An Oil Well Dataset Derived from Satellite-Based Remote Sensing

Estimation of the number and geo-location of oil wells is important for policy holders considering their impact on energy resource planning. With the recent development in optical remote sensing, it is possible to identify oil wells from satellite images. Moreover, the recent advancement in deep learning frameworks for object detection in remote sensing makes it possible to automatically detect oil wells from remote sensing images. In this paper, we collected a dataset named Northeast Petroleum University-Oil Well Object Detection Version 1.0 (NEPU-OWOD V1.0) based on high-resolution remote sensing images from Google Earth Imagery. Our database includes 1192 oil wells in 432 images from Daqing City, which has the largest oilfield in China. In this study, we compared nine different state-of-the-art deep learning models based on algorithms for object detection from optical remote sensing images. Experimental results show that the state-of-the-art deep learning models achieve high precision on our collected dataset, which demonstrate the great potential for oil well detection in remote sensing.

Automated summary

This study explores the automatic detection of oil wells using optical remote sensing and deep learning technologies. Experimental comparisons on the NEPU-OWOD V1.0 dataset validate the high precision performance of state-of-the-art deep learning models in this area, demonstrating the great potential for oil well detection in remote sensing.