{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T11:39:47Z","timestamp":1772969987459,"version":"3.50.1"},"reference-count":106,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T00:00:00Z","timestamp":1651795200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001807","name":"Sao Paulo Research Foundation (FAPESP)","doi-asserted-by":"publisher","award":["2019\/17555-1"],"award-info":[{"award-number":["2019\/17555-1"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001807","name":"Sao Paulo Research Foundation (FAPESP)","doi-asserted-by":"publisher","award":["2016\/06628-0"],"award-info":[{"award-number":["2016\/06628-0"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001807","name":"Sao Paulo Research Foundation (FAPESP)","doi-asserted-by":"publisher","award":["2019\/26568-0"],"award-info":[{"award-number":["2019\/26568-0"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001807","name":"Sao Paulo Research Foundation (FAPESP)","doi-asserted-by":"publisher","award":["423481\/2018-5"],"award-info":[{"award-number":["423481\/2018-5"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001807","name":"Sao Paulo Research Foundation (FAPESP)","doi-asserted-by":"publisher","award":["304413\/2018-6"],"award-info":[{"award-number":["304413\/2018-6"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"National Council of Scientific and Technological Development","doi-asserted-by":"publisher","award":["2019\/17555-1"],"award-info":[{"award-number":["2019\/17555-1"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"National Council of Scientific and Technological Development","doi-asserted-by":"publisher","award":["2016\/06628-0"],"award-info":[{"award-number":["2016\/06628-0"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"National Council of Scientific and Technological Development","doi-asserted-by":"publisher","award":["2019\/26568-0"],"award-info":[{"award-number":["2019\/26568-0"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"National Council of Scientific and Technological Development","doi-asserted-by":"publisher","award":["423481\/2018-5"],"award-info":[{"award-number":["423481\/2018-5"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"National Council of Scientific and Technological Development","doi-asserted-by":"publisher","award":["304413\/2018-6"],"award-info":[{"award-number":["304413\/2018-6"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Automatic landslide mapping is crucial for a fast response in a disaster scenario and improving landslide susceptibility models. Recent studies highlighted the potential of deep learning methods for automatic landslide segmentation. However, only a few works discuss the generalization capacity of these models to segment landslides in areas that differ from the ones used to train the models. In this study, we evaluated three different locations to assess the generalization capacity of these models in areas with similar and different environmental aspects. The model training consisted of three distinct datasets created with RapidEye satellite images, Normalized Vegetation Index (NDVI), and a digital elevation model (DEM). Here, we show that larger patch sizes (128 \u00d7 128 and 256 \u00d7 256 pixels) favor the detection of landslides in areas similar to the training area, while models trained with smaller patch sizes (32 \u00d7 32 and 64 \u00d7 64 pixels) are better for landslide detection in areas with different environmental aspects. In addition, we found that the NDVI layer helped to balance the model\u2019s results and that morphological post-processing operations are efficient for improving the segmentation precision results. Our research highlights the potential of deep learning models for segmenting landslides in different areas and is a starting point for more sophisticated investigations that evaluate model generalization in images from various sensors and resolutions.