{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T22:14:44Z","timestamp":1767046484143,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T00:00:00Z","timestamp":1663286400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002848","name":"Chilean Science Council (ANID)","doi-asserted-by":"publisher","award":["PII-180008","ACT210080","ANID\/R20F0002","ANID\/FONDAP\/15130015"],"award-info":[{"award-number":["PII-180008","ACT210080","ANID\/R20F0002","ANID\/FONDAP\/15130015"]}],"id":[{"id":"10.13039\/501100002848","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Water Research Center For Agriculture and Mining, CRHIAM","award":["PII-180008","ACT210080","ANID\/R20F0002","ANID\/FONDAP\/15130015"],"award-info":[{"award-number":["PII-180008","ACT210080","ANID\/R20F0002","ANID\/FONDAP\/15130015"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Landslide inventories are crucial to studying the dynamics, associated risks, and effects of these geomorphological processes on the evolution of mountainous landscapes. The production of landslide maps is mainly based on manual visual interpretation methods of aerial and satellite images combined with field surveys. In recent times, advances in machine learning methods have made it possible to explore new semi-automated landslide detection methodologies using remotely detected images. In this sense, developing new artificial intelligence models based on Deep Learning (DL) opens up an excellent opportunity to automate this arduous process. Although the Andes mountain range is one of the most geomorphologically active areas on the planet, the few investigations that use DL mainly focus on mountain ranges in Europe and Asia. One of the main reasons is the low density of landslide data available in the Andean areas, making it difficult to experiment with DL models requiring large data volumes. In this work, we seek to narrow the existing gap in the availability of landslide inventories in the area of the Patagonian Andes. In addition, the feasibility and efficiency of DL techniques are studied to develop landslide detection models in the Andes from the generated datasets. To achieve this goal, we generated in a manual process a datasets of 10,000 landslides for northern Chilean Patagonia (42\u201345\u00b0S), being the largest freely accessible landslide datasets in this region. We implement a machine learning model, through DL, to detect landslides in optical images of the Sentinel-2 constellation using a model based on the DeepLabv3+ architecture, a state-of-the-art deep learning network for semantic segmentation. Our results indicate that the algorithm detects landslides with an accuracy of 0.75 at the object level. For its part, the segmentation reaches a precision of 0.86, a recall of 0.74, and an F1-score of 0.79. The correlation of the segmentation measured through the Matthews correlation coefficient shows a value of 0.59, and the geometric similarity of the correctly detected landslides measured through the Jaccard score reaches 0.70. Although the model shows a good response in the testing area, errors are generated that can be explained by geometric and spectral relationships, which should be solved through new training approaches and data sets.<\/jats:p>","DOI":"10.3390\/rs14184622","type":"journal-article","created":{"date-parts":[[2022,9,19]],"date-time":"2022-09-19T04:49:22Z","timestamp":1663562962000},"page":"4622","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Patagonian Andes Landslides Inventory: The Deep Learning\u2019s Way to Their Automatic Detection"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7863-5503","authenticated-orcid":false,"given":"Bastian","family":"Morales","sequence":"first","affiliation":[{"name":"Butamallin Research Center for Global Change, Universidad de La Frontera, Av. Francisco Salazar 01145, Temuco 4780000, Chile"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6848-481X","authenticated-orcid":false,"given":"Angel","family":"Garcia-Pedrero","sequence":"additional","affiliation":[{"name":"Department of Computer Architecture and Technology, Universidad Polit\u00e9cnica de Madrid, 28660 Boadilla del Monte, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8150-5208","authenticated-orcid":false,"given":"Elizabet","family":"Lizama","sequence":"additional","affiliation":[{"name":"Butamallin Research Center for Global Change, Universidad de La Frontera, Av. Francisco Salazar 01145, Temuco 4780000, Chile"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5634-9162","authenticated-orcid":false,"given":"Mario","family":"Lillo-Saavedra","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda Agr\u00edcola, Universidad de Concepci\u00f3n, Chill\u00e1n 3812120, Chile"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0804-9293","authenticated-orcid":false,"given":"Consuelo","family":"Gonzalo-Mart\u00edn","sequence":"additional","affiliation":[{"name":"Department of Computer Architecture and Technology, Universidad Polit\u00e9cnica de Madrid, 28660 Boadilla del Monte, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6135-0739","authenticated-orcid":false,"given":"Ningsheng","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Mountain Hazards and Earth Surface Processes, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7863-4407","authenticated-orcid":false,"given":"Marcelo","family":"Somos-Valenzuela","sequence":"additional","affiliation":[{"name":"Butamallin Research Center for Global Change, Universidad de La Frontera, Av. Francisco Salazar 01145, Temuco 4780000, Chile"},{"name":"Department of Forest Sciences, Faculty of Agriculture and Environmental Sciencies, Universidad de La Frontera, Av. Francisco Salazar 01145, Temuco 4780000, Chile"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1038\/s41561-019-0315-9","article-title":"Increased landslide activity on forested hillslopes following two recent volcanic eruptions in Chile","volume":"12","author":"Korup","year":"2019","journal-title":"Nat. Geosci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2767","DOI":"10.1007\/s10346-021-01675-9","article-title":"A comparative machine learning approach to identify landslide triggering factors in northern Chilean Patagonia","volume":"18","author":"Morales","year":"2021","journal-title":"Landslides"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1007\/s10346-010-0203-2","article-title":"Landslides induced by the April 2007 Ays\u00e9n Fjord earthquake, Chilean Patagonia","volume":"7","author":"Serey","year":"2010","journal-title":"Landslides"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2319","DOI":"10.5194\/nhess-20-2319-2020","article-title":"The mudflow disaster at Villa Santa Luc\u00eda in Chilean Patagonia: Understandings and insights derived from numerical simulation and postevent field surveys","volume":"20","author":"Chen","year":"2020","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.earscirev.2012.02.001","article-title":"Landslide inventory maps: New tools for an old problem","volume":"112","author":"Guzzetti","year":"2012","journal-title":"Earth-Sci. Rev."