{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T00:10:36Z","timestamp":1768522236475,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":40,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819981472","type":"print"},{"value":"9789819981489","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T00:00:00Z","timestamp":1700956800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T00:00:00Z","timestamp":1700956800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-99-8148-9_35","type":"book-chapter","created":{"date-parts":[[2023,11,25]],"date-time":"2023-11-25T10:02:23Z","timestamp":1700906543000},"page":"442-458","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Oil and\u00a0Gas Automatic Infrastructure Mapping: Leveraging High-Resolution Satellite Imagery Through Fine-Tuning of\u00a0Object Detection Models"],"prefix":"10.1007","author":[{"given":"Jade Eva","family":"Guisiano","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"\u00c9ric","family":"Moulines","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Lauvaux","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J\u00e9r\u00e9mie","family":"Sublime","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,26]]},"reference":[{"key":"35_CR1","unstructured":"IPCC. https:\/\/www.ipcc.ch\/report\/sixth-assessment-report-working-group-3"},{"key":"35_CR2","unstructured":"Sheng, H., et al.: OGNet: towards a global oil and gas infrastructure database using deep learning on remotely sensed imagery. arXiv preprint arXiv:2011.07227 (2020)"},{"key":"35_CR3","unstructured":"Zhu, B., et al.: METER-ML: a multi-sensor earth observation benchmark for automated methane source mapping. arXiv preprint arXiv:2207.11166, 2022"},{"key":"35_CR4","doi-asserted-by":"crossref","unstructured":"Lindeberg, T.: Scale Invariant feature transform 7, 05 (2012)","DOI":"10.4249\/scholarpedia.10491"},{"key":"35_CR5","doi-asserted-by":"crossref","unstructured":"Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886\u2013893 (2005)","DOI":"10.1109\/CVPR.2005.177"},{"key":"35_CR6","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.patrec.2020.07.042","volume":"141","author":"P Wang","year":"2021","unstructured":"Wang, P., Fan, E., Wang, P.: Comparative analysis of image classification algorithms based on traditional machine learning and deep learning. Pattern Recogn. Lett. 141, 61\u201367 (2021)","journal-title":"Pattern Recogn. Lett."},{"issue":"4","key":"35_CR7","first-page":"4071","volume":"45","author":"G Huang","year":"2022","unstructured":"Huang, G., Laradji, I., Vazquez, D., Lacoste-Julien, S., Rodriguez, P.: A survey of self-supervised and few-shot object detection. IEEE Trans. Pattern Anal. Mach. Intell. 45(4), 4071\u20134089 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"35_CR8","doi-asserted-by":"publisher","first-page":"23729","DOI":"10.1007\/s11042-020-08976-6","volume":"79","author":"Y Xiao","year":"2020","unstructured":"Xiao, Y., et al.: A review of object detection based on deep learning. Multimedia Tools Appl. 79, 23729\u201323791 (2020)","journal-title":"Multimedia Tools Appl."},{"key":"35_CR9","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"35_CR10","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440\u20131448 (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"35_CR11","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, vol. 28 (2015)"},{"key":"35_CR12","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection, pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"35_CR13","unstructured":"Terven, J., Cordova-Esparza, D.-M.: A comprehensive review of yolo: from yolov1 to yolov8 and beyond (2023)"},{"key":"35_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/978-3-319-46448-0_2","volume-title":"Computer Vision \u2013 ECCV 2016","author":"W Liu","year":"2016","unstructured":"Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21\u201337. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2"},{"key":"35_CR15","doi-asserted-by":"crossref","unstructured":"Lin, T. Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"35_CR16","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1186\/s12911-021-01691-8","volume":"21","author":"L Tan","year":"2021","unstructured":"Tan, L., Huangfu, T., Liyao, W., Chen, W.: Comparison of RetinaNet, SSD, and YOLO v3 for real-time pill identification. BMC Med. Inform. Decis. Mak. 21, 11 (2021)","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"35_CR17","doi-asserted-by":"crossref","unstructured":"Dos Santos, D.F., Fran\u00e7ani, A.O., Maximo, M.R., Ferreira, A.S.: Performance comparison of convolutional neural network models for object detection in tethered balloon imagery. In: 2021 Latin American Robotics Symposium (LARS), 2021 Brazilian Symposium on Robotics (SBR), and 2021 Workshop on Robotics in Education (WRE), pp. 246\u2013251 (2021)","DOI":"10.1109\/LARS\/SBR\/WRE54079.2021.9605459"},{"issue":"9","key":"35_CR18","doi-asserted-by":"publisher","first-page":"5810","DOI":"10.3390\/app13095810","volume":"13","author":"M Jakubec","year":"2023","unstructured":"Jakubec, M., Lieskovsk\u00e1, E., Bu\u010dko, B., Z\u00e1bovsk\u00e1, K.: Comparison of CNN-based models for pothole detection in real-world adverse conditions: overview and evaluation. Appl. Sci. 13(9), 5810 (2023)","journal-title":"Appl. Sci."},{"key":"35_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/978-3-030-58452-8_13","volume-title":"Computer Vision \u2013 ECCV 2020","author":"N Carion","year":"2020","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 213\u2013229. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_13"},{"key":"35_CR20","series-title":"Lecture Notes in Electrical Engineering","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1007\/978-981-16-9012-9_18","volume-title":"Sustainable Advanced Computing","author":"K Bhil","year":"2022","unstructured":"Bhil, K., et al.: Recent progress in object detection in satellite imagery: a review. In: Aurelia, S., Hiremath, S.S., Subramanian, K., Biswas, S.K. (eds.) Sustainable Advanced Computing. LNEE, vol. 840, pp. 209\u2013218. Springer, Singapore (2022). https:\/\/doi.org\/10.1007\/978-981-16-9012-9_18"},{"key":"35_CR21","doi-asserted-by":"publisher","first-page":"116793","DOI":"10.1016\/j.eswa.2022.116793","volume":"197","author":"Y Wang","year":"2022","unstructured":"Wang, Y., Bashir, S.M.A., Khan, M., Ullah, Q., Wang, R., Song, Y., Guo, Z., Niu, Y.: Remote sensing image super-resolution and object detection: benchmark and state of the art. Expert Syst. Appl. 197, 116793 (2022)","journal-title":"Expert Syst. Appl."},{"issue":"10","key":"35_CR22","doi-asserted-by":"publisher","first-page":"2385","DOI":"10.3390\/rs14102385","volume":"14","author":"Z Li","year":"2022","unstructured":"Li, Z., et al.: Deep learning-based object detection techniques for remote sensing images: a survey. Remote Sens. 14(10), 2385 (2022)","journal-title":"Remote Sens."},{"key":"35_CR23","doi-asserted-by":"publisher","first-page":"20118","DOI":"10.1109\/ACCESS.2022.3149052","volume":"10","author":"J Kang","year":"2022","unstructured":"Kang, J., Tariq, S., Han, O., Woo, S.S.: A survey of deep learning-based object detection methods and datasets for overhead imagery. IEEE Access 10, 20118\u201320134 (2022)","journal-title":"IEEE Access"},{"issue":"17","key":"35_CR24","doi-asserted-by":"publisher","first-page":"4938","DOI":"10.3390\/s20174938","volume":"20","author":"M Li","year":"2020","unstructured":"Li, M., Zhang, Z., Lei, L., Wang, X., Guo, X.: Agricultural greenhouses detection in high-resolution satellite images based on convolutional neural networks: Comparison of faster R-CNN, YOLO v3 and SSD. Sensors 20(17), 4938 (2020)","journal-title":"Sensors"},{"key":"35_CR25","doi-asserted-by":"publisher","first-page":"383","DOI":"10.1007\/s11069-020-04315-y","volume":"105","author":"K Hac\u0131efendio\u011flu","year":"2021","unstructured":"Hac\u0131efendio\u011flu, K., Ba\u015fa\u011fa, H.B., Demir, G.: Automatic detection of earthquake-induced ground failure effects through faster R-CNN deep learning-based object detection using satellite images. Nat. Hazards 105, 383\u2013403 (2021)","journal-title":"Nat. Hazards"},{"key":"35_CR26","unstructured":"Demidov, D., Grandhe, R., Almarri, S.: Object detection in aerial imagery (2022)"},{"issue":"4","key":"35_CR27","doi-asserted-by":"publisher","first-page":"984","DOI":"10.3390\/rs14040984","volume":"14","author":"Q Li","year":"2022","unstructured":"Li, Q., Chen, Y., Zeng, Y.: Transformer with transfer CNN for remote-sensing-image object detection. Remote Sens. 14(4), 984 (2022)","journal-title":"Remote Sens."