<\/jats:p>","DOI":"10.3390\/rs14092237","type":"journal-article","created":{"date-parts":[[2022,5,8]],"date-time":"2022-05-08T23:27:25Z","timestamp":1652052445000},"page":"2237","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Landslide Segmentation with Deep Learning: Evaluating Model Generalization in Rainfall-Induced Landslides in Brazil"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6980-597X","authenticated-orcid":false,"given":"Lucas Pedrosa","family":"Soares","sequence":"first","affiliation":[{"name":"Institute of Geosciences, University of S\u00e3o Paulo (IGc-USP), S\u00e3o Paulo 05508-080, Brazil"},{"name":"Spatial Analysis and Modelling Lab (SPAMLab), Institute of Energy and Environment, University of S\u00e3o Paulo (IEE-USP), S\u00e3o Paulo 05508-080, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5006-7006","authenticated-orcid":false,"given":"Helen Cristina","family":"Dias","sequence":"additional","affiliation":[{"name":"Spatial Analysis and Modelling Lab (SPAMLab), Institute of Energy and Environment, University of S\u00e3o Paulo (IEE-USP), S\u00e3o Paulo 05508-080, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1209-7842","authenticated-orcid":false,"given":"Guilherme Pereira Bento","family":"Garcia","sequence":"additional","affiliation":[{"name":"Institute of Geosciences, University of S\u00e3o Paulo (IGc-USP), S\u00e3o Paulo 05508-080, Brazil"},{"name":"Spatial Analysis and Modelling Lab (SPAMLab), Institute of Energy and Environment, University of S\u00e3o Paulo (IEE-USP), S\u00e3o Paulo 05508-080, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5073-5572","authenticated-orcid":false,"given":"Carlos Henrique","family":"Grohmann","sequence":"additional","affiliation":[{"name":"Spatial Analysis and Modelling Lab (SPAMLab), Institute of Energy and Environment, University of S\u00e3o Paulo (IEE-USP), S\u00e3o Paulo 05508-080, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.rse.2005.08.004","article-title":"Remote sensing of landslides: An analysis of the potential contribution to geo-spatial systems for hazard assessment in mountainous environments","volume":"98","author":"Metternicht","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2161","DOI":"10.5194\/nhess-18-2161-2018","article-title":"Global fatal landslide occurrence from 2004 to 2016","volume":"18","author":"Froude","year":"2018","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10064-006-0080-z","article-title":"The Third Hans Cloos Lecture. Urban landslides: Socioeconomic impacts and overview of mitigative strategies","volume":"66","author":"Schuster","year":"2007","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yi, Y., Zhang, Z., Zhang, W., Zhang, C., Li, W., and Zhao, T. (2019). Semantic segmentation of urban buildings from VHR remote sensing imagery using a deep convolutional neural network. Remote Sens., 11.","DOI":"10.3390\/rs11151774"},{"key":"ref_5","first-page":"139","article-title":"Rainfall-induced landslide susceptibility assessment at the Chongren area (China) using frequency ratio, certainty factor, and index of entropy","volume":"32","author":"Hong","year":"2017","journal-title":"Geocarto Int."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.geomorph.2006.09.022","article-title":"A brief survey of GIS in mass-movement studies, with reflections on theory and methods","volume":"94","author":"Alexander","year":"2008","journal-title":"Geomorphology"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1555","DOI":"10.1080\/01431161.2019.1672904","article-title":"Landslide mapping with remote sensing: Challenges and opportunities","volume":"41","author":"Zhong","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","unstructured":"Tominaga, L.K., Santoro, J., and Amaral, R. (2009). Desastres Naturais, Instituto Geol\u00f3gico."},{"key":"ref_9","unstructured":"CRED (2022, May 01). EM-DAT: The International Emergency Disasters Database. Available online: https:\/\/www.emdat.be\/."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/S0928-2025(08)10012-8","article-title":"Landslides and disasters in southeastern and southern Brazil","volume":"13","author":"Lacerda","year":"2009","journal-title":"Dev. Earth Surf. Process."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Netto, A.L.C., Sato, A.M., de Souza Avelar, A., Vianna, L.G.G., Ara\u00fajo, I.S., Ferreira, D.L., Lima, P.H., Silva, A.P.A., and Silva, R.P. (2013). January 2011: The extreme landslide disaster in Brazil. Landslide Science and Practice, Springer.","DOI":"10.1007\/978-3-642-31319-6_51"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Vieira, B.C., and Gramani, M.F. (2015). Serra do Mar: The most \u201ctormented\u201d relief in Brazil. Landscapes and Landforms of Brazil, Springer.","DOI":"10.1007\/978-94-017-8023-0_26"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mondini, A.C., Santangelo, M., Rocchetti, M., Rossetto, E., Manconi, A., and