{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T13:19:06Z","timestamp":1768828746360,"version":"3.49.0"},"reference-count":43,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,22]],"date-time":"2021-11-22T00:00:00Z","timestamp":1637539200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"International Partnership Program of the Chinese Academy of Sciences","award":["131551KYSB20160002\/131211KYSB20170046"],"award-info":[{"award-number":["131551KYSB20160002\/131211KYSB20170046"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41871345"],"award-info":[{"award-number":["41871345"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remote sensing is a powerful tool that provides flexibility and scalability for monitoring and investigating glacial lakes in High Mountain Asia (HMA). However, existing methods for mapping glacial lakes are designed based on a combination of several spectral features and ancillary data (such as the digital elevation model, DEM) to highlight the lake extent and suppress background information. These methods, however, suffer from either the inevitable requirement of post-processing work or the high costs of additional data acquisition. Signifying a key advancement in the deep learning models, a generative adversarial network (GAN) can capture multi-level features and learn the mapping rules in source and target domains using a minimax game between a generator and discriminator. This provides a new and feasible way to conduct large-scale glacial lake mapping. In this work, a complete glacial lake dataset was first created, containing approximately 4600 patches of Landsat-8 OLI images edited in three ways\u2014random cropping, density cropping, and uniform cropping. Then, a GAN model for glacial lake mapping (GAN-GL) was constructed. The GAN-GL consists of two parts\u2014a generator that incorporates a water attention module and an image segmentation module to produce the glacial lake masks, and a discriminator which employs the ResNet-152 backbone to ascertain whether a given pixel belonged to a glacial lake. The model was evaluated using the created glacial lake dataset, delivering a good performance, with an F1 score of 92.17% and IoU of 86.34%. Moreover, compared to the mapping results derived from the global\u2013local iterative segmentation algorithm and random forest for the entire Eastern Himalayas, our proposed model was superior regarding the segmentation of glacial lakes under complex and diverse environmental conditions, in terms of accuracy (precision = 93.19%) and segmentation efficiency. Our model was also very good at detecting small glacial lakes without assistance from ancillary data or human intervention.<\/jats:p>","DOI":"10.3390\/rs13224728","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"4728","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["GAN-GL: Generative Adversarial Networks for Glacial Lake Mapping"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0482-1573","authenticated-orcid":false,"given":"Hang","family":"Zhao","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9621-4879","authenticated-orcid":false,"given":"Meimei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fang","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Hainan Key Laboratory of Earth Observation, Aerospace Information Research Institute, Chinese Academy of Sciences, Sanya 572029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100008","DOI":"10.1016\/j.srs.2020.100008","article-title":"Mapping of glacial lakes using Sentinel-1 and Sentinel-2 data and a random forest classifier: Strengths and challenges","volume":"2","author":"Wangchuk","year":"2020","journal-title":"Sci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Khadka, N., Zhang, G.Q., and Thakuri, S. (2018). Glacial lakes in the Nepal Himalaya: Inventory and decadal dynamics (1977\u20132017). Remote Sens., 10.","DOI":"10.3390\/rs10121913"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Chand, M.B., and Watanabe, T. (2019). Development of supraglacial ponds in the Everest Region, Nepal, between 1989 and 2018. Remote Sens., 11.","DOI":"10.3390\/rs11091058"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1016\/j.jhydrol.2016.06.054","article-title":"Glacial lake evolution in the southeastern Tibetan Plateau and the cause of rapid expansion of proglacial lakes linked to glacial-hydrogeomorphic processes","volume":"540","author":"Song","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5194\/essd-12-2169-2020","article-title":"Glacial lake inventory of High Mountain Asia (1990\u20132018) derived from Landsat