{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T10:49:23Z","timestamp":1775472563937,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,11,27]],"date-time":"2021-11-27T00:00:00Z","timestamp":1637971200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NSFC\/RGC Joint Research Scheme","award":["N_HKUST620\/20"],"award-info":[{"award-number":["N_HKUST620\/20"]}]},{"name":"Research Grants Council of the Hong Kong SAR Government","award":["16203720"],"award-info":[{"award-number":["16203720"]}]},{"name":"Research Grants Council of the Hong Kong SAR Government","award":["16205719"],"award-info":[{"award-number":["16205719"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Snow preserves fresh water and impacts regional climate and the environment. Enabled by modern satellite Earth observations, fast and accurate automated snow mapping is now possible. In this study, we developed the Automated Snow Mapper Powered by Machine Learning (AutoSMILE), which is the first machine learning-based open-source system for snow mapping. It is built in a Python environment based on object-based analysis. AutoSMILE was first applied in a mountainous area of 1002 km2 in Bome County, eastern Tibetan Plateau. A multispectral image from Sentinel-2B, a digital elevation model, and machine learning algorithms such as random forest and convolutional neural network, were utilized. Taking only 5% of the study area as the training zone, AutoSMILE yielded an extraordinarily satisfactory result over the rest of the study area: the producer\u2019s accuracy, user\u2019s accuracy, intersection over union and overall accuracy reached 99.42%, 98.78%, 98.21% and 98.76%, respectively, at object level, corresponding to 98.84%, 98.35%, 97.23% and 98.07%, respectively, at pixel level. The model trained in Bome County was subsequently used to map snow at the Qimantag Mountain region in the northern Tibetan Plateau, and a high overall accuracy of 97.22% was achieved. AutoSMILE outperformed threshold-based methods at both sites and exhibited superior performance especially in handling complex land covers. The outstanding performance and robustness of AutoSMILE in the case studies suggest that AutoSMILE is a fast and reliable tool for large-scale high-accuracy snow mapping and monitoring.<\/jats:p>","DOI":"10.3390\/rs13234826","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"4826","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["An Automated Snow Mapper Powered by Machine Learning"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1803-6250","authenticated-orcid":false,"given":"Haojie","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7208-5515","authenticated-orcid":false,"given":"Limin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7706-5502","authenticated-orcid":false,"given":"Jian","family":"He","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongyu","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,27]]},"reference":[{"key":"ref_1","first-page":"25","article-title":"The Most Detailed Portrait of Earth","volume":"136","author":"Arino","year":"2008","journal-title":"Eur. Space Agency"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Dedieu, J.-P., Carlson, B.Z., Bigot, S., Sirguey, P., Vionnet, V., and Choler, P. (2016). On the Importance of High-Resolution Time Series of Optical Imagery for Quantifying the Effects of Snow Cover Duration on Alpine Plant Habitat. Remote Sens., 8.","DOI":"10.3390\/rs8060481"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1038\/s41893-019-0305-3","article-title":"Importance of Snow and Glacier Meltwater for Agriculture on the Indo-Gangetic Plain","volume":"2","author":"Biemans","year":"2019","journal-title":"Nat. Sustain."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.crm.2018.03.001","article-title":"The Snow Load in Europe and the Climate Change","volume":"20","author":"Croce","year":"2018","journal-title":"Clim. Risk Manag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1007\/s40333-015-0044-x","article-title":"Uncertainties of Snow Cover Extraction Caused by the Nature of Topography and Underlying Surface","volume":"7","author":"Zhao","year":"2015","journal-title":"J. Arid Land"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2712","DOI":"10.1016\/j.scitotenv.2018.10.128","article-title":"Ground-Based Evaluation of MODIS Snow Cover Product V6 across China: Implications for the Selection of NDSI Threshold","volume":"651","author":"Zhang","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1629","DOI":"10.5194\/tc-12-1629-2018","article-title":"On the Need for a Time- and Location-Dependent Estimation of the NDSI Threshold Value for Reducing Existing Uncertainties in Snow Cover Maps at Different Scales","volume":"12","author":"Bernhardt","year":"2018","journal-title":"Cryosphere"},{"key":"ref_8","unstructured":"Hall, D.K., Riggs, G.A., Salomonson, V.V., Barton, J., Casey, K., Chien, J., DiGirolamo, N., Klein, A., Powell, H., and Tait, A. (2001). Algorithm Theoretical Basis Document (ATBD) for the MODIS Snow and Sea Ice-Mapping Algorithms, NASA Goddard Space Flight Center."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"493","DOI":"10.5194\/essd-11-493-2019","article-title":"Theia Snow Collection: High-Resolution Operational Snow Cover Maps from Sentinel-2 and Landsat-8 Data","volume":"11","author":"Gascoin","year":"2019","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_10","unstructured":"Hall, D.K., and Salomonson, V.V. (2006). MODIS Snow Products User Guide to Collection 5, NASA Goddard Space Flight Center."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1534","DOI":"10.1002\/hyp.6715","article-title":"Accuracy Assessment of the MODIS Snow Products","volume":"21","author":"Hall","year":"2007","journal-title":"Hydrol. Process."