{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T17:08:09Z","timestamp":1770829689734,"version":"3.50.1"},"reference-count":92,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T00:00:00Z","timestamp":1701648000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministerio de Ciencia Tecnolog\u00eda e Innovaci\u00f3n - Colombia","award":["1-Bicentenario Corte II."],"award-info":[{"award-number":["1-Bicentenario Corte II."]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>Land cover classification, generated from satellite imagery through semantic segmentation, has become fundamental for monitoring land use and land cover change (LULCC). The tropical Andes territory provides opportunities due to its significance in the provision of ecosystem services. However, the lack of reliable data for this region, coupled with challenges arising from its mountainous topography and diverse ecosystems, hinders the description of its coverage. Therefore, this research proposes the Tropical Andes Land Cover Dataset (TALANDCOVER). It is constructed from three sample strategies: aleatory, minimum 50%, and 70% of representation per class, which address imbalanced geographic data. Additionally, the U-Net deep learning model is applied for enhanced and tailored classification of land covers. Using high-resolution data from the NICFI program, our analysis focuses on the Department of Antioquia in Colombia. The TALANDCOVER dataset, presented in TIF format, comprises multiband R-G-B-NIR images paired with six labels (dense forest, grasslands, heterogeneous agricultural areas, bodies of water, built-up areas, and bare-degraded lands) with an estimated 0.76 F1 score compared to ground truth data by expert knowledge and surpassing the precision of existing global cover maps for the study area. To the best of our knowledge, this work is a pioneer in its release of open-source data for segmenting coverages with pixel-wise labeled NICFI imagery at a 4.77 m resolution. The experiments carried out with the application of the sample strategies and models show F1 score values of 0.70, 0.72, and 0.74 for aleatory, balanced 50%, and balanced 70%, respectively, over the expert segmented sample (ground truth), which suggests that the personalized application of our deep learning model, together with the TALANDCOVER dataset offers different possibilities that facilitate the training of deep architectures for the classification of large-scale covers in complex areas, such as the tropical Andes. This advance has significant potential for decision making, emphasizing sustainable land use and the conservation of natural resources.<\/jats:p>","DOI":"10.3390\/data8120185","type":"journal-article","created":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T03:40:58Z","timestamp":1701661258000},"page":"185","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Land Cover Classification in the Antioquia Region of the Tropical Andes Using NICFI Satellite Data Program Imagery and Semantic Segmentation Techniques"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9971-4908","authenticated-orcid":false,"given":"Luisa F.","family":"Gomez-Ossa","sequence":"first","affiliation":[{"name":"Facultad de Minas, Universidad Nacional de Colombia, Medell\u00edn 050041, Colombia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9069-0732","authenticated-orcid":false,"given":"German","family":"Sanchez-Torres","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00edas, Universidad del Magdalena, Santa Marta 470004, Colombia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0378-028X","authenticated-orcid":false,"given":"John W.","family":"Branch-Bedoya","sequence":"additional","affiliation":[{"name":"Facultad de Minas, Universidad Nacional de Colombia, Medell\u00edn 050041, Colombia"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4772","DOI":"10.1016\/j.matpr.2022.03.341","article-title":"A Review on Remote Sensing Imagery Augmentation Using Deep Learning","volume":"62","author":"Lalitha","year":"2022","journal-title":"Mater. Today Proc."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"114417","DOI":"10.1016\/j.eswa.2020.114417","article-title":"A Review of Deep Learning Methods for Semantic Segmentation of Remote Sensing Imagery","volume":"169","author":"Yuan","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1016\/j.ecoleng.2016.11.047","article-title":"Modeling the Spatial Dynamics of Deforestation and Fragmentation Using Multi-Layer Perceptron Neural Network and Landscape Fragmentation Tool","volume":"99","author":"Singh","year":"2017","journal-title":"Ecol. Eng."