images","volume":"12","author":"Wang","year":"2020","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"102892","DOI":"10.1016\/j.earscirev.2019.102892","article-title":"Revisiting the dynamics of catastrophic late Pleistocene glacial-lake drainage, Altai Mountains, central Asia","volume":"197","author":"Bohorqueza","year":"2019","journal-title":"Earth Sci. Rev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.geomorph.2017.06.014","article-title":"Inventory and recently increasing GLOF susceptibility of glacial lakes in Sikkim, Eastern Himalaya","volume":"295","author":"Prakash","year":"2017","journal-title":"Geomorphology"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1080\/19475705.2018.1445663","article-title":"Glacial lake changes and outburst flood hazard in Chandra basin, North-Western Indian Himalaya","volume":"9","author":"Prakash","year":"2018","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.scitotenv.2017.03.068","article-title":"Glacial lake inventory and lake outburst potential in Uzbekistan","volume":"592","author":"Petro","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"741","DOI":"10.5194\/essd-13-741-2021","article-title":"Annual 30\u2009m dataset for glacial lakes in High Mountain Asia from 2008 to 2017","volume":"13","author":"Chen","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.geomorph.2017.01.033","article-title":"Altitudinal dynamics of glacial lakes under changing climate in the Hindu Kush, Karakoram, and Himalaya ranges","volume":"283","author":"Arshad","year":"2017","journal-title":"Geomorphology"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"103432","DOI":"10.1016\/j.earscirev.2020.103432","article-title":"Increasing glacial lake outburst flood hazard in response to surge glaciers in the Karakoram","volume":"212","author":"Bazai","year":"2021","journal-title":"Earth Sci. Rev."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The use of Normalized Difference Water Index (NDWI) in the delineation of open water features","volume":"17","author":"McFeeters","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5194","DOI":"10.1080\/01431161.2012.657370","article-title":"An automated scheme for glacial lake dynamics mapping using Landsat imagery and Digital Elevation Models: A Case Study in the Himalayas","volume":"33","author":"Li","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","first-page":"84","article-title":"A Method for Object\u2014Oriented Automatic Extraction of Lakes in the Mountain Area from Remote Sensing Image","volume":"3","author":"Shen","year":"2012","journal-title":"Remote Sens. Land Resour."},{"key":"ref_16","first-page":"150","article-title":"A lake detection algorithm (LDA) using Landsat 8 data: A comparative approach in glacial environment","volume":"38","author":"Bhardwaj","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1016\/j.jhydrol.2018.03.062","article-title":"Hydrological network and classification of lakes on the Third Pole","volume":"560","author":"Gao","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2788","DOI":"10.1109\/JSTARS.2018.2846551","article-title":"A Systematic Extraction Approach for Mapping Glacial lakes in High Mountain Regions of Asia","volume":"11","author":"Zhao","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","first-page":"1272","article-title":"A lake extraction method in mountainous regions based on the integration of object-oriented approach and watershed algorithm","volume":"23","author":"Li","year":"2021","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_20","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G. (2012, January 3\u20136). ImageNet classification with deep convolutional neural networks. Proceedings of the Conference and Workshop on Neural Information Processing System (NIPS), Lake Tahoe, NE, USA."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 8\u201310). Fully Convolutional Models for Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Hynes Convention Center, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_22","unstructured":"Goodfellow, I.J., Abadie, J.P., Mirza, M., Xu, B., Farley, D.W., Ozair, S., Courvile, A., and Bengio, Y. (2014). Generative Adversarial Nets. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Qayyum, N., Ghuffar, S., Ahmad, H.M., Yousaf, A., and Shahid, I. (2020). Glacial Lakes Mapping Using Multi Satellite PlanetScope Imagery and Deep Learning. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9100560"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wu, R., Liu, G., Zhang, R., Wang, X., Li, Y., Zhang, B., Cai, J., and Xiang, W. (2020). A Deep Learning Method for Mapping Glacial Lakes from the Combined Use of Synthetic-Aperture Radar and Optical Satellite Images. Remote Sens., 12.","DOI":"10.3390\/rs12244020"},{"key":"ref_25","unstructured":"Donahue, J., and Simonyan, K. (2019). Large Scale Adversarial Representation Learning. arXiv."