{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T06:31:05Z","timestamp":1784615465216,"version":"3.55.0"},"reference-count":42,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,5,23]],"date-time":"2021-05-23T00:00:00Z","timestamp":1621728000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2016YFB0501504"],"award-info":[{"award-number":["2016YFB0501504"]}]},{"name":"National Natural Science Foundation of China","award":["41771397"],"award-info":[{"award-number":["41771397"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Dam failure of tailings ponds can result in serious casualties and environmental pollution. Therefore, timely and accurate monitoring is crucial for managing tailings ponds and preventing damage from tailings pond accidents. Remote sensing technology facilitates the regular extraction and monitoring of tailings pond information. However, traditional remote sensing techniques are inefficient and have low levels of automation, which hinders the large-scale, high-frequency, and high-precision extraction of tailings pond information. Moreover, research into the automatic and intelligent extraction of tailings pond information from high-resolution remote sensing images is relatively rare. However, the deep learning end-to-end model offers a solution to this problem. This study proposes an intelligent and high-precision method for extracting tailings pond information from high-resolution images, which improves deep learning target detection model: faster region-based convolutional neural network (Faster R-CNN). A comparison study is conducted and the model input size with the highest precision is selected. The feature pyramid network (FPN) is adopted to obtain multiscale feature maps with rich context information, the attention mechanism is used to improve the FPN, and the contribution degrees of feature channels are recalibrated. The model test results based on GoogleEarth high-resolution remote sensing images indicate a significant increase in the average precision (AP) and recall of tailings pond detection from that of Faster R-CNN by 5.6% and 10.9%, reaching 85.7% and 62.9%, respectively. Considering the current rapid increase in high-resolution remote sensing images, this method will be important for large-scale, high-precision, and intelligent monitoring of tailings ponds, which will greatly improve the decision-making efficiency in tailings pond management.<\/jats:p>","DOI":"10.3390\/rs13112052","type":"journal-article","created":{"date-parts":[[2021,5,24]],"date-time":"2021-05-24T00:01:20Z","timestamp":1621814480000},"page":"2052","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["An Improved Faster R-CNN Method to Detect Tailings Ponds from High-Resolution Remote Sensing Images"],"prefix":"10.3390","volume":"13","author":[{"given":"Dongchuan","family":"Yan","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Institute of Mineral Resources Research, China Metallurgical Geology Bureau, Beijing 101300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoqing","family":"Li","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangqiang","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Mineral Resources Research, China Metallurgical Geology Bureau, Beijing 101300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0206-9381","authenticated-orcid":false,"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hua","family":"Lei","sequence":"additional","affiliation":[{"name":"Institute of Mineral Resources Research, China Metallurgical Geology Bureau, Beijing 101300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3553-4849","authenticated-orcid":false,"given":"Kaixuan","family":"Lu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minghua","family":"Cheng","sequence":"additional","affiliation":[{"name":"Institute of Mineral Resources Research, China Metallurgical Geology Bureau, Beijing 101300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuxiao","family":"Zhu","sequence":"additional","affiliation":[{"name":"Institute of Mineral Resources Research, China Metallurgical Geology Bureau, Beijing 101300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,23]]},"reference":[{"key":"ref_1","first-page":"680","article-title":"Application of analytic hierarchy process to tailings pond safety operation analysis","volume":"29","author":"Wang","year":"2008","journal-title":"Rock Soil Mech."},{"key":"ref_2","first-page":"100","article-title":"The Application of Remote Sensing in the Environmental Risk Monitoring of Tailings pond in Zhangjiakou City, China","volume":"29","author":"Xiao","year":"2014","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Santamarina, J.C., Torres-Cruz, L.A., and Bachus, R.C. (2019). Why coal ash and tailings dam disasters occur. Science, 364.","DOI":"10.1126\/science.aax1927"},{"key":"ref_4","unstructured":"Jie, L. (2014). Remote Sensing Research and Application of Tailings Pond\u2013A Case Study on the Tailings Pond in Hebei Province, China University of Geosciences."},{"key":"ref_5","first-page":"26","article-title":"Remote sensing monitoring of tailings ponds based on the latest domestic satellite data","volume":"33","author":"Gao","year":"2019","journal-title":"J. Heilongjiang Inst. Technol."},{"key":"ref_6","first-page":"246","article-title":"Application of remote sensing technology to environmental pollution monitoring","volume":"15","author":"Tan","year":"2000","journal-title":"Remote. Sens. Technol. Appl."},{"key":"ref_7","first-page":"209","article-title":"Application of remote sensing technology to environment monitoring","volume":"4","author":"Dai","year":"2007","journal-title":"West. Explor. Eng."},{"key":"ref_8","first-page":"53","article-title":"The progress and challenges of satellite remote sensing technology applications in the field of environmental protection","volume":"25","author":"Wang","year":"2009","journal-title":"Environ. Monit. China"},{"key":"ref_9","first-page":"90","article-title":"Application of TM image in monitoring the water quality of tailing reservoir","volume":"30","author":"Liu","year":"2010","journal-title":"Min. Res. Dev."