self-Supervised Learning](https://mlsanity.com/paper/2304.12210/) (2023)
- [Towards Causal Representation Learning](https://mlsanity.com/paper/2102.11107/) (2021)
- [Explainable Artificial Intelligence: a Systematic Review](https://mlsanity.com/paper/2006.00093/) (2020)
- [A review of machine learning applications in wildfire science and management](https://mlsanity.com/paper/2003.00646/) (2020)
- [Gated Transformer Networks for Multivariate Time Series Classification](https://mlsanity.com/paper/2103.14438/) (2021)
- [Next Day Wildfire Spread: A Machine Learning Data Set to Predict Wildfire Spreading from Remote-Sensing Data](https://mlsanity.com/paper/2112.02447/) (2021)
- [A Survey on Deep Learning with Noisy Labels: How to train your model when you cannot trust on the annotations?](https://mlsanity.com/paper/2012.03061/) (2020)
- [Deep Learning Methods for Daily Wildfire Danger Forecasting](https://mlsanity.com/paper/2111.02736/) (2021)
- [Deep Learning Models for Predicting Wildfires from Historical Remote-Sensing Data](https://mlsanity.com/paper/2010.07445/) (2020)
- [Convolutional LSTM Neural Networks for Modeling Wildland Fire Dynamics](https://mlsanity.com/paper/2012.06679/) (2020)
- [Deep Learning for Global Wildfire Forecasting](https://mlsanity.com/paper/2211.00534/) (2022)
- [Uncertainty Aware Wildfire Management](https://mlsanity.com/paper/2010.07915/) (2020)

## Built on by (most cited first, 19 of 19)

- [CLLMate: A Multimodal Benchmark for Weather and Climate Events Forecasting](https://mlsanity.com/paper/2409.19058/) (2024)
- [Kuro Siwo: 33 billion m2 under the water. A global multi-temporal satellite dataset for rapid flood mapping](https://mlsanity.com/paper/2311.12056/) (2023)
- [Probabilistic Machine Learning for Noisy Labels in Earth Observation](https://mlsanity.com/paper/2504.03478/) (2025)
- [SeasFire as a Multivariate Earth System Datacube for Wildfire Dynamics](https://mlsanity.com/paper/2312.07199/) (2023)
- [TS-SatFire: A Multi-Task Satellite Image Time-Series Dataset for Wildfire Detection and Prediction](https://mlsanity.com/paper/2412.11555/) (2024)
- [Uncertainty-Aware Deep Learning for Wildfire Danger Forecasting](https://mlsanity.com/paper/2509.25017/) (2025)
- [Localized Forest Fire Risk Prediction: A Department-Aware Approach for Operational Decision Support](https://mlsanity.com/paper/2506.04254/) (2025)
- [From Black Box to Insight: Explainable AI for Extreme Event Preparedness](https://mlsanity.com/paper/2511.13712/) (2025)
- [BCWildfire: A Long-term Multi-factor Dataset and Deep Learning Benchmark for Boreal Wildfire Risk Prediction](https://mlsanity.com/paper/2511.17597/) (2025)
- [IberFire -- a detailed creation of a spatio-temporal dataset for wildfire risk assessment in Spain](https://mlsanity.com/paper/2505.00837/) (2025)
- [Wildfire spread forecasting with Deep Learning](https://mlsanity.com/paper/2505.17556/) (2025)
- [Set Prediction for Next-Day Active Fire Forecasting](https://mlsanity.com/paper/2605.10298/) (2026)
- [Set2Seq Transformer: Temporal and Position-Aware Set Representations for Sequential Multiple-Instance Learning](https://mlsanity.com/paper/2408.03404/) (2024)
- [MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting](https://mlsanity.com/paper/2505.10281/) (2025)
- [Trustworthy Data-Driven Wildfire Risk Prediction and Understanding in Western Canada](https://mlsanity.com/paper/2601.01677/) (2026)
- [OSMGraphCLIP: Learning Global Location Representations from OpenStreetMap Graphs](https://mlsanity.com/paper/2606.08046/) (2026)
- [Data-Driven Fire-Zone Segmentation for Improved Short-Term Wildfire Prediction](https://mlsanity.com/paper/2608.07472/) (2026)
- [PyroStack: A Multi-Band Spatio-Temporal Sub-Daily Dataset for Wildfires in the United States](https://mlsanity.com/paper/2609.36315/) (2026)
- [FireGraphNet: Sub-daily wildfire spread prediction with directed graph neural networks](https://mlsanity.com/paper/2610.19191/) (2026)

## Abstract (arXiv)

We introduce Mesogeos, a large-scale multi-purpose dataset for wildfire modeling in the Mediterranean. Mesogeos integrates variables representing wildfire drivers (meteorology, vegetation, human activity) and historical records of wildfire ignitions and burned areas for 17 years (2006-2022). It is designed as a cloud-friendly spatio-temporal dataset, namely a datacube, harmonizing all variables in a grid of 1km x 1km x 1-day resolution. The datacube structure offers opportunities to assess machine learning (ML) usage in various wildfire modeling tasks. We extract two ML-ready datasets that establish distinct tracks to demonstrate this potential: (1) short-term wildfire danger forecasting and (2) final burned area estimation given the point of ignition. We define appropriate metrics and baselines to evaluate the performance of models in each track. By publishing the datacube, along with the code to create the ML datasets and models, we encourage the community to foster the implementation of additional tracks for mitigating the increasing threat of wildfires in the Mediterranean.