{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T03:01:40Z","timestamp":1780542100897,"version":"3.54.1"},"reference-count":49,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,5]],"date-time":"2023-01-05T00:00:00Z","timestamp":1672876800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2022JCCXDC01"],"award-info":[{"award-number":["2022JCCXDC01"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2020QN07"],"award-info":[{"award-number":["2020QN07"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["454-0601-YBN-DONH"],"award-info":[{"award-number":["454-0601-YBN-DONH"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["454-0601-YBN-YNA6"],"award-info":[{"award-number":["454-0601-YBN-YNA6"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Yueqi Young Scholar of China University of Mining and Technology (Beijing)","award":["2022JCCXDC01"],"award-info":[{"award-number":["2022JCCXDC01"]}]},{"name":"Yueqi Young Scholar of China University of Mining and Technology (Beijing)","award":["2020QN07"],"award-info":[{"award-number":["2020QN07"]}]},{"name":"Yueqi Young Scholar of China University of Mining and Technology (Beijing)","award":["454-0601-YBN-DONH"],"award-info":[{"award-number":["454-0601-YBN-DONH"]}]},{"name":"Yueqi Young Scholar of China University of Mining and Technology (Beijing)","award":["454-0601-YBN-YNA6"],"award-info":[{"award-number":["454-0601-YBN-YNA6"]}]},{"name":"Geological Research Project of the Hebei Bureau of Geology and Mineral Resources","award":["2022JCCXDC01"],"award-info":[{"award-number":["2022JCCXDC01"]}]},{"name":"Geological Research Project of the Hebei Bureau of Geology and Mineral Resources","award":["2020QN07"],"award-info":[{"award-number":["2020QN07"]}]},{"name":"Geological Research Project of the Hebei Bureau of Geology and Mineral Resources","award":["454-0601-YBN-DONH"],"award-info":[{"award-number":["454-0601-YBN-DONH"]}]},{"name":"Geological Research Project of the Hebei Bureau of Geology and Mineral Resources","award":["454-0601-YBN-YNA6"],"award-info":[{"award-number":["454-0601-YBN-YNA6"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In the process of extracting tailings ponds from large scene remote sensing images, semantic segmentation models usually perform calculations on all small-size remote sensing images segmented by the sliding window method. However, some of these small-size remote sensing images do not have tailings ponds, and their calculations not only affect the model accuracy, but also affect the model speed. For this problem, we proposed a fast tailings pond extraction method (Scene-Classification-Sematic-Segmentation, SC-SS) that couples scene classification and semantic segmentation models. The method can map tailings ponds rapidly and accurately in large scene remote sensing images. There were two parts in the method: a scene classification model, and a semantic segmentation model. Among them, the scene classification model adopted the lightweight network MobileNetv2. With the help of this network, the scenes containing tailings ponds can be quickly screened out from the large scene remote sensing images, and the interference of scenes without tailings ponds can be reduced. The semantic segmentation model used the U-Net model to finely segment objects from the tailings pond scenes. In addition, the encoder of the U-Net model was replaced by the VGG16 network with stronger feature extraction ability, which improves the model\u2019s accuracy. In this paper, the Google Earth images of Luanping County were used to create the tailings pond scene classification dataset and tailings pond semantic segmentation dataset, and based on these datasets, the training and testing of models were completed. According to the experimental results, the extraction accuracy (Intersection Over Union, IOU) of the SC-SS model was 93.48%. The extraction accuracy of IOU was 15.12% higher than the U-Net model, while the extraction time was shortened by 35.72%. This research is of great importance to the remote sensing dynamic observation of tailings ponds on a large scale.<\/jats:p>","DOI":"10.3390\/rs15020327","type":"journal-article","created":{"date-parts":[[2023,1,5]],"date-time":"2023-01-05T05:29:48Z","timestamp":1672896588000},"page":"327","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Fast Tailings Pond Mapping Exploiting Large Scene Remote Sensing Images by Coupling Scene Classification and Sematic Segmentation Models"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2846-9882","authenticated-orcid":false,"given":"Pan","family":"Wang","sequence":"first","affiliation":[{"name":"College of Geoscience and Surveying Engineering, China University of Mining and Technology\u2014Beijing, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0776-142X","authenticated-orcid":false,"given":"Hengqian","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Geoscience and Surveying Engineering, China University of Mining and Technology\u2014Beijing, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zihan","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Geoscience and Surveying Engineering, China University of Mining and Technology\u2014Beijing, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Jin","sequence":"additional","affiliation":[{"name":"Hebei Research Center for Geoanalysis, Baoding 071051, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanhua","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Geoscience and Surveying Engineering, China University of Mining and Technology\u2014Beijing, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengjiu","family":"Xia","sequence":"additional","affiliation":[{"name":"College of Geoscience and Surveying Engineering, China University of Mining and Technology\u2014Beijing, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingxuan","family":"Meng","sequence":"additional","affiliation":[{"name":"College of Geoscience and Surveying Engineering, China University of Mining and Technology\u2014Beijing, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.mineng.2014.01.018","article-title":"Current state of fine mineral tailings treatment: A critical review on theory and practice","volume":"58","author":"Wang","year":"2014","journal-title":"Miner. