A timely and accurate approach to nearshore oil spill monitoring using deep learning and GIS

• Published in: The Science of the total environment Vol. 912; p. 169500
• Main Authors: Lau, Tsz-Kin, Huang, Kai-Hsiang
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 20.02.2024
• Subjects: Deep learning; environment; environmental protection; Geographic information system; geographic information systems; marine pollution; Nearshore monitoring; neural networks; Oil spill detection; oil spills; Radar imaging; Deep learning; Radar imaging; Nearshore monitoring; Oil spill detection; Geographic information system
• ISSN: 0048-9697; 1879-1026; 1879-1026
• DOI: 10.1016/j.scitotenv.2023.169500

Oil spill accidents are a key contributor to marine pollution worldwide. Therefore, timely and effective oil spill detection is crucial for reducing marine pollution and enhancing environmental protection. Against this backdrop, this study explored two methods for performing nearshore on-site oil spill detection and segmentation, namely the U-net and Mask region-based convolutional neural network (R-CNN) methods. The U-net and Mask R-CNN models were revealed to exhibit acceptable and favorable performance, achieving overall accuracy of 77.01 % and 89.02 %, respectively. Subsequently, a verification system based on the Geographic Information System (GIS) was developed to improve the performance of the deep-learning model. With the integration of the verification system, the Mask R-CNN model achieved higher overall accuracy of 90.78 %. The feasibility of applying deep-learning methods to nearshore on-site oil spill monitoring was demonstrated through this study. In addition, the integration of the GIS not only assisted in the provision of oil spill information but also in the improvement of the deep-learning models. The timely, accurate, and effective method for nearshore on-site oil spill monitoring that this study explored can be applied to considerably improve traditional on-site oil spill monitoring, which has received limited academic attention in the last two decades. [Display omitted] •Well-trained Deep Learning models were used to help in nearshore oil spill monitoring.•Drawbacks in traditional oil spill monitoring were solved through DL and GIS.•The importance of temporal changes in oil spill detection was determined.