{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T15:07:39Z","timestamp":1778339259142,"version":"3.51.4"},"reference-count":50,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,19]],"date-time":"2022-08-19T00:00:00Z","timestamp":1660867200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program","award":["2017YFC1500902"],"award-info":[{"award-number":["2017YFC1500902"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate mapping of dams can provide useful information about geographical locations and boundaries and can help improve public dam datasets. However, when applied to disaster emergency management, it is often difficult to completely determine the distribution of dams due to the incompleteness of the available data. Thus, we propose an automatic and intelligent extraction method that combines location with post-segmentation for dam detection. First, we constructed a dataset named RSDams and proposed an object detection model, YOLOv5s-ViT-BiFPN (You Only Look Once version 5s-Vision Transformer-Bi-Directional Feature Pyramid Network), with a training method using deep transfer learning to generate graphical locations for dams. After retraining the model on the RSDams dataset, its precision for dam detection reached 88.2% and showed a 3.4% improvement over learning from scratch. Second, based on the graphical locations, we utilized an improved Morphological Building Index (MBI) algorithm for dam segmentation to derive dam masks. The average overall accuracy and Kappa coefficient of the model applied to 100 images reached 97.4% and 0.7, respectively. Finally, we applied the dam extraction method to two study areas, namely, Yangbi County of Yunnan Province and Changping District of Beijing in China, and the recall rates reached 69.2% and 81.5%, respectively. The results show that our method has high accuracy and good potential to serve as an automatic and intelligent method for the establishment of a public dam dataset on a regional or national scale.<\/jats:p>","DOI":"10.3390\/rs14164049","type":"journal-article","created":{"date-parts":[[2022,8,22]],"date-time":"2022-08-22T01:56:40Z","timestamp":1661133400000},"page":"4049","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Dam Extraction from High-Resolution Satellite Images Combined with Location Based on Deep Transfer Learning and Post-Segmentation with an Improved MBI"],"prefix":"10.3390","volume":"14","author":[{"given":"Yafei","family":"Jing","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhuan","family":"Ren","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yalan","family":"Liu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dacheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linjun","family":"Yu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1890\/100125","article-title":"High-resolution mapping of the world\u2019s reservoirs and dams for sustainable river-flow management","volume":"9","author":"Lehner","year":"2011","journal-title":"Front. Ecol. Environ."},{"key":"ref_2","unstructured":"(2022, January 20). AQUASTAT-FAO\u2019s Global Information System on Water and Agriculture (Food and Agriculture Organization of the United Nations). Available online: http:\/\/www.fao.org\/nr\/water\/aquastat\/dams\/."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/s00027-014-0377-0","article-title":"A global boom in hydropower dam construction","volume":"77","author":"Zarfl","year":"2015","journal-title":"Aquat. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1038\/s41597-020-0362-5","article-title":"GOODD, a global dataset of more than 38,000 georeferenced dams","volume":"7","author":"Mulligan","year":"2020","journal-title":"Sci. Data"},{"key":"ref_5","unstructured":"(2021, December 20). OpenStreetMap. Available online: https:\/\/www.openstreetmap.org."},{"key":"ref_6","unstructured":"(2020, September 01). ICOLD (International Commission on Large Dams). Available online: https:\/\/www.icold-cigb.org\/GB\/icold\/icold.asp."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Balaniuk, R., Isupova, O., and Reece, S. (2020). Mining and tailings dam detection in satellite imagery using deep learning. Sensors, 20.","DOI":"10.3390\/s20236936"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully Convolutional Networks for Semantic Segmentation. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Christian, S., Reed, S., Fu, C.-Y., and Berg, A.C. (2016, January 11\u201314). SSD: Single Shot Multibox Detector. Proceedings of the 14th European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_10","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 (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_11","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-Jin-Ji Region, China. Remote Sens., 12.","DOI":"10.3390\/rs12162626"},{"key":"ref_12","first-page":"102576","article-title":"Detecting unknown dams from high-resolution remote sensing images: A deep learning and spatial analysis approach","volume":"104","author":"Jing","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ferreira, E., Brito, M., Balaniuk, R., Alvim, M.S., and dos Santos, J.A. (2020, January 22\u201326). Brazildam: A Benchmark Dataset for Tailings Dam Detection. Proceedings of the 2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS), Santiago, Chile.","DOI":"10.1109\/LAGIRS48042.2020.9165620"},{"key":"ref_14","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_15","doi-asserted-by":"crossref","unstructured":"Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., and Liu, C. (2018, January 4\u20137). A Survey on Deep Transfer Learning. Proceedings of the 27th International Conference on Artificial Neural Networks (ICANN 2018), Rhodes, Greece.","DOI":"10.1007\/978-3-030-01424-7_27"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Oquab, M., Bottou, L., Laptev, I., and Sivic, J. (2014, January 23\u201328). Learning and Transferring Mid-Level Image Representations Using Convolutional Neural Networks. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.222"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Razavian, A.S., Azizpour, H., Sullivan, J., and Carlsson, S. (2014, January 23\u201328). CNN Features Off-the-Shelf: An Astounding Baseline for Recognition. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPRW.2014.131"},{"key":"ref_18","unstructured":"Yosinski, J., Clune, J., Bengio, Y., and Lipson, H. (2014, January 8\u201311). How Transferable are Features in Deep Neural Networks?. Proceedings of the Advances in Neural Information Processing Systems 27 (NIPS\u201914), Montreal, QU, Canada. Available online: https:\/\/arxiv.org\/abs\/1411.1792."