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README.md
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# Hotspot disambiguation dataset
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This repository contains the dataset assembled as part of a work **A Multimodal Supervised Machine Learning Approach for Satellite-based Wildfire Identification in Europe**. Tha paper has been presented at the International Geoscience and Remote Sensing Symposium (**IGARSS**) 2023.
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The full paper is available at https://arxiv.org/abs/2308.02508 .
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The data folder contains two files:
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- `dataset.csv`: this file contains the full cross-referenced dataset, obtained by conducing a temporal and spatial data intersection between the EFFIS burned areas and the MODIS/VIIRS hotspots.
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- `dataset_500.csv`: this file contains a subset of the previous dataset (~500k data points), subsampled to obtain a dataset stratified with respect to the spatial distribution, and with a positive-negative proportion of 10%-90%. In addition to MODIS/VIIRS data points, additional columns have been added to improve the models' performances. This file is the one used to obtain the results showed in the paper.
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## Code
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The code and models used in this work are available at https://github.com/links-ads/hotspot-disambiguation .
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## Contributions
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- Angelica Urbanelli (angelica.urbanelli@linksfoundation.com)
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- Luca Barco (luca.barco@linksfoundation.com)
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- Edoardo Arnaudo (edoardo.arnaudo@polito.it | linksfoundation.com)
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- Claudio Rossi (claudio.rossi@linksfoundation.com)
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## BibTex
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```
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@inproceedings{urbanelli2023hotspot,
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title={A Multimodal Supervised Machine Learning Approach for Satellite-based Wildfire Identification in Europe},
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author={Urbanelli, Angelica and Barco, Luca and Arnaudo, Edoardo and Rossi, Claudio},
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booktitle={2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS},
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year={2023}
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}
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```
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## Licence
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cc-by-4.0
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## Acknowledgments
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This work was carried out in the context of two H2020 projects: SAFERS (GA n.869353) and OVERWATCH (GA n.101082320), and presented at IGARSS 2023.
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