},{"key":"ref_4","first-page":"100804","article-title":"Analyzing Land Use and Land Cover Change Patterns and Population Dynamics of Fast-Growing US Cities: Evidence from Collin County, Texas","volume":"27","author":"Zhang","year":"2022","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_5","first-page":"341","article-title":"Development of a Map for Land Use and Land Cover Classification of the Northern Border Region Using Remote Sensing and GIS","volume":"26","author":"Darem","year":"2023","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_6","first-page":"100843","article-title":"Urban Land Use and Land Cover Classification with Interpretable Machine Learning\u2014A Case Study Using Sentinel-2 and Auxiliary Data","volume":"28","author":"Hosseiny","year":"2022","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Parente, L., Taquary, E., Silva, A.P., Souza, C., and Ferreira, L. (2019). Next Generation Mapping: Combining Deep Learning, Cloud Computing, and Big Remote Sensing Data. Remote Sens., 11.","DOI":"10.3390\/rs11232881"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"112780","DOI":"10.1016\/j.rse.2021.112780","article-title":"Land Cover Classification in an Era of Big and Open Data: Optimizing Localized Implementation and Training Data Selection to Improve Mapping Outcomes","volume":"268","author":"Hermosilla","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1080\/10095020.2017.1333230","article-title":"Earth Observation in Service of the 2030 Agenda for Sustainable Development","volume":"20","author":"Anderson","year":"2017","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Holloway, J., and Mengersen, K. (2018). Statistical Machine Learning Methods and Remote Sensing for Sustainable Development Goals: A Review. Remote Sens., 10.","DOI":"10.3390\/rs10091365"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isprsjprs.2019.04.015","article-title":"Deep Learning in Remote Sensing Applications: A Meta-Analysis and Review","volume":"152","author":"Ma","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","unstructured":"Potsdam, I. (2023, June 24). 2D Semantic Labeling Contest\u2014Potsdam 2019. ISPRS Potsdam 2D Semantic Labeling Dataset. Available online: https:\/\/www.isprs.org\/education\/benchmarks\/UrbanSemLab\/2d-sem-label-potsdam.aspx."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1080\/23766808.2016.1248710","article-title":"Effects of Climate Change on Andean Biodiversity: A Synthesis of Studies Published until 2015","volume":"2","author":"Jaramillo","year":"2016","journal-title":"Neotrop. Biodivers."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"eabg1620","DOI":"10.1126\/sciadv.abg1620","article-title":"Rapid Expansion of Human Impact on Natural Land in South America since 1985","volume":"7","author":"Zalles","year":"2021","journal-title":"Sci. Adv."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.tplants.2021.09.010","article-title":"The Andes through Time: Evolution and Distribution of Andean Floras","volume":"27","author":"Zizka","year":"2022","journal-title":"Trends Plant Sci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Schmitt, M., Hughes, L.H., Qiu, C., and Zhu, X.X. (2019). SEN12MS\u2014A Curated Dataset of Georeferenced Multi-Spectral Sentinel-1\/2 Imagery for Deep Learning and Data Fusion. arXiv.","DOI":"10.5194\/isprs-annals-IV-2-W7-153-2019"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Sumbul, G., Charfuelan, M., Demir, B., and Markl, V. (August, January 28). Bigearthnet: A Large-Scale Benchmark Archive for Remote Sensing Image Understanding. Proceedings of the IGARSS 2019\u20142019 IEEE International Geoscience and Remote Sensing Symposium 2019, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8900532"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Qi, X., Zhu, P., Wang, Y., Zhang, L., Peng, J., Wu, M., Chen, J., Zhao, X., Zang, N., and Mathiopoulos, P.T. (2020). MLRSNet: A Multi-Label High Spatial Resolution Remote Sensing Dataset for Semantic Scene Understanding. arXiv.","DOI":"10.1016\/j.isprsjprs.2020.09.020"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"11709","DOI":"10.1109\/TCYB.2021.3070577","article-title":"A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification","volume":"52","author":"Zhu","year":"2022","journal-title":"IEEE Trans. Cybern."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"104384","DOI":"10.1016\/j.landurbplan.2022.104384","article-title":"Large-Scale Automatic Identification of Urban Vacant Land Using Semantic Segmentation of High-Resolution Remote Sensing Images","volume":"222","author":"Mao","year":"2022","journal-title":"Landsc. Urban Plan."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"116210","DOI":"10.1016\/j.eswa.2021.116210","article-title":"Deep-Learning-Based Solution for Data Deficient Satellite Image Segmentation","volume":"191","author":"Yeung","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_23","unstructured":"(2023, June 24). ISPRS Vaihingen 2D Semantic Labeling Dataset. Available online: https:\/\/www.isprs.org\/education\/benchmarks\/UrbanSemLab\/2d-sem-label-vaihingen.aspx."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Van Etten, A. (2019, January 7\u201311). Satellite Imagery Multiscale Rapid Detection with Windowed Networks. Proceedings of the 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA.","DOI":"10.1109\/WACV.2019.00083"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Toker, A., Kondmann, L., Weber, M., Eisenberger, M., Camero, A., Hu, J., Hoderlein, A.P., \u015eenaras, \u00c7., Davis, T., and Cremers, D. (2022, January 18\u201324). DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.02048"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1038\/s41597-022-01307-4","article-title":"Dynamic World, Near Real-Time Global 10 m Land Use Land Cover Mapping","volume":"9","author":"Brown","year":"2022","journal-title":"Sci. Data"},{"key":"ref_27","unstructured":"Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N., Brockmann, C., Quast, R., Wevers, J., Grosu, A., Paccini, A., and Vergnaud, S. (2021). ESA WorldCover 10 m 2020 V100. Zenodo."