@inproceedings{1bd05c88ecf44e5c8484c30fd23058e5,
title = "Spatial information extraction of oil well sites based on medium-resolution satellite imagery",
abstract = "The oil extraction process has cumulative detrimental impacts on the environment. However, in the process of oil mining, a large number of petroleum-based pollutants cause severe effect to soil and groundwater, which poses a serious risk to the ecological environment and human health. Understanding the distribution of oil well sites, is of vital importance to sustainable mining development. Efficient mapping these sites require automated identification and extraction of the oil well sites from satellite images. With the development of remote sensing satellite technology and the wide application of deep learning-based algorithms, it has become possible to automatically extract oil well sites from remote sensing images. However, there is lack of usage of Sentinel-2 satellite data to explore the efficacy in oil well sites detection. Therefore, we conducted this work to explore the feasibility of detecting the oil well sites with semantic segmentation from Sentinel-2 imagery. In this work, we established the Northeast Petroleum University Oil Well Sites Version 2.0 (NEPU-OWS V2.0) with spatial coverage spanning the Austin region of United States. We then validate the usability and effectiveness of the dataset using semantic segmentation models based on DANet and Swin-Unet, which are more capable of recognizing small targets. Our experimental results show that both models have great potential for remote sensing detection in the medium sized oil well sites and the Swin-Unet model achieved a better performance for the detection of oil well sites with a MIoU of 77.53%.",
author = "Hao Wu and Hongli Dong and Zhibao Wang and Lu Bai and Fengcai Huo and Jinhua Tao and Liangfu Chen",
year = "2023",
month = oct,
day = "19",
doi = "10.1117/12.2680249",
language = "English",
isbn = "9781510666955",
series = "Proceedings of SPIE",
publisher = "SPIE - The International Society for Optical Engineering",
editor = "Lorenzo Bruzzone and Francesca Bovolo",
booktitle = "Image and Signal Processing for Remote Sensing XXIX: proceedings",
address = "United States",
note = "Image and Signal Processing for Remote Sensing XXIX 2023 ; Conference date: 03-09-2023 Through 07-09-2023",
}