Oil and Gas Automatic Infrastructure Mapping: Leveraging High-Resolution Satellite Imagery Through Fine-Tuning of Object Detection Models

The oil and gas sector is the second largest anthropogenic emitter of methane, which is responsible for at least 25% of current global warming. To curb methane’s contribution to climate change, emissions behavior from oil and gas infrastructure must be determined by an automated monitoring across the globe. This requires, as first step, an efficient solution to automatically detect and identify these infrastructures. In this extended study, we focus on automated identification of oil and gas infrastructure by using and comparing two types of advanced supervised object detection algorithms: Region-based Object Detector (YOLO and FASTER-RCNN) and Transformer-based Object Detector (DETR) with fine-tuning on our customized high-resolution satellite image database (Permian Basin U.S). The pre-training effect of each of these algorithms on detection results is studied and compared with non-pre-trained algorithms. The performed experiments demonstrate the general effectiveness of pre-trained YOLO v8 model with a Mean Average Precision over 90. The non-pre-trained model of this last one also over perform compare to FASTER-RCNN and DETR.