},{"key":"ref_28","unstructured":"Bragagnolo, L., da Silva, R.V., and Grzybowski, J.M.V. (2021). Amazon and Atlantic Forest Image Datasets for Semantic Segmentation. Zenodo."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"106381","DOI":"10.1016\/j.ocecoaman.2022.106381","article-title":"Deep Semantic Segmentation of Mangroves in Brazil Combining Spatial, Temporal, and Polarization Data from Sentinel-1 Time Series","volume":"231","author":"Andrade","year":"2023","journal-title":"Ocean Coast. Manag."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"499","DOI":"10.2307\/2339293","article-title":"On the Probable Errors of Frequency-Constants (Contd.)","volume":"71","author":"Edgeworth","year":"1908","journal-title":"J. R. Stat. Soc."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1016\/j.procs.2018.10.434","article-title":"Investigation on Land Cover Mapping Capability of Maximum Likelihood Classifier: A Case Study on North Canara, India","volume":"143","author":"Shivakumar","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_32","first-page":"100859","article-title":"Geospatial-Based Machine Learning Techniques for Land Use and Land Cover Mapping Using a High-Resolution Unmanned Aerial Vehicle Image","volume":"29","author":"Mollick","year":"2023","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.geog.2022.09.002","article-title":"Spatiotemporal Detection of Land Use\/Land Cover Changes and Land Surface Temperature Using Landsat and MODIS Data across the Coastal Kanyakumari District, India","volume":"14","author":"Sam","year":"2023","journal-title":"Geod. Geodyn."},{"key":"ref_34","first-page":"S27","article-title":"Land Cover Change Assessment Using Decision Trees, Support Vector Machines and Maximum Likelihood Classification Algorithms","volume":"12","author":"Otukei","year":"2010","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"139197","DOI":"10.1016\/j.scitotenv.2020.139197","article-title":"Predicting the Deforestation Probability Using the Binary Logistic Regression, Random Forest, Ensemble Rotational Forest, REPTree: A Case Study at the Gumani River Basin, India","volume":"730","author":"Saha","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Mangkhaseum, S., and Hanazawa, A. (2021, January 3\u20134). Comparison of Machine Learning Classifiers for Land Cover Changes Using Google Earth Engine. Proceedings of the 2021 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES), Bali, Indonesia.","DOI":"10.1109\/ICARES53960.2021.9665186"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"15365","DOI":"10.1038\/s41598-021-94422-y","article-title":"Semantic Segmentation of PolSAR Image Data Using Advanced Deep Learning Model","volume":"11","author":"Garg","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Palanisamy, P.A., Jain, K., and Bonafoni, S. (2023). Machine Learning Classifier Evaluation for Different Input Combinations: A Case Study with Landsat 9 and Sentinel-2 Data. Remote Sens., 15.","DOI":"10.3390\/rs15133241"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"101955","DOI":"10.1016\/j.ecoinf.2022.101955","article-title":"Application of Machine Learning Approaches for Land Cover Monitoring in Northern Cameroon","volume":"74","author":"Yuh","year":"2023","journal-title":"Ecol. Inform."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_41","first-page":"103092","article-title":"Comparison of Common Classification Strategies for Large-Scale Vegetation Mapping over the Google Earth Engine Platform","volume":"115","author":"Valle","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"112105","DOI":"10.1016\/j.rse.2020.112105","article-title":"Improving Land Cover Classification in an Urbanized Coastal Area by Random Forests: The Role of Variable Selection","volume":"251","author":"Zhang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_43","first-page":"102475","article-title":"Identifying Core Driving Factors of Urban Land Use Change from Global Land Cover Products and POI Data Using the Random Forest Method","volume":"103","author":"Wu","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Amorim, F.d.L.L.d., Rick, J., Lohmann, G., and Wiltshire, K.H. (2021). Evaluation of Machine Learning Predictions of a Highly Resolved Time Series of Chlorophyll-a Concentration. Appl. Sci., 11.","DOI":"10.3390\/app11167208"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2600","DOI":"10.1109\/JSTARS.2018.2835377","article-title":"Building Extraction at Scale Using Convolutional Neural Network: Mapping of the United States","volume":"11","author":"Yang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1685","DOI":"10.1109\/LGRS.2017.2728698","article-title":"Land Cover Classification via Multitemporal Spatial Data by Deep Recurrent Neural Networks","volume":"14","author":"Ienco","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1016\/j.asoc.2018.08.049","article-title":"Hyperspectral Image Classification Using K-Sparse Denoising Autoencoder and Spectral\u2013Restricted Spatial