{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T04:36:08Z","timestamp":1774326968655,"version":"3.50.1"},"reference-count":67,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,10]],"date-time":"2024-08-10T00:00:00Z","timestamp":1723248000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>High-mountain water bodies represent critical components of their ecosystems, serving as vital freshwater reservoirs, environmental regulators, and sentinels of climate change. To understand the environmental dynamics of these regions, comprehensive analyses of lakes across spatial and temporal scales are necessary. While remote sensing offers a powerful tool for lake monitoring, applications in high-mountain terrain present unique challenges. The Ancash and Cuzco regions of the Peruvian Andes exemplify these challenges. These regions harbor numerous high-mountain lakes, which are crucial for fresh water supply and environmental regulation. This paper presents an exploratory examination of remote sensing techniques for lake monitoring in the Ancash and Cuzco regions of the Peruvian Andes. The study compares three deep learning models for lake segmentation: the well-established DeepWaterMapV2 and WatNet models and the adapted WaterSegDiff model, which is based on a combination of diffusion and transformation mechanisms specifically conditioned for lake segmentation. In addition, the Normalized Difference Water Index (NDWI) with Otsu thresholding is used for comparison purposes. To capture lakes across these regions, a new dataset was created with Landsat-8 multispectral imagery (bands 2\u20137) from 2013 to 2023. Quantitative and qualitative analyses were performed using metrics such as Mean Intersection over Union (MIoU), Pixel Accuracy (PA), and F1 Score. The results achieved indicate equivalent performance of DeepWaterMapV2 and WatNet encoder\u2013decoder architectures, achieving adequate lake segmentation despite the challenging geographical and atmospheric conditions inherent in high-mountain environments. In the qualitative analysis, the behavior of the WaterSegDiff model was considered promising for the proposed application. Considering that WatNet is less computationally complex, with 3.4 million parameters, this architecture becomes the most pertinent to implement. Additionally, a detailed temporal analysis of Lake Singrenacocha in the Vilcanota Mountains was conducted, pointing out the more significant behavior of the WatNet model.<\/jats:p>","DOI":"10.3390\/s24165177","type":"journal-article","created":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T11:23:46Z","timestamp":1723461826000},"page":"5177","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Exploratory Analysis Using Deep Learning for Water-Body Segmentation of Peru\u2019s High-Mountain Remote Sensing Images"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5379-5008","authenticated-orcid":false,"given":"William Isaac","family":"Perez-Torres","sequence":"first","affiliation":[{"name":"LIECAR Laboratory, Universidad Nacional de San Antonio Abad del Cusco (UNSAAC), Cusco 08003, Peru"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7177-7970","authenticated-orcid":false,"given":"Diego Armando","family":"Uman-Flores","sequence":"additional","affiliation":[{"name":"LIECAR Laboratory, Universidad Nacional de San Antonio Abad del Cusco (UNSAAC), Cusco 08003, Peru"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2854-9922","authenticated-orcid":false,"given":"Andres Benjamin","family":"Quispe-Quispe","sequence":"additional","affiliation":[{"name":"LIECAR Laboratory, Universidad Nacional de San Antonio Abad del Cusco (UNSAAC), Cusco 08003, Peru"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5947-6682","authenticated-orcid":false,"given":"Facundo","family":"Palomino-Quispe","sequence":"additional","affiliation":[{"name":"LIECAR Laboratory, Universidad Nacional de San Antonio Abad del Cusco (UNSAAC), Cusco 08003, Peru"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4519-8332","authenticated-orcid":false,"given":"Emili","family":"Bezerra","sequence":"additional","affiliation":[{"name":"PAVIC Laboratory, University of Acre (UFAC), Rio Branco 69915-900, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6678-1131","authenticated-orcid":false,"given":"Quefren","family":"Leher","sequence":"additional","affiliation":[{"name":"PAVIC Laboratory, University of Acre (UFAC), Rio Branco 69915-900, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5563-8971","authenticated-orcid":false,"given":"Thuanne","family":"Paix\u00e3o","sequence":"additional","affiliation":[{"name":"PAVIC Laboratory, University of Acre (UFAC), Rio Branco 69915-900, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3403-8261","authenticated-orcid":false,"given":"Ana Beatriz","family":"Alvarez","sequence":"additional","affiliation":[{"name":"PAVIC Laboratory, University of Acre (UFAC), Rio Branco 69915-900, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2283","DOI":"10.4319\/lo.2009.54.6_part_2.2283","article-title":"Lakes as sentinels of climate change","volume":"54","author":"Adrian","year":"2009","journal-title":"Limnol. 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