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"721","DOI":"10.14358\/PERS.77.7.721","article-title":"A Multidirectional and Multiscale Morphological Index for Automatic Building Extraction from Multispectral GeoEye-1 Imagery","volume":"77","author":"Huang","year":"2011","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1109\/LGRS.2013.2278551","article-title":"Quality Assessment of Panchromatic and Multispectral Image Fusion for the ZY-3 Satellite: From an Information Extraction Perspective","volume":"11","author":"Huang","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A threshold selection method from gray level histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Jung, S., Lee, K., and Lee, W.H. (2022). Object-Based High-Rise Building Detection Using Morphological Building Index and Digital Map. Remote Sens., 14.","DOI":"10.3390\/rs14020330"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","article-title":"SLIC Superpixels Compared to State-of-the-Art Superpixel Methods","volume":"34","author":"Achanta","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","first-page":"100","article-title":"Remote Sensing Image Building Extraction Method that Combination of MBI and SLIC Algorithm","volume":"42","author":"Wei","year":"2019","journal-title":"Geomat. Spat. Inf. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollar, P., and Zitnick, C.L. (2014, January 6\u201312). Microsoft coco: Common objects in context. Proceedings of the 13th European Conference on Computer Vision (ECCV), Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_26","unstructured":"Jocher, G., Stoken, A., and Borovec, J. (2021, June 25). Ultralytic\/Yolov5. Available online: https:\/\/github.com\/ultralytics\/yolov5."},{"key":"ref_27","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2021, January 4). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. Proceedings of the 2021 International Conference on Learning Representations (ICLR), Vienna, Austria. Available online: https:\/\/arxiv.org\/abs\/2010.11929."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 13\u201319). EfficientDet: Scalable and Efficient Object Detection. Proceedings of the 2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA. Available online: https:\/\/ieeexplore.ieee.org\/document\/9156454.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Neubeck, A., and Van Gool, A. (2006, January 20\u201324). Efficient Non-Maximum Suppression. Proceedings of the 18th International Conference on Pattern Recognition (ICPR\u201906), Hong Kong, China.","DOI":"10.1109\/ICPR.2006.479"},{"key":"ref_30","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Jing, Y., Ren, Y., Liu, Y., Wang, D., and Yu, L. (2022). Automatic Extraction of Damaged Houses by Earthquake Based on Improved YOLOv5: A Case Study in Yangbi. Remote Sens, 14.","DOI":"10.3390\/rs14020382"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Mark Liao, H.-Y., Wu, Y.-H., Chen, P.-Y., Hsieh, J.-W., and Yeh, I.-H. (2020, January 14\u201319). CSPNet: A New Backbone that can Enhance Learning Capability of CNN. Proceedings of the 2020 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201323). Path Aggregation Network for Instance Segmentation. Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA. Available online: https:\/\/ieeexplore.ieee.org\/document\/8579011.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_35","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Bodla, N., Singh, B., Chellappa, R., and Davis, L.S. (2017, January 22\u201329). Improving Object Detection with One Line of Code. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.593"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Jiang, B., Luo, R., Mao, J., Xiao, T., and Jiang, Y. (2018, January 8\u201314). Acquisition of Localization Confidence for Accurate Object Detection. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_48"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Liu, S., Huang, D., and Wang, Y. (2019, January 15\u201320). Adaptive NMS: Refining Pedestrian Detection in a Crowd. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00662"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"12993","DOI":"10.1609\/aaai.v34i07.6999","article-title":"Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression","volume":"Volume 34","author":"Zheng","year":"2020","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence, Hilton"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"104117","DOI":"10.1016\/j.imavis.2021.104117","article-title":"Weighted boxes fusion: Ensembling boxes from different object models","volume":"107","author":"Solovyev","year":"2021","journal-title":"Image Vis. Comput."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"106780","DOI":"10.1016\/j.compag.2022.106780","article-title":"An improved YOLOv5 model based on visual attention mechanism: Application to recognition of tomato virus disease","volume":"194","author":"Qi","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1109\/TNNLS.2014.2330900","article-title":"Transfer Learning for Visual Categorization: A Survey","volume":"26","author":"Shao","year":"2015","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Soumik, M.F.I., Aziz, A.Z.B., and Hossain, M.A. (2021, January 8\u20139). Improved Transfer Learning Based Deep Learning Model for Breast Cancer Histopathological Image Classification. Proceedings of the 2021 International Conference on Automation, Control and Mechatronics for Industry 4.0 (ACMI), Rajshahi, Bangladesh.","DOI":"10.1109\/ACMI53878.2021.9528263"},{"key":"ref_44","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201311). Generative Adversarial Networks. Proceedings of the Advances in Neural Information Processing Systems 27 (NIPS\u201914), Montreal, QU, Canada. Available online: https:\/\/arxiv.org\/abs\/1406.2661."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1109\/JSTARS.2011.2168195","article-title":"Morphological building\/shadow index for building extraction from high-resolution imagery over urban areas","volume":"5","author":"Huang","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_46","unstructured":"Van Etten, A. (2018). You Only Look Twice: Rapid multi-scale object detection in satellite imagery. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1038\/nature20584","article-title":"High-resolution mapping of global surface water and its long-term changes","volume":"540","author":"Pekel","year":"2016","journal-title":"Nature"},{"key":"ref_48","unstructured":"Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N., Brockmann, C., Quast, R., Wevers, J., Grosu, A., Paccini, A., and Vergnaud, S. (2022, January 20). ESA WorldCover 10 m 2020 v100. Available online: https:\/\/doi.org\/10.5281\/zenodo.5571936."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., and Fergus, R. (2014, January 6\u201312). Visualizing and Understanding Convolutional Networks. Proceedings of the 13th European Conference on Computer Vision (ECCV), Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/16\/4049\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:12:18Z","timestamp":1760141538000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/16\/4049"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,19]]},"references-count":50,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["rs14164049"],"URL":"https:\/\/doi.org\/10.3390\/rs14164049","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,19]]}}}