},{"key":"ref_26","unstructured":"Liu, L., Muelly, M., Deng, J., Pfister, T., and Li, L. (November, January 27). Generative Modeling for Small-Data Object Detection. Proceedings of the International Conference on Computer Vision (ICCV), COEX Convention Center, Seoul, Korea."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., and Wang, Z. (2016). Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. arXiv.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kupyn, O., Budzan, V., Mykhailych, M., Mishkin, D., and Matas, J. (2017). DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks. arXiv.","DOI":"10.1109\/CVPR.2018.00854"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Minaee, S., Boykov, Y., Porikli, F., Plaza, A., Kehtarnavaz, N., and Terzopoulos, D. (2020). Image Segmentation Using Deep Learning: A Survey. arXiv.","DOI":"10.1109\/TPAMI.2021.3059968"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Xue, Y., Xu, T., Zhang, H., Long, R., and Huang, X. (2017). SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation. arXiv.","DOI":"10.1007\/s12021-018-9377-x"},{"key":"ref_31","unstructured":"Son, J., Park, S.J., and Jung, K.H. (2017). Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1017\/jog.2019.13","article-title":"Glacial lake evolution and glacier\u2013lake interactions in the Poiqu River basin, central Himalaya, 1964\u20132017","volume":"65","author":"Zhang","year":"2019","journal-title":"J. Glaciol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.rse.2015.12.041","article-title":"Representative lake water extent mapping at continental scales using multi-temporal Landsat-8 imagery","volume":"185","author":"Sheng","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2018). Dual Attention Network for Scene Segmentation. arXiv.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_35","unstructured":"Li, H., Xiong, P., An, J., and Wang, L. (2018). Pyramid Attention Network for Semantic Segmentation. arXiv."},{"key":"ref_36","unstructured":"Li, X., Zhong, Z., Wu, J., Yang, Y., and Liu, Y. (November, January 27). Expectation-Maximization Attention Networks for Semantic Segmentation. Proceedings of the International Conference on Computer Vision (ICCV), COEX Convention Center, Seoul, Korea."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2707","DOI":"10.1007\/s11629-020-6255-4","article-title":"Evaluation of effective spectral features for glacial lake mapping by using Landsat-8 OLI imagery","volume":"17","author":"Zhang","year":"2020","journal-title":"J. Mt. Sci."},{"key":"ref_38","unstructured":"Salimans, T., Goodfellow, I., Zaremba, W., Cheung, W., Radford, A., and Chen, X. (2016). Improved Techniques for Training GANs. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3025","DOI":"10.1080\/01431160600589179","article-title":"Modification of normalized difference water index (NDWI) to enhance open water features in remotely sense imagery","volume":"27","author":"Xu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","first-page":"62","article-title":"A study on information extraction of water system in semi-arid regions with the Enhanced Water Index (EWI) and GIS based noise remove techniques","volume":"6","author":"Pei","year":"2007","journal-title":"Remote Sens. Inf."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1270","DOI":"10.1016\/j.scib.2021.01.014","article-title":"Numerous unreported glacial lake outburst floods in the Third Pole revealed by high-resolution satellite data and geomorphological evidence","volume":"66","author":"Zheng","year":"2021","journal-title":"Sci. Bull."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"5067","DOI":"10.3390\/rs6065067","article-title":"An automated method for extracting rivers and lakes from Landsat imagery","volume":"6","author":"Jiang","year":"2014","journal-title":"Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.rse.2017.12.025","article-title":"Detecting Himalayan glacial lake outburst floods from 16 Landsat time series","volume":"207","author":"Veh","year":"2017","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/22\/4728\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:34:18Z","timestamp":1760168058000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/22\/4728"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,22]]},"references-count":43,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["rs13224728"],"URL":"https:\/\/doi.org\/10.3390\/rs13224728","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,22]]}}}