Towards Large Scale Geostatistical Methane Monitoring with Part-based Object Detection

TL;DR

This paper presents a large-scale geostatistical methane monitoring approach using part-based object detection on remote sensing imagery, focusing on bio-digesters in France to estimate methane emissions.

Contribution

It introduces a novel bio-digester dataset and a part-based detection method to improve identification of rare objects in large geographic areas.

Findings

Successful detection of bio-digesters in new regions

Accurate geostatistical estimates of methane emissions

Enhanced detection performance through sub-element analysis

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.