},{"key":"ref_6","first-page":"1","article-title":"Review on remote sensing methods for landslide detection using machine and deep learning","volume":"32","author":"Mohan","year":"2021","journal-title":"Trans. Emerg. Telecommun. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, S., Tiede, D., and Aryal, J. (2019). Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection. Remote Sens., 11.","DOI":"10.3390\/rs11020196"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3390","DOI":"10.1080\/01431161.2019.1701725","article-title":"Transferability of object-based image analysis approaches for landslide detection in the Himalaya Mountains of northern Pakistan","volume":"41","author":"Bacha","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"114363","DOI":"10.1109\/ACCESS.2019.2935761","article-title":"Landslide Detection Using Residual Networks and the Fusion of Spectral and Topographic Information","volume":"7","author":"Sameen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Prakash, N., Manconi, A., and Loew, S. (2020). Mapping Landslides on EO Data: Performance of Deep Learning Models vs. Traditional Machine Learning Models. Remote Sens., 12.","DOI":"10.5194\/egusphere-egu2020-11876"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6166","DOI":"10.1109\/JSTARS.2020.3028855","article-title":"A New Deep-Learning-Based Approach for Earthquake-Triggered Landslide Detection From Single-Temporal RapidEye Satellite Imagery","volume":"13","author":"Yi","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Qi, W., Wei, M., Yang, W., Xu, C., and Ma, C. (2020). Automatic Mapping of Landslides by the ResU-Net. Remote Sens., 12.","DOI":"10.3390\/rs12152487"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"9722","DOI":"10.1038\/s41598-021-89015-8","article-title":"A new strategy to map landslides with a generalized convolutional neural network","volume":"11","author":"Prakash","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., and Kalinin, A.A. (2020). Albumentations: Fast and Flexible Image Augmentations. Information, 11.","DOI":"10.3390\/info11020125"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_17","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_18","doi-asserted-by":"crossref","unstructured":"Abraham, N., and Khan, N.M. (2019, January 8\u201311). A novel focal tversky loss function with improved attention u-net for lesion segmentation. Proceedings of the 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy.","DOI":"10.1109\/ISBI.2019.8759329"},{"key":"ref_19","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"106179","DOI":"10.1016\/j.compag.2021.106179","article-title":"Improving deep learning sorghum head detection through test time augmentation","volume":"186","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"14629","DOI":"10.1038\/s41598-021-94190-9","article-title":"A comprehensive transferability evaluation of U-Net and ResU-Net for landslide detection from Sentinel-2 data (case study areas from Taiwan, China, and Japan)","volume":"11","author":"Ghorbanzadeh","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Shahabi, H., Rahimzad, M., Tavakkoli Piralilou, S., Ghorbanzadeh, O., Homayouni, S., Blaschke, T., Lim, S., and Ghamisi, P. (2021). Unsupervised Deep Learning for Landslide Detection from Multispectral Sentinel-2 Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13224698"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1007\/s10346-021-01843-x","article-title":"Landslide detection using deep learning and object-based image analysis","volume":"19","author":"Ghorbanzadeh","year":"2022","journal-title":"Landslides"},{"key":"ref_24","first-page":"1","article-title":"The application of ResU-net and OBIA for landslide detection from multi-temporal sentinel-2 images","volume":"6","author":"Ghorbanzadeh","year":"2022","journal-title":"Big Earth Data"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"117642","DOI":"10.1016\/j.epsl.2022.117642","article-title":"Deep learning reveals one of Earth\u2019s largest landslide terrain in Patagonia","volume":"593","author":"Winocur","year":"2022","journal-title":"Earth Planet. Sci. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1038\/s43017-020-0040-3","article-title":"Climate impacts of the El Ni no\u2013Southern Oscillation on South America","volume":"1","author":"Cai","year":"2020","journal-title":"Nat. Rev. Earth Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1038\/35002501","article-title":"Biodiversity hotspots for conservation priorities","volume":"403","author":"Myers","year":"2000","journal-title":"Nature"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"753","DOI":"10.5194\/nhess-22-753-2022","article-title":"Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine","volume":"22","author":"Handwerger","year":"2022","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Nava, L., Bhuyan, K., Meena, S.R., Monserrat, O., and Catani, F. (2022). Rapid Mapping of Landslides on SAR Data by Attention U-Net. Remote Sens., 14.","DOI":"10.3390\/rs14061449"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/18\/4622\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:32:40Z","timestamp":1760142760000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/18\/4622"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,16]]},"references-count":29,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["rs14184622"],"URL":"https:\/\/doi.org\/10.3390\/rs14184622","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,9,16]]}}}