},{"key":"ref_12","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_13","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_14","first-page":"1","article-title":"A Comparative Analysis of Pixel- and Object-Based Detection of Landslides from Very High-Resolution Images","volume":"64","author":"Keyport","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1109\/JSTARS.2013.2274668","article-title":"A Comparison of Pixel- and Object-Based Glacier Classification with Optical Satellite Images","volume":"7","author":"Rastner","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, X., Gao, X., Zhang, X., Wang, W., and Yang, F. (2020). An Automated Method for Surface Ice\/Snow Mapping Based on Objects and Pixels from Landsat Imagery in a Mountainous Region. Remote Sens., 12.","DOI":"10.3390\/rs12030485"},{"key":"ref_17","unstructured":"Wang, H.J., Zhang, L.M., and Xiao, T. (2020, January 4\u20137). DTM and Rainfall-Based Landslide Susceptibility Analysis Using Machine Learning: A Case Study of Lantau Island, Hong Kong. Proceedings of the The Seventh Asian-Pacific Symposium on Structural Reliability and Its Applications (APSSRA 2020), Tokyo, Japan."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.enggeo.2019.02.004","article-title":"A Novel Physically-Based Model for Updating Landslide Susceptibility","volume":"251","author":"Wang","year":"2019","journal-title":"Eng. Geol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1080\/17499518.2020.1751208","article-title":"A Data-Driven Fuzzy Model for Prediction of Rockburst","volume":"15","author":"Rastegarmanesh","year":"2021","journal-title":"Georisk Assess. Manag. Risk Eng. Syst. Geohazards"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1080\/17499518.2019.1612526","article-title":"Optimisation of Deep Mixing Technique by Artificial Neural Network Based on Laboratory and Field Experiments","volume":"14","author":"Hosseini","year":"2020","journal-title":"Georisk Assess. Manag. Risk Eng. Syst. Geohazards"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, L., Chen, Y., Tang, L., Fan, R., and Yao, Y. (2018). Object-Based Convolutional Neural Networks for Cloud and Snow Detection in High-Resolution Multispectral Imagers. Water, 10.","DOI":"10.3390\/w10111666"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, C., Huang, X., Li, X., and Liang, T. (2020). MODIS Fractional Snow Cover Mapping Using Machine Learning Technology in a Mountainous Area. Remote Sens., 12.","DOI":"10.3390\/rs12060962"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Rahmati, O., Ghorbanzadeh, O., Teimurian, T., Mohammadi, F., Tiefenbacher, J.P., Falah, F., Pirasteh, S., Ngo, P.T.T., and Bui, D.T. (2019). Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions. Remote Sens., 11.","DOI":"10.3390\/rs11242995"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Tsai, Y.-L.S., Dietz, A., Oppelt, N., and Kuenzer, C. (2019). Wet and Dry Snow Detection Using Sentinel-1 SAR Data for Mountainous Areas with a Machine Learning Technique. Remote Sens., 11.","DOI":"10.3390\/rs11080895"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"112399","DOI":"10.1016\/j.rse.2021.112399","article-title":"High-Resolution Cubesat Imagery and Machine Learning for Detailed Snow-Covered Area","volume":"258","author":"Cannistra","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"884","DOI":"10.1007\/s11629-019-5723-1","article-title":"Snow Cover Estimation from MODIS and Sentinel-1 SAR Data Using Machine Learning Algorithms in the Western Part of the Tianshan Mountains","volume":"17","author":"Liu","year":"2020","journal-title":"J. Mt. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4610","DOI":"10.1109\/TGRS.2017.2694881","article-title":"Super-Resolving Multiresolution Images with Band-Independent Geometry of Multispectral Pixels","volume":"55","author":"Brodu","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1002\/hyp.3360050103","article-title":"Digital Terrain Modelling: A Review of Hydrological, Geomorphological, and Biological Applications","volume":"5","author":"Moore","year":"1991","journal-title":"Hydrol. Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/S0034-4257(01)00318-2","article-title":"Detection of Forest Harvest Type Using Multiple Dates of Landsat TM Imagery","volume":"80","author":"Wilson","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3025","DOI":"10.1080\/01431160600589179","article-title":"Modification of Normalised Difference Water Index (NDWI) to Enhance Open Water Features in Remotely Sensed Imagery","volume":"27","author":"Xu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Vedaldi, A., and Soatto, S. (2008, January 23\u201328). Quick Shift and Kernel Methods for Mode Seeking. Computer Vision\u2014ECCV 2008, Proceedings of the European Conference on Computer Vision, Marseille, France.","DOI":"10.1007\/978-3-540-88693-8_52"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1109\/PROC.1979.11328","article-title":"Statistical and Structural Approaches to Texture","volume":"67","author":"Haralick","year":"1979","journal-title":"Proc. IEEE"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.gsf.2020.02.012","article-title":"Landslide Identification Using Machine Learning","volume":"12","author":"Wang","year":"2021","journal-title":"Geosci. Front."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"106103","DOI":"10.1016\/j.enggeo.2021.106103","article-title":"AI-Powered Landslide Susceptibility Assessment in Hong Kong","volume":"288","author":"Wang","year":"2021","journal-title":"Eng. Geol."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An Introduction to Statistical Learning, Springer.","DOI":"10.1007\/978-1-4614-7138-7"},{"key":"ref_36","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4826\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:36:50Z","timestamp":1760168210000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4826"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,27]]},"references-count":36,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13234826"],"URL":"https:\/\/doi.org\/10.3390\/rs13234826","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,27]]}}}