文献类型：期刊文献

英文题名：Automatic error correction: Improving annotation quality for model optimization in oil-exploration related land disturbances mapping

作者：Cai, Yuwei[1,2];Hu, Bingxu[2];He, Hongjie[2];Gao, Kyle[3];Xu, Hongzhang[2];Zhang, Ying[4];Pirasteh, Saied[1,5];Wang, Xiuqing[1];Chen, Wenping[1];Li, Huxiong[1]

机构：[1]Shaoxing Univ, Inst Artificial Intelligence, Sch Mech & Elect Engn, Shaoxing 312000, Peoples R China;[2]Univ Waterloo, Dept Geog & Environm Management, Waterloo, ON N2L 3G1, Canada;[3]Univ Waterloo, Dept Syst Design Engn, Waterloo, ON N2L 3G1, Canada;[4]Nat Resources Canada, Canada Ctr Remote Sensing, Ottawa, ON K1A 0Y7, Canada;[5]Saveetha Inst Med & Tech Sci, Dept Geotech & Geomat, Saveetha Sch Engn, Chennai 602105, Tamil Nadu, India

年份：2024

卷号：27

期号：1

起止页码：108

外文期刊名：EGYPTIAN JOURNAL OF REMOTE SENSING AND SPACE SCIENCES

收录：SCI-EXPANDED(收录号：WOS:001178934800001)、、EI(收录号：20240715538250)、Scopus(收录号：2-s2.0-85184510354)、WOS

语种：英文

外文关键词：Deep -learning; Object -extraction; Land disturbance; Segmentation; Automatic error correction

外文摘要：The manual extraction of land disturbances associated with oil exploration, which normally includes resource roads, mining facilities, and well pads, presents significant challenges in terms of cost and time. Accurate monitoring and mapping of land disturbances resulting from oil exploration plays a crucial role in conducting comprehensive environmental assessments and facilitating effective land reclamation initiatives. However, prevailing deep learning methodologies in the realm of oil and gas exploration primarily focus on oil spill detection, neglecting the critical aspect of land disturbances resulting from oil exploration, thus overlooking the impact on land. Furthermore, given that the well sites are scattered and relatively diminutive compared to other land covers, their detection poses substantial difficulties. This paper proposes an automatic error-correcting (AEC) algorithm to address deficiencies in ground truth data quality. This AEC method was integrated into the deep-learning framework for land disturbance extraction, specifically tailored for land disturbances analysis associated with oil exploration. The efficacy of our method was validated on a dataset collected in Alberta covering an area of oil sand mining sites. The application of the AEC algorithm significantly enhanced the accuracy of land disturbance analysis, thereby contributing to a more effective hydrocarbon exploration impact analysis and facilitating the timely planning by the Alberta government. The results demonstrate notable improvements in both average pixel accuracy (AA) and mean intersection over union (mIoU), ranging from 8.3% to 15.4% and 0.5% to 5.8%, respectively. These enhancements, which have profound implications for the precision of land disturbance detection, prove that the proposed AEC algorithm can serve a dual purpose: correcting errors in the dataset and efficiently detecting land disturbance features in the oil exploration area.