Rapid Wildfire Hotspot Detection Using Self-Supervised Learning on Temporal Remote Sensing Data
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917679422504960 |
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| author | Barco, Luca Urbanelli, Angelica Rossi, Claudio |
| author_facet | Barco, Luca Urbanelli, Angelica Rossi, Claudio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_20093 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Rapid Wildfire Hotspot Detection Using Self-Supervised Learning on Temporal Remote Sensing Data Barco, Luca Urbanelli, Angelica Rossi, Claudio Computer Vision and Pattern Recognition 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. |
| title | Rapid Wildfire Hotspot Detection Using Self-Supervised Learning on Temporal Remote Sensing Data |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.20093 |