Towards Large Scale Geostatistical Methane Monitoring with Part-based Object Detection
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909703837057024 |
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| author | de Senneville, Adhemar Bou, Xavier Ehret, Thibaud Grompone, Rafael Bonne, Jean Louis Dumelie, Nicolas Lauvaux, Thomas Facciolo, Gabriele |
| author_facet | de Senneville, Adhemar Bou, Xavier Ehret, Thibaud Grompone, Rafael Bonne, Jean Louis Dumelie, Nicolas Lauvaux, Thomas Facciolo, Gabriele |
| contents | Object detection is one of the main applications of computer vision in remote sensing imagery. Despite its increasing availability, the sheer volume of remote sensing data poses a challenge when detecting rare objects across large geographic areas. Paradoxically, this common challenge is crucial to many applications, such as estimating environmental impact of certain human activities at scale. In this paper, we propose to address the problem by investigating the methane production and emissions of bio-digesters in France. We first introduce a novel dataset containing bio-digesters, with small training and validation sets, and a large test set with a high imbalance towards observations without objects since such sites are rare. We develop a part-based method that considers essential bio-digester sub-elements to boost initial detections. To this end, we apply our method to new, unseen regions to build an inventory of bio-digesters. We then compute geostatistical estimates of the quantity of methane produced that can be attributed to these infrastructures in a given area at a given time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_18513 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Large Scale Geostatistical Methane Monitoring with Part-based Object Detection de Senneville, Adhemar Bou, Xavier Ehret, Thibaud Grompone, Rafael Bonne, Jean Louis Dumelie, Nicolas Lauvaux, Thomas Facciolo, Gabriele Computer Vision and Pattern Recognition Object detection is one of the main applications of computer vision in remote sensing imagery. Despite its increasing availability, the sheer volume of remote sensing data poses a challenge when detecting rare objects across large geographic areas. Paradoxically, this common challenge is crucial to many applications, such as estimating environmental impact of certain human activities at scale. In this paper, we propose to address the problem by investigating the methane production and emissions of bio-digesters in France. We first introduce a novel dataset containing bio-digesters, with small training and validation sets, and a large test set with a high imbalance towards observations without objects since such sites are rare. We develop a part-based method that considers essential bio-digester sub-elements to boost initial detections. To this end, we apply our method to new, unseen regions to build an inventory of bio-digesters. We then compute geostatistical estimates of the quantity of methane produced that can be attributed to these infrastructures in a given area at a given time. |
| title | Towards Large Scale Geostatistical Methane Monitoring with Part-based Object Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.18513 |