Monserrat, O. (2019). Sentinel-1 SAR Amplitude Imagery for Rapid Landslide Detection. Remote Sens., 11.","DOI":"10.3390\/rs11070760"},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/j.geomorph.2016.08.032","article-title":"Spatial patterns of landslide dimension: A tool for magnitude mapping","volume":"273","author":"Catani","year":"2016","journal-title":"Geomorphology"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Mavroulis, S., Diakakis, M., Kranis, H., Vassilakis, E., Kapetanidis, V., Spingos, I., Kaviris, G., Skourtsos, E., Voulgaris, N., and Lekkas, E. (2022). Inventory of Historical and Recent Earthquake-Triggered Landslides and Assessment of Related Susceptibility by GIS-Based Analytic Hierarchy Process: The Case of Cephalonia (Ionian Islands, Western Greece). Appl. Sci., 12.","DOI":"10.3390\/app12062895"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Shao, X., Ma, S., Xu, C., Shen, L., and Lu, Y. (2020). Inventory, distribution and geometric characteristics of landslides in Baoshan City, Yunnan Province, China. Sustainability, 12.","DOI":"10.3390\/su12062433"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1080\/17445647.2012.694271","article-title":"Landslide inventory map for the Briga and the Giampilieri catchments, NE Sicily, Italy","volume":"8","author":"Ardizzone","year":"2012","journal-title":"J. Maps"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1080\/17445647.2013.852142","article-title":"Landslide inventory map of north-eastern Calabria (South Italy)","volume":"10","author":"Conforti","year":"2014","journal-title":"J. Maps"},{"key":"ref_20","unstructured":"Dias, H.C., H\u00f6lbling, D.W., and Grohmann, C.H. (2021, January 22\u201326). Landslide inventory mapping in Brazil: Status and challenges. Proceedings of the XIII International Symposium on Landslides, virtual."},{"key":"ref_21","first-page":"392","article-title":"An\u00e1lise comparativa entre m\u00e9todos heur\u00edsticos de mapeamento de \u00e1reas suscept\u00edveis a escorregamento","volume":"1","author":"Marcelino","year":"2004","journal-title":"Simp\u00f3sio Bras. Desastr. Nat."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.enggeo.2006.09.019","article-title":"Landslide susceptibility revealed by LIDAR imagery and historical records, Seattle, Washington","volume":"89","author":"Schulz","year":"2007","journal-title":"Eng. Geol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.geomorph.2009.06.006","article-title":"Landslide susceptibility mapping using geological data, a DEM from ASTER images and an Artificial Neural Network (ANN)","volume":"113","author":"Kawabata","year":"2009","journal-title":"Geomorphology"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1007\/s12559-012-9148-1","article-title":"Deformation prediction of landslide based on improved back-propagation neural network","volume":"5","author":"Chen","year":"2013","journal-title":"Cogn. Comput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1016\/j.autcon.2006.11.002","article-title":"3D laser scanning and GPS technology for landslide earthwork volume estimation","volume":"16","author":"Du","year":"2007","journal-title":"Autom. Constr."},{"key":"ref_26","first-page":"133","article-title":"Remote monitoring of a landslide using an integration of GB-INSAR and LIDAR techniques","volume":"37","author":"Lingua","year":"2008","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.geomorph.2008.09.015","article-title":"LIDAR monitoring of mass wasting processes: The Radicofani landslide, Province of Siena, Central Italy","volume":"105","author":"Baldo","year":"2009","journal-title":"Geomorphology"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s11069-010-9634-2","article-title":"Use of LIDAR in landslide investigations: A review","volume":"61","author":"Jaboyedoff","year":"2012","journal-title":"Nat. Hazards"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1007\/s10064-012-0417-8","article-title":"Three-dimensional distinct element modelling and dynamic runout analysis of a landslide in gneissic rock, British Columbia, Canada","volume":"71","author":"Brideau","year":"2012","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2720","DOI":"10.3390\/rs5062720","article-title":"Landslide displacement monitoring using 3D range flow on airborne and terrestrial LiDAR data","volume":"5","author":"Ghuffar","year":"2013","journal-title":"Remote Sens."