Detection of oil wells based on faster R-CNN in optical satellite remote sensing images

Abstract

The number and location of oil wells represent the status of oilfield development, which is important for policyholders considering their impact on energy resources planning. More importantly, petroleum production has a potential risk on the environment and public health due to its impact associated with local soil and water. With the advancement of satellite remote sensing and computer vision, there is emerging research interest in the area of object detection using optical remote sensing images. The detection of oil wells from remote sensing images remains an unexplored research area. Therefore, automatic detection of oil wells is explored in this paper and aims to help the policyholders with resources planning and environment monitoring. CNN (Convolutional Neural Network) based deep learning methods are able to learn distinctive high-level features efficiently, which address the challenges in the object detection in remote sensing. In this paper, we explore frameworks to automatically detect oil wells from the optical remote sensing images based on Faster R-CNN (Regional Convolutional Neural Network). In order to evaluate our methods, we have built a dataset of oil wells named NEPU-OWOD V1.0 (Northeast Petroleum University - Oil Well Object Detection Version 1.0) based on high-resolution remote sensing images from Google Earth Imagery. The experimental results show high precision up to 92.4%, which demonstrate that our methods can detect the oil wells from remote sensing images effectively.