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1016\/j.ijmst.2020.05.007","article-title":"A resilience-based approach in managing the closure and abandonment of large mine tailing ponds","volume":"30","author":"Komljenovic","year":"2020","journal-title":"Int. J. Min. Sci. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1016\/j.petrol.2014.11.020","article-title":"Emissions from oil sands tailings ponds: Review of tailings pond parameters and emission estimates","volume":"127","author":"Small","year":"2015","journal-title":"J. Pet. Sci. Eng."},{"key":"ref_4","first-page":"102119","article-title":"The 2019 Brumadinho tailings dam collapse: Possible cause and impacts of the worst human and environmental disaster in Brazil","volume":"90","author":"Rotta","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1007\/s12583-020-1103-6","article-title":"Early Warning of Heavy Metal Pollution after Tailing Pond Failure Accident","volume":"33","author":"Wang","year":"2022","journal-title":"J. Earth Sci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yan, D., Zhang, H., Li, G., Li, X., Lei, H., Lu, K., Zhang, L., and Zhu, F. (2022). Improved Method to Detect the Tailings Ponds from Multispectral Remote Sensing Images Based on Faster R-CNN and Transfer Learning. Remote Sens., 14.","DOI":"10.3390\/rs14010103"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.1134\/S1062739114060106","article-title":"Integrated assessment of the environmental condition of the high-loaded industrial areas by the remote sensing data","volume":"50","author":"Oparin","year":"2014","journal-title":"J. Min. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Song, W., Song, W., Gu, H., and Li, F. (2020). Progress in the remote sensing monitoring of the ecological environment in mining areas. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17061846"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1007\/s10230-020-00727-1","article-title":"DAMSAT: An eye in the sky for monitoring tailings dams","volume":"40","author":"Lumbroso","year":"2021","journal-title":"Mine Water Environ."},{"key":"ref_10","first-page":"1215","article-title":"High-resolution remote sensing image rare earth mining identification method based on Mask R-CNN","volume":"49","author":"Li","year":"2020","journal-title":"J. China Univ. Min. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3589","DOI":"10.1109\/JSTARS.2022.3171290","article-title":"Open-Pit Mine Area Mapping with Gaofen-2 Satellite Images Using U-Net+","volume":"15","author":"Chen","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Rivera, M.J., Lu\u00eds, A.T., Grande, J.A., Sarmiento, A.M., D\u00e1vila, J.M., Fortes, J.C., C\u00f3rdoba, F., Diaz-Curiel, J., and Santisteban, M. (2019). Physico-chemical influence of surface water contaminated by acid mine drainage on the populations of diatoms in dams (Iberian Pyrite Belt, SW Spain). Int. J. Environ. Res. Public Health, 16.","DOI":"10.3390\/ijerph16224516"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.chemosphere.2016.10.040","article-title":"Effect of two different composts on soil quality and on the growth of various plant species in a polymetallic acidic mine soil","volume":"168","author":"Mingorance","year":"2017","journal-title":"Chemosphere"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"106579","DOI":"10.1016\/j.gexplo.2020.106579","article-title":"Statistical analysis of tailings ponds in China","volume":"216","author":"Tang","year":"2020","journal-title":"J. Geochem. Explor."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3555","DOI":"10.1109\/TIP.2021.3062726","article-title":"Multi-Task Deep Learning for Image Segmentation Using Recursive Approximation Tasks","volume":"30","author":"Ke","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2340","DOI":"10.1109\/TIP.2021.3051462","article-title":"EnlightenGAN: Deep Light Enhancement without Paired Supervision","volume":"30","author":"Jiang","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Fan, M., Lai, S., Huang, J., Wei, X., Chai, Z., Luo, J., and Wei, X. (2021, January 20\u201325). Rethinking BiSeNet for real-time semantic segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00959"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1779","DOI":"10.1109\/TGRS.2018.2869101","article-title":"Remote Sensing Image Scene Classification Using Rearranged Local Features","volume":"57","author":"Yuan","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","first-page":"1","article-title":"DifUnet++: A Satellite Images Change Detection Network Based on Unet++ and Differential Pyramid","volume":"19","author":"Zhang","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1039","DOI":"10.1109\/JSTARS.2022.3140776","article-title":"Multiscale and Direction Target Detecting in Remote Sensing Images via Modified YOLO-v4","volume":"15","author":"Zakria","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Xu, G., Wu, X., Zhang, X., and He, X. (2021). LeviT-UNet: Make faster encoders with transformer for medical image segmentation. arXiv.","DOI":"10.2139\/ssrn.4116174"},{"key":"ref_22","unstructured":"Huang, X., Deng, Z., Li, D., and Yuan, X. (2021). MISSformer: An effective medical image segmentation transformer. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Huang, H., Lin, L., Tong, R., Hu, H., Zhang, Q., Iwamoto, Y., Han, X., Chen, Y.