},{"key":"ref_31","first-page":"92","article-title":"Landslide mapping and monitoring by using radar and optical remote sensing: Examples from the EC-FP7 project SAFER","volume":"4","author":"Casagli","year":"2016","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_32","unstructured":"Hsiao, K., Liu, J., Yu, M., and Tseng, Y. (2004, January 12\u201313). Change detection of landslide terrains using ground-based LiDAR data. Proceedings of the XXth ISPRS Congress, Istanbul, Turkey, Commission VII, WG, Istanbul, Turkey."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/S0169-555X(03)00164-8","article-title":"Objective landslide detection and surface morphology mapping using high-resolution airborne laser altimetry","volume":"57","author":"McKean","year":"2004","journal-title":"Geomorphology"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.geomorph.2005.07.006","article-title":"Analysis of LiDAR-derived topographic information for characterizing and differentiating landslide morphology and activity","volume":"73","author":"Glenn","year":"2006","journal-title":"Geomorphology"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.enggeo.2008.02.006","article-title":"Tracking landslide displacements by multi-temporal DTMs: A combined aerial stereophotogrammetric and LIDAR approach in western Belgium","volume":"99","author":"Dewitte","year":"2008","journal-title":"Eng. Geol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.geomorph.2009.06.004","article-title":"LiDAR-derived DEM evaluation of deep-seated landslides in a steep and rocky region of Japan","volume":"113","author":"Kasai","year":"2009","journal-title":"Geomorphology"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Liu, J.K., Chang, K.T., Rau, J.Y., Hsu, W.C., Liao, Z.Y., Lau, C.C., and Shih, T.Y. (2009). The geomorphometry of rainfall-induced landslides in taiwan obtained by airborne lidar and digital photography. Geoscience and Remote Sensing, In-Tech, Inc.","DOI":"10.5772\/8305"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"315","DOI":"10.2113\/gseegeosci.16.4.315","article-title":"Analysis of elevation changes detected from multi-temporal LiDAR surveys in forested landslide terrain in western Oregon","volume":"16","author":"Burns","year":"2010","journal-title":"Environ. Eng. Geosci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3237","DOI":"10.1016\/j.rse.2011.07.007","article-title":"Tracking and evolution of complex active landslides by multi-temporal airborne LiDAR data: The Montaguto landslide (Southern Italy)","volume":"115","author":"Ventura","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.enggeo.2008.03.010","article-title":"Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview","volume":"102","author":"Castellanos","year":"2008","journal-title":"Eng. Geol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1111\/gto.12034","article-title":"Landslide prediction from machine learning","volume":"30","author":"Korup","year":"2014","journal-title":"Geol. Today"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1767","DOI":"10.1007\/s10346-017-0820-0","article-title":"The influence of systematically incomplete shallow landslide inventories on statistical susceptibility models and suggestions for improvements","volume":"14","author":"Steger","year":"2017","journal-title":"Landslides"},{"key":"ref_43","unstructured":"Nilsen, T.H. (1973). Preliminary Photointerpretation Map of Landslide and Other Surficial Deposits of the Concord 15-Minute Quadrangle and the Oakland West, Richmond, and Part of the San Quentin 7 1\/2-Minute Quadrangles, Contra Costa and Alameda Counties, California, Technical Report."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/S0169-555X(99)00078-1","article-title":"Landslide hazard evaluation: A review of current techniques and their application in a multi-scale study, Central Italy","volume":"31","author":"Guzzetti","year":"1999","journal-title":"Geomorphology"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.geomorph.2004.11.001","article-title":"The effectiveness of hillshade maps and expert knowledge in mapping old deep-seated landslides","volume":"67","author":"Poesen","year":"2005","journal-title":"Geomorphology"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.geomorph.2009.02.027","article-title":"Automated landslide mapping using spectral analysis and high-resolution topographic data: Puget Sound lowlands, Washington, and Portland Hills, Oregon","volume":"109","author":"Booth","year":"2009","journal-title":"Geomorphology"},{"key":"ref_47","unstructured":"Burns, W.J., and Madin, I. (2022, May 01). Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (LiDAR) Imagery. Available online: https:\/\/www.oregongeology.org\/pubs\/dds\/slido\/sp-42_onscreen.pdf."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.geomorph.2013.04.009","article-title":"\u2018You are HERE\u2019: Connecting the dots with airborne lidar for geomorphic fieldwork","volume":"200","author":"Roering","year":"2013","journal-title":"Geomorphology"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"9600","DOI":"10.3390\/rs6109600","article-title":"Remote sensing for landslide investigations: An overview of recent achievements and perspectives","volume":"6","author":"Scaioni","year":"2014","journal-title":"Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1007\/s10346-011-0299-z","article-title":"Statistical modelling of Europe-wide landslide susceptibility using limited landslide inventory data","volume":"9","author":"Jaedicke","year":"2012","journal-title":"Landslides"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Knevels, R., Petschko, H., Leopold, P., and Brenning, A. (2019). Geographic object-based image analysis for automated landslide detection using open source GIS software. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8120551"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Li, Y., and Chen, W. (2020). Landslide susceptibility evaluation using hybrid integration of evidential belief function and machine learning techniques. Water, 12.","DOI":"10.3390\/w12010113"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"104445","DOI":"10.1016\/j.cageo.2020.104445","article-title":"Comparative study of landslide susceptibility mapping with different recurrent neural networks","volume":"138","author":"Wang","year":"2020","journal-title":"Comput. Geosci."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1016\/j.gsf.2014.03.004","article-title":"Preparation of earthquake-triggered landslide inventory maps using remote sensing and GIS technologies: Principles and case studies","volume":"6","author":"Xu","year":"2015","journal-title":"Geosci. Front."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"104388","DOI":"10.1016\/j.cageo.2019.104388","article-title":"Landslide detection based on contour-based deep learning framework in case of national scale of Nepal in 2015","volume":"135","author":"Yu","year":"2020","journal-title":"Comput. Geosci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object based image analysis for remote sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.isprsjprs.2019.02.009","article-title":"Segmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective","volume":"150","author":"Hossain","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2564","DOI":"10.1016\/j.rse.2011.05.013","article-title":"Object-oriented mapping of landslides using Random Forests","volume":"115","author":"Stumpf","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.isprsjprs.2013.09.014","article-title":"Geographic object-based image analysis\u2013towards a new paradigm","volume":"87","author":"Blaschke","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_60","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_61","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, S.R., 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_62","doi-asserted-by":"crossref","unstructured":"Peng, D., Zhang, Y., and Guan, H. (2019). End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++. Remote Sens., 11.","DOI":"10.3390\/rs11111382"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep learning in remote sensing: A comprehensive review and list of resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_64","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 IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Radovic, M., Adarkwa, O., and Wang, Q. (2017). Object recognition in aerial images using convolutional neural networks. J. Imaging, 3.","DOI":"10.3390\/jimaging3020021"},{"key":"ref_66","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_67","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_68","doi-asserted-by":"crossref","first-page":"1","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_69","doi-asserted-by":"crossref","unstructured":"Pradhan, B., Seeni, M.I., and