用部件检测法追踪法国沼气池，实现大规模甲烷排放监测。
Towards Large Scale Geostatistical Methane Monitoring with Part-based Object Detection
- 基于沼气池关键部件设计检测方法，提升稀有目标识别率。
- 在未见区域检测出数千个沼气池，支持大范围甲烷排放估算。
- 适合环境监测、碳排放评估研究者使用。
目标检测是遥感图像中计算机视觉的主要应用之一。尽管数据日益丰富，海量遥感数据在大范围地理区域内检测稀有目标时仍面临挑战。这一难题对评估人类活动的环境影响至关重要。本文以法国沼气池的甲烷产生与排放为研究对象，提出一种新数据集，包含少量训练/验证样本和大量测试样本，且正负样本严重失衡（无目标样本占多数）。我们开发了一种基于部件的方法，利用沼气池的关键子结构提升初始检测性能。将该方法应用于新区域后，构建了沼气池的分布清单，并进一步计算特定区域内特定时间的甲烷产量地理统计估计值。
原文摘要 · Abstract (English)
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.
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