-W., and Wu, J. (2020, January 4\u20138). UNet 3+: A full-scale connected unet for medical image segmentation. Proceedings of the 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2020), Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"111716","DOI":"10.1016\/j.rse.2020.111716","article-title":"Deep learning in environmental remote sensing: Achievements and challenges","volume":"241","author":"Yuan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Shi, W., Zhang, M., Zhang, R., Chen, S., and Zhan, Z. (2020). Change detection based on artificial intelligence: State-of-the-art and challenges. Remote Sens., 12.","DOI":"10.3390\/rs12101688"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1109\/MGRS.2020.3046356","article-title":"Deep learning meets SAR: Concepts, models, pitfalls, and perspectives","volume":"9","author":"Zhu","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1362","DOI":"10.1109\/JSTARS.2020.2978864","article-title":"Semisupervised center loss for remote sensing image scene classification","volume":"13","author":"Zhang","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.isprsjprs.2019.11.023","article-title":"Object detection in optical remote sensing images: A survey and a new benchmark","volume":"159","author":"Li","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"114417","DOI":"10.1016\/j.eswa.2020.114417","article-title":"A review of deep learning methods for semantic segmentation of remote sensing imagery","volume":"169","author":"Yuan","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_31","first-page":"293","article-title":"Detection of tailings pond in Beijing-Tianjin-Hebei region based on SSD model","volume":"36","author":"Li","year":"2021","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_32","first-page":"129","article-title":"Remote sensing identification of tailings pond based on deep learning model","volume":"46","author":"Liu","year":"2021","journal-title":"Sci. Surv. Mapp."},{"key":"ref_33","first-page":"65","article-title":"Tailing pond extraction of Tangshan City based on Multi-Task-Branch Network","volume":"41","author":"Zhang","year":"2022","journal-title":"J. Henan Polytech. Univ. Nat. Sci."},{"key":"ref_34","first-page":"21","article-title":"SSD: Single shot multibox detector","volume":"Volume 9905","author":"Leibe","year":"2016","journal-title":"Computer Vision\u2013ECCV 2016, Proceedings of the European Conference on Computer Vision 2016 (ECCV 2016), Amsterdam, The Netherlands, 8\u201316 October 2016"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_36","first-page":"360","article-title":"Automatic extraction of tailing pond based on SSD of deep learning","volume":"37","author":"Kai","year":"2020","journal-title":"J. Univ. Chin. Acad. Sci."},{"key":"ref_37","first-page":"234","article-title":"U-Net: Convolutional networks for biomedical image segmentation","volume":"Volume 9351","author":"Navab","year":"2015","journal-title":"Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015, Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2015), Munich, Germany, 5\u20139 October 2015"},{"key":"ref_38","first-page":"252","article-title":"Recognition of the spatial scopes of tailing ponds based on U-Net and GF-6 images","volume":"33","author":"Zhang","year":"2021","journal-title":"Remote Sens. Land Resour."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Lyu, J., Hu, Y., Ren, S., Yao, Y., Ding, D., Guan, Q., and Tao, L. (2021). Extracting the Tailings Ponds from High Spatial Resolution Remote Sensing Images by Integrating a Deep Learning-Based Model. Remote Sens., 13.","DOI":"10.3390\/rs13040743"},{"key":"ref_40","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018, January 18\u201323). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2018 (CVPR 2018), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016 (CVPR 2016), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Liu, A., Yang, Y., Sun, Q., and Xu, Q. (2018, January 20\u201322). A deep fully convolution neural network for semantic segmentation based on adaptive feature fusion. Proceedings of the 5th International Conference on Information Science and Control Engineering (ICISCE 2018), Zhengzhou, China.","DOI":"10.1109\/ICISCE.2018.00013"},{"key":"ref_44","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Lin, S.-Q., Wang, G.-J., Liu, W.-L., Zhao, B., Shen, Y.-M., Wang, M.-L., and Li, X.-S. (2022). Regional Distribution and Causes of Global Mine Tailings Dam Failures. Metals, 12.","DOI":"10.3390\/met12060905"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Cheng, D., Cui, Y., Li, Z., and Iqbal, J. (2021). Watch Out for the Tailings Pond, a Sharp Edge Hanging over Our Heads: Lessons Learned and Perceptions from the Brumadinho Tailings Dam Failure Disaster. Remote Sens., 13.","DOI":"10.3390\/rs13091775"},{"key":"ref_47","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the Advances in Neural Information Processing Systems 2017 (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_48","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Roy, S.K., Deria, A., Hong, D., Rasti, B., Plaza, A., and Chanussot, J. (2022). Multimodal fusion transformer for remote sensing image classification. arXiv.","DOI":"10.1109\/TGRS.2023.3286826"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/2\/327\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:00:01Z","timestamp":1760119201000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/2\/327"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,5]]},"references-count":49,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["rs15020327"],"URL":"https:\/\/doi.org\/10.3390\/rs15020327","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,5]]}}}