Nampak, H. (2017). Integration of LiDAR and QuickBird data for automatic landslide detection using object-based analysis and random forests. Laser Scanning Applications in Landslide Assessment, Springer.","DOI":"10.1007\/978-3-319-55342-9_4"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Avelar, A.S., Netto, A.L.C., Lacerda, W.A., Becker, L.B., and Mendon\u00e7a, M.B. (2013). Mechanisms of the recent catastrophic landslides in the mountainous range of Rio de Janeiro, Brazil. Landslide Science and Practice, Springer.","DOI":"10.1007\/978-3-642-31337-0_34"},{"key":"ref_71","unstructured":"Dantas, M.E. (2001). Geomorfologia do Estado do Rio de Janeiro, CPRM. Estudo Geoambiental do Estado do Rio de Janeiro."},{"key":"ref_72","unstructured":"Tupinamb\u00e1, M., Heilbron, M., Duarte, B.P., de Almeida, J.C.H., Valladares, C.S., Pacheco, B.T., dos Santos Salom\u00e3o, M., Concei\u00e7\u00e3o, F.R., da Silva, L.G.E., and de Almeida, C.G. (2012). Mapa Geol\u00f3gico Folha Nova Friburgo SF-23-Z-B-II, CPRM\u2014Servi\u00e7o Geol\u00f3gico do Brasil. Technical Report."},{"key":"ref_73","unstructured":"K\u00f6ppen, W. (1936). Das Geographische System der Klimate, Gerbr\u00fcder Borntr\u00e4ger. Das geographische System der Klimate."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Sobral, B.S., Oliveira-J\u00fanior, J.F., Gois, G., de Bodas Terassi, P.M., and Muniz-J\u00fanior, J.G.R. (2018). Variabilidade espa\u00e7o-temporal e interanual da chuva no estado do Rio de Janeiro. Rev. Bras. Climatol., 22.","DOI":"10.5380\/abclima.v22i0.55592"},{"key":"ref_75","first-page":"158","article-title":"Classification algorithms comparison for landslide scars","volume":"20","author":"Uehara","year":"2019","journal-title":"GEOINFO"},{"key":"ref_76","unstructured":"Gameiro, S., Quevedo, R.P., Oliveira, G., Ruiz, L., and Guasselli, L. (2019, January 14\u201317). An\u00e1lise e correla\u00e7\u00e3o de atributos morfom\u00e9tricos e sua influ\u00eancia nos movimentos de massa ocorridos na Bacia do Rio Rolante, RS. Proceedings of the Anais do XIX Simp\u00f3sio Brasileiro de Sensoriamento Remoto, Santos, Brazil."},{"key":"ref_77","unstructured":"Quevedo, R.P., Oliveira, G., Gameiro, S., Ruiz, L., and Guasselli, L. (2019, January 14\u201317). Modelagem de \u00e1reas suscet\u00edveis a movimentos de massa com redes neurais artificiais. Proceedings of the Anais do XIX Simp\u00f3sio Brasileiro de Sensoriamento Remoto, Santos, Brazil."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"781","DOI":"10.5016\/geociencias.v38i3.14019","article-title":"Modelagem de \u00e1reas suscet\u00edveis a movimentos de massa: Avalia\u00e7\u00e3o comparativa de t\u00e9cnicas de amostragem, aprendizado de m\u00e1quina e modelos digitais de eleva\u00e7\u00e3o","volume":"38","author":"Quevedo","year":"2020","journal-title":"Geoci\u00eancias"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"RG2004","DOI":"10.1029\/2005RG000183","article-title":"The Shuttle Radar Topography Mission","volume":"45","author":"Farr","year":"2007","journal-title":"Rev. Geophys."},{"key":"ref_80","unstructured":"RapidEye, A. (2011). Satellite imagery product specifications. Satellite Imagery Product Specifications: Version, BlackBridge."},{"key":"ref_81","unstructured":"Planet Team (2020, May 01). Planet Application Program Interface: In Space for Life on Earth. Available online: https:\/\/api.planet.com."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_83","unstructured":"(2022, May 01). Rasterio: Geospatial Raster I\/O for Python Programmers. Available online: https:\/\/github.com\/rasterio\/rasterio."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Dixit, M., Kwitt, R., Niethammer, M., and Vasconcelos, N. (2017, January 21\u201326). Aga: Attribute-guided augmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.355"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_86","unstructured":"Quinn, J., McEachen, J., Fullan, M., Gardner, M., and Drummy, M. (2019). Dive into Deep Learning: Tools for Engagement, Corwin Press."},{"key":"ref_87","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_88","unstructured":"(2022, May 01). Keras. Available online: https:\/\/keras.io\/."},{"key":"ref_89","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2022, May 01). TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Available online: tensorflow.org."