# Rapid Wildfire Hotspot Detection Using Self-Supervised Learning on Temporal Remote Sensing Data

arXiv: https://arxiv.org/abs/2405.20093 · HTML: https://mlsanity.com/paper/2405.20093/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 11 references that are in the map.
- First posted on arXiv 30 May 2024.

## Builds on (most cited first, 5 of 5)

- [Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning](https://mlsanity.com/paper/2212.14532/) (2022)
- [Lightweight, Pre-trained Transformers for Remote Sensing Timeseries](https://mlsanity.com/paper/2304.14065/) (2023)
- [SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation](https://mlsanity.com/paper/2211.07044/) (2022)
- [Multimodal contrastive learning for remote sensing tasks](https://mlsanity.com/paper/2209.02329/) (2022)
- [A Multimodal Supervised Machine Learning Approach for Satellite-based Wildfire Identification in Europe](https://mlsanity.com/paper/2308.02508/) (2023)

## Abstract (arXiv)

Rapid detection and well-timed intervention are essential to mitigate the impacts of wildfires. Leveraging remote sensed data from satellite networks and advanced AI models to automatically detect hotspots (i.e., thermal anomalies caused by active fires) is an effective way to build wildfire monitoring systems. In this work, we propose a novel dataset containing time series of remotely sensed data related to European fire events and a Self-Supervised Learning (SSL)-based model able to analyse multi-temporal data and identify hotspots in potentially near real time. We train and evaluate the performance of our model using our dataset and Thraws, a dataset of thermal anomalies including several fire events, obtaining an F1 score of 63.58.