},{"key":"ref_10","unstructured":"Zhao, Y.M. (2011). Moniter Tailings based on 3S Technology to Tower Mountain in Shanxi Province. [Master\u2019s Thesis, China University of Geoscience]."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hao, L., Zhang, Z., and Yang, X. (2019). Mine tailing extraction indexes and model using remote-sensing images in southeast Hubei Province. Environ. Earth Sci., 78.","DOI":"10.1007\/s12665-019-8439-1"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ma, B., Chen, Y., Zhang, S., and Li, X. (2018). Remote sensing extraction method of tailings ponds in ultra-low-grade iron mining area based on spectral characteristics and texture entropy. Entropy, 20.","DOI":"10.3390\/e20050345"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Xiao, R., Shen, W., Fu, Z., Shi, Y., Xiong, W., and Cao, F. (2012). The application of remote sensing in the environmental risk monitoring of tailings pond: A case study in Zhangjiakou area of China. SPIE Proc., 8538.","DOI":"10.1117\/12.964380"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1957","DOI":"10.1007\/s12665-011-1422-0","article-title":"Pyrite mine waste and water mapping using Hymap and Hyperion hyperspectral data","volume":"66","author":"Riaza","year":"2012","journal-title":"Environ. Earth Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Li, Q., Chen, Z., Zhang, B., Li, B., Lu, K., Lu, L., and Guo, H. (2020). Detection of tailings dams using high-resolution satellite imagery and a single shot multibox detector in the Jing\u2013Jin\u2013Ji Region, China. Remote. Sens., 12.","DOI":"10.3390\/rs12162626"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 8\u201316). Ssd: Single shot multibox detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_17","unstructured":"Pereira, F., Burges, C.J.C., Bottou, L., and Weinberger, K.Q. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_18","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_19","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, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maarten, L., and Weinberger, K.Q. (2016). Densely connected convolutional networks. arXiv.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.neucom.2020.01.085","article-title":"Recent advances in deep learning for object detection","volume":"396","author":"Wu","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_24","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2016). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada, 7\u201312 December 2015, MIT Press."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_26","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Li, Y., Huang, Q., Pei, X., Jiao, L. (2020). RADet: Refine feature pyramid network and multi-layer attention network for arbitrary-oriented object detection of remote sensing images. Remote Sens., 12.","DOI":"10.3390\/rs12030389"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask r-cnn. Proceedings of the 2017 IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Li, Y., Xu, W., Chen, H., Jiang, J., and Li, X. (2021). A Novel Framework Based on Mask R-CNN and Histogram Thresholding for Scalable Segmentation of New and Old Rural Buildings. Remote. Sens., 13.","DOI":"10.3390\/rs13061070"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Bhuiyan, M.A.E., Witharana, C., and Liljedahl, A.K. (2020). Use of Very High Spatial Resolution Commercial Satellite Imagery and Deep Learning to Automatically Map Ice-Wedge Polygons across Tundra Vegetation Types. J. Imaging, 6.","DOI":"10.3390\/jimaging6120137"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhao, K., Kang, J., Jung, J., and Sohn, G. (2018, January 18\u201322). Building extraction from satellite images using mask R-CNN with building boundary regularization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00045"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Bai, T., Pang, Y., Wang, J., Han, K., Luo, J., Wang, H., Lin, J., Wu, J., and Zhang, H. (2020). An Optimized Faster R-CNN Method Based on DRNet and RoI Align for Building Detection in Remote Sensing Images. Remote Sens., 12.","DOI":"10.3390\/rs12050762"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Liu, Y., Cen, C., Che, Y., Ke, R., Ma, Y., and Ma, Y. (2020). Detection of Maize Tassels from UAV RGB Imagery with Faster R-CNN. Remote Sens., 12.","DOI":"10.3390\/rs12020338"},{"key":"ref_35","first-page":"3238","article-title":"Review of new progress in tailing dam safety in foreign research and current state with development trend in China","volume":"33","author":"Yu","year":"2014","journal-title":"Chin. J. Rock Mech. Eng."},{"key":"ref_36","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Dollar, P., Girshick, R., He, H., Hariharan, B., and Belongie, S. (2017). Feature pyramid networks for object detection. arXiv.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Chaudhari, S., Mithal, V., Polatkan, G., and Ramanath, R. (2020). An attentive survey of attention models. arXiv.","DOI":"10.1145\/3465055"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation Networks. Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1002\/(SICI)1097-4571(199401)45:1<12::AID-ASI2>3.0.CO;2-L","article-title":"The relationship between Recall and Precision","volume":"45","author":"Buckland","year":"1994","journal-title":"J. Am. Soc. Inf. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.isprsjprs.2013.12.011","article-title":"Efficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding","volume":"89","author":"Han","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/11\/2052\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:06:19Z","timestamp":1760162779000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/11\/2052"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,23]]},"references-count":42,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["rs13112052"],"URL":"https:\/\/doi.org\/10.3390\/rs13112052","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,23]]}}}