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Tiede, D., Dabiri, Z., Sudmanns, M., and Lang, S. (2018). Dwelling extraction in refugee camps using cnn\u2013first experiences and lessons learnt. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.","DOI":"10.5194\/isprs-archives-XLII-1-161-2018"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Guirado, E., Tabik, S., Alcaraz-Segura, D., Cabello, J., and Herrera, F. (2017). Deep-learning convolutional neural networks for scattered shrub detection with google earth imagery. arXiv.","DOI":"10.3390\/rs9121220"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"e453","DOI":"10.7717\/peerj.453","article-title":"Scikit-image: Image processing in Python","volume":"2","author":"Boulogne","year":"2014","journal-title":"PeerJ"},{"key":"ref_93","unstructured":"Soares, L.P., Dias, H.C., and Grohmann, C.H. (2020). Landslide segmentation with u-net: Evaluating different sampling methods and patch sizes. arXiv."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/TIT.1968.1054102","article-title":"On the mean accuracy of statistical pattern recognizers","volume":"14","author":"Hughes","year":"1968","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.1109\/36.536541","article-title":"Designing optimal spectral indexes for remote sensing applications","volume":"34","author":"Verstraete","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"1743","DOI":"10.1016\/j.rse.2011.03.006","article-title":"Semi-automatic recognition and mapping of rainfall induced shallow landslides using optical satellite images","volume":"115","author":"Mondini","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"111235","DOI":"10.1016\/j.rse.2019.111235","article-title":"Landslide mapping from multi-sensor data through improved change detection-based Markov random field","volume":"231","author":"Lu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_98","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_99","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1038\/s41592-018-0019-x","article-title":"The curse (s) of dimensionality","volume":"15","author":"Altman","year":"2018","journal-title":"Nat. Methods"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1109\/JSTARS.2020.3043836","article-title":"Landslide Mapping Using Two Main Deep-Learning Convolution Neural Network Streams Combined by the Dempster\u2013Shafer Model","volume":"14","author":"Ghorbanzadeh","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_101","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_102","unstructured":"Bottou, L., Cortes, C., Denker, J.S., Drucker, H., Guyon, I., Jackel, L.D., LeCun, Y., Muller, U.A., Sackinger, E., and Simard, P. (1994, January 9\u201313). Comparison of classifier methods: A case study in handwritten digit recognition. Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3-Conference C: Signal Processing (Cat. No. 94CH3440-5), Jerusalem, Israel."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"2217","DOI":"10.1109\/JSTARS.2019.2918242","article-title":"Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification","volume":"12","author":"Helber","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Yang, Y., and Newsam, S. (2010, January 2\u20135). Bag-of-visual-words and spatial extensions for land-use classification. Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, San Jose, CA, USA.","DOI":"10.1145\/1869790.1869829"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"3965","DOI":"10.1109\/TGRS.2017.2685945","article-title":"AID: A benchmark data set for performance evaluation of aerial scene classification","volume":"55","author":"Xia","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Penatti, O.A., Nogueira, K., and Dos Santos, J.A. (2015, January 7\u201312). Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, MA, USA.","DOI":"10.1109\/CVPRW.2015.7301382"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/9\/2237\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:07:18Z","timestamp":1760137638000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/9\/2237"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,6]]},"references-count":106,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["rs14092237"],"URL":"https:\/\/doi.org\/10.3390\/rs14092237","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,6]]}}}