{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T13:13:26Z","timestamp":1775913206971,"version":"3.50.1"},"reference-count":76,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2020,12,25]],"date-time":"2020-12-25T00:00:00Z","timestamp":1608854400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41974094"],"award-info":[{"award-number":["41974094"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41874004"],"award-info":[{"award-number":["41874004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory  (Guangzhou)","award":["GML2019ZD0209"],"award-info":[{"award-number":["GML2019ZD0209"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Variations of lake area and shoreline can indicate hydrological and climatic changes effectively. Accordingly, how to automatically and simultaneously extract lake area and shoreline from remote sensing images attracts our attention. In this paper, we formulate lake area and shoreline extraction as a multitask learning problem. Different from existing models that take the deep and complex network architecture as the backbone to extract feature maps, we present LaeNet\u2014a novel end-to-end lightweight multitask fully CNN with no-downsampling to automatically extract lake area and shoreline from remote sensing images. Landsat-8 images over Selenco and the vicinity in the Tibetan Plateau are utilized to train and evaluate our model. Experimental results over the testing image patches achieve an Accuracy of 0.9962, Precision of 0.9912, Recall of 0.9982, F1-score of 0.9941, and mIoU of 0.9879, which align with the mainstream semantic segmentation models (UNet, DeepLabV3+, etc.) or even better. Especially, the running time of each epoch and the size of our model are only 6 s and 0.047 megabytes, which achieve a significant reduction compared to the other models. Finally, we conducted fieldwork to collect the in-situ shoreline position for one typical part of lake Selenco, in order to further evaluate the performance of our model. The validation indicates high accuracy in our results (DRMSE: 30.84 m, DMAE: 22.49 m, DSTD: 21.11 m), only about one pixel deviation for Landsat-8 images. LaeNet can be expanded potentially to the tasks of area segmentation and edge extraction in other application fields.<\/jats:p>","DOI":"10.3390\/rs13010056","type":"journal-article","created":{"date-parts":[[2020,12,25]],"date-time":"2020-12-25T09:30:19Z","timestamp":1608888619000},"page":"56","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["LaeNet: A Novel Lightweight Multitask CNN for Automatically Extracting Lake Area and Shoreline from Remote Sensing Images"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0038-6519","authenticated-orcid":false,"given":"Wei","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen 518055, China"},{"name":"School of Computer and Software, Nanyang Institute of Technology, Nanyang 473004, China"}]},{"given":"Xingyu","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen 518055, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9245-3346","authenticated-orcid":false,"given":"Jiangjun","family":"Ran","sequence":"additional","affiliation":[{"name":"Department of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen 518055, China"},{"name":"Shenzhen Key Laboratory of Deep Offshore Oil and Gas Exploration Technology, Southern University of Science and Technology, Shenzhen 518055, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9581-1337","authenticated-orcid":false,"given":"Lin","family":"Liu","sequence":"additional","affiliation":[{"name":"Earth System Science Programme, Faculty of Science, The Chinese University of Hong Kong, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8006-9950","authenticated-orcid":false,"given":"Qiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Vultus AB, Lilla Fiskaregatan 19, 22222 Lund, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5779-5479","authenticated-orcid":false,"given":"Linyang","family":"Xin","sequence":"additional","affiliation":[{"name":"Department of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen 518055, China"}]},{"given":"Gang","family":"Li","sequence":"additional","affiliation":[{"name":"Guangzhou Marine Geological Survey, Guangzhou 510075, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory, Guangzhou 511458, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1007\/s10584-011-0099-4","article-title":"Response of hydrological cycle to recent climate changes in the Tibetan Plateau","volume":"109","author":"Yang","year":"2011","journal-title":"Clim. 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