Next day fire prediction via semantic segmentation
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929282922577920 |
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| author | Alexis, Konstantinos Girtsou, Stella Apostolakis, Alexis Giannopoulos, Giorgos Kontoes, Charalampos |
| author_facet | Alexis, Konstantinos Girtsou, Stella Apostolakis, Alexis Giannopoulos, Giorgos Kontoes, Charalampos |
| contents | In this paper we present a deep learning pipeline for next day fire prediction. The next day fire prediction task consists in learning models that receive as input the available information for an area up until a certain day, in order to predict the occurrence of fire for the next day. Starting from our previous problem formulation as a binary classification task on instances (daily snapshots of each area) represented by tabular feature vectors, we reformulate the problem as a semantic segmentation task on images; there, each pixel corresponds to a daily snapshot of an area, while its channels represent the formerly tabular training features. We demonstrate that this problem formulation, built within a thorough pipeline achieves state of the art results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_13545 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Next day fire prediction via semantic segmentation Alexis, Konstantinos Girtsou, Stella Apostolakis, Alexis Giannopoulos, Giorgos Kontoes, Charalampos Computer Vision and Pattern Recognition Machine Learning In this paper we present a deep learning pipeline for next day fire prediction. The next day fire prediction task consists in learning models that receive as input the available information for an area up until a certain day, in order to predict the occurrence of fire for the next day. Starting from our previous problem formulation as a binary classification task on instances (daily snapshots of each area) represented by tabular feature vectors, we reformulate the problem as a semantic segmentation task on images; there, each pixel corresponds to a daily snapshot of an area, while its channels represent the formerly tabular training features. We demonstrate that this problem formulation, built within a thorough pipeline achieves state of the art results. |
| title | Next day fire prediction via semantic segmentation |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2403.13545 |