},{"issue":"3","key":"35_CR28","doi-asserted-by":"publisher","first-page":"1147","DOI":"10.3390\/s22031147","volume":"22","author":"A Tahir","year":"2022","unstructured":"Tahir, A.: Automatic target detection from satellite imagery using machine learning. Sensors 22(3), 1147 (2022)","journal-title":"Sensors"},{"key":"35_CR29","doi-asserted-by":"crossref","unstructured":"Zhu, J., Chen, X., Zhang, H., Tan, Z., Wang, S., Ma, H.: Transformer based remote sensing object detection with enhanced multispectral feature extraction. IEEE Geosci. Remote Sens. Lett. 1\u20131 (2023)","DOI":"10.1109\/LGRS.2023.3276052"},{"issue":"11","key":"35_CR30","doi-asserted-by":"publisher","first-page":"4287","DOI":"10.1080\/01431161.2022.2109445","volume":"43","author":"Y-J Yang","year":"2022","unstructured":"Yang, Y.-J., Singha, S., Mayerle, R.: A deep learning based oil spill detector using sentinel-1 SAR imagery. Int. J. Remote Sens. 43(11), 4287\u20134314 (2022)","journal-title":"Int. J. Remote Sens."},{"key":"35_CR31","doi-asserted-by":"crossref","unstructured":"Yang, Y.J., Singha, S., Goldman, R.: An automatic oil spill detection and early warning system in the Southeastern Mediterranean Sea. In: EGU General Assembly Conference Abstracts, EGU General Assembly Conference Abstracts, pp. EGU22-8408 (2022)","DOI":"10.5194\/egusphere-egu22-8408"},{"issue":"10","key":"35_CR32","doi-asserted-by":"publisher","first-page":"4895","DOI":"10.1109\/JSTARS.2015.2467377","volume":"8","author":"L Zhang","year":"2015","unstructured":"Zhang, L., Shi, Z., Jun, W.: A hierarchical oil tank detector with deep surrounding features for high-resolution optical satellite imagery. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 8(10), 4895\u20134909 (2015)","journal-title":"IEEE J. Sel. Top. Appl. Earth Observations Remote Sens."},{"issue":"16","key":"35_CR33","doi-asserted-by":"publisher","first-page":"3243","DOI":"10.3390\/rs13163243","volume":"13","author":"P Shi","year":"2021","unstructured":"Shi, P., et al.: Oil well detection via large-scale and high-resolution remote sensing images based on improved YOLO v4. Remote Sens. 13(16), 3243 (2021)","journal-title":"Remote Sens."},{"key":"35_CR34","doi-asserted-by":"crossref","unstructured":"Song, G., Wang, Z., Bai, L., Zhang, J., Chen, L.: Detection of oil wells based on faster R-CNN in optical satellite remote sensing images. In: Image and Signal Processing for Remote Sensing XXVI, vol. 11533, pp. 14\u2013121. SPIE (2020)","DOI":"10.1117\/12.2572996"},{"issue":"6","key":"35_CR35","doi-asserted-by":"publisher","first-page":"1132","DOI":"10.3390\/rs13061132","volume":"13","author":"L Zhibao Wang","year":"2021","unstructured":"Zhibao Wang, L., et al.: An oil well dataset derived from satellite-based remote sensing. Remote Sens. 13(6), 1132 (2021)","journal-title":"Remote Sens."},{"issue":"1","key":"35_CR36","doi-asserted-by":"publisher","first-page":"2146853","DOI":"10.1080\/08839514.2022.2146853","volume":"36","author":"B Ga\u0161parovi\u0107","year":"2022","unstructured":"Ga\u0161parovi\u0107, B., Lerga, J., Mau\u0161a, G., Iva\u0161i\u0107-Kos, M.: Deep learning approach for objects detection in underwater pipeline images. Appl. Artif. Intell. 36(1), 2146853 (2022)","journal-title":"Appl. Artif. Intell."},{"key":"35_CR37","doi-asserted-by":"crossref","unstructured":"Zhang, N.: et al.: Automatic recognition of oil industry facilities based on deep learning. In: IGARSS 2018\u20132018 IEEE International Geoscience and Remote Sensing Symposium, pp. 2519\u20132522 (2018)","DOI":"10.1109\/IGARSS.2018.8518054"},{"key":"35_CR38","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"35_CR39","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"issue":"2","key":"35_CR40","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The Pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303\u2013338 (2010)","journal-title":"Int. J. Comput. Vis."}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8148-9_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T09:38:19Z","timestamp":1730626699000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8148-9_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,26]]},"ISBN":["9789819981472","9789819981489"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8148-9_35","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,26]]},"assertion":[{"value":"26 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1274","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"650","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"51% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.14","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.46","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}