# Towards Large Scale Geostatistical Methane Monitoring with Part-based Object Detection Source: [Towards Large Scale Geostatistical Methane Monitoring with Part-based Object Detection]() Collection index: [llms.txt]() Data updated: 2025-10-26T05:15:13.470Z Authors: Adhemar de Senneville, Xavier Bou, Thibaud Ehret, Rafael Grompone, Jean Louis Bonne, Nicolas Dumelie, Thomas Lauvaux, Gabriele Facciolo Published: 2025-07-24T15:33:55.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()