Characteristics","volume":"74","author":"Lan","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"ref_48","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_49","doi-asserted-by":"crossref","unstructured":"Sol\u00f3rzano, J.V., Mas, J.F., Gao, Y., and Gallardo-Cruz, J.A. (2021). Land Use Land Cover Classification with U-Net: Advantages of Combining Sentinel-1 and Sentinel-2 Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13183600"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Navab, N., Hornegger, J., Wells, W.M., and Frangi, A.F. U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015.","DOI":"10.1007\/978-3-319-24553-9"},{"key":"ref_51","first-page":"101897","article-title":"Using a U-Net Convolutional Neural Network to Map Woody Vegetation Extent from High Resolution Satellite Imagery across Queensland, Australia","volume":"82","author":"Flood","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Mazza, A., Sica, F., Rizzoli, P., and Scarpa, G. (2019). TanDEM-X Forest Mapping Using Convolutional Neural Networks. Remote Sens., 11.","DOI":"10.3390\/rs11242980"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Stoian, A., Poulain, V., Inglada, J., Poughon, V., and Derksen, D. (2019). Land Cover Maps Production with High Resolution Satellite Image Time Series and Convolutional Neural Networks: Adaptations and Limits for Operational Systems. Remote Sens., 11.","DOI":"10.20944\/preprints201906.0270.v1"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"113332","DOI":"10.1016\/j.rse.2022.113332","article-title":"Optimizing WorldView-2, -3 Cloud Masking Using Machine Learning Approaches","volume":"284","author":"Carroll","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"108734","DOI":"10.1016\/j.compeleceng.2023.108734","article-title":"High-Resolution Remote Sensing Images Semantic Segmentation Using Improved UNet and SegNet","volume":"108","author":"Wang","year":"2023","journal-title":"Comput. Electr. Eng."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Wagner, F.H., Dalagnol, R., Tagle Casapia, X., Streher, A.S., Phillips, O.L., Gloor, E., and Arag\u00e3o, L.E.O.C. (2020). Regional Mapping and Spatial Distribution Analysis of Canopy Palms in an Amazon Forest Using Deep Learning and VHR Images. Remote Sens., 12.","DOI":"10.3390\/rs12142225"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"186257","DOI":"10.1109\/ACCESS.2020.3030112","article-title":"U-Net Convolutional Networks for Mining Land Cover Classification Based on High-Resolution UAV Imagery","volume":"8","author":"Giang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Du, L., McCarty, G.W., Zhang, X., Lang, M.W., Vanderhoof, M.K., Li, X., Huang, C., Lee, S., and Zou, Z. (2020). Mapping Forested Wetland Inundation in the Delmarva Peninsula, USA Using Deep Convolutional Neural Networks. Remote Sens., 12.","DOI":"10.3390\/rs12040644"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of Deep Learning: Concepts, CNN Architectures, Challenges, Applications, Future Directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J. Big Data"},{"key":"ref_60","unstructured":"Xie, Q., Dai, Z., Hovy, E., Luong, M.-T., and Le, Q.V. (2020). Unsupervised Data Augmentation for Consistency Training. arXiv."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Cubuk, E.D., Zoph, B., Mane, D., Vasudevan, V., and Le, Q.V. (2019). AutoAugment: Learning Augmentation Policies from Data. arXiv.","DOI":"10.1109\/CVPR.2019.00020"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Awuah, K.T., and Aplin, P. (2021, January 11\u201316). Fusion of Sentinel-2 Data with High Resolution Open Access Planet Basemaps for Grazing Lawn Detection in Southern African Savannahs. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9554156"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Vizzari, M. (2022). PlanetScope, Sentinel-2, and Sentinel-1 Data Integration for Object-Based Land Cover Classification in Google Earth Engine. Remote Sens., 14.","DOI":"10.3390\/rs14112628"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"101522","DOI":"10.1016\/j.ecoinf.2021.101522","article-title":"Evaluation and Comparison of the Earth Observing Sensors in Land Cover\/Land Use Studies Using Machine Learning Algorithms","volume":"68","author":"Prasad","year":"2022","journal-title":"Ecol. Inform."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Heckel, K., Urban, M., Schratz, P., Mahecha, M.D., and Schmullius, C. (2020). Predicting Forest Cover in Distinct Ecosystems: The Potential of Multi-Source Sentinel-1 and -2 Data Fusion. Remote Sens., 12.","DOI":"10.3390\/rs12020302"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"114804","DOI":"10.1016\/j.jenvman.2022.114804","article-title":"High-Resolution Planet Satellite Imagery and Multi-Temporal Surveys to Predict Risk of Tree Mortality in Tropical Eucalypt Forestry","volume":"310","author":"Pascual","year":"2022","journal-title":"J. Environ. Manag."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"2258","DOI":"10.1038\/s41467-023-37880-4","article-title":"More than One Quarter of Africa\u2019s Tree Cover Is Found Outside Areas Previously Classified as Forest","volume":"14","author":"Reiner","year":"2023","journal-title":"Nat. Commun."},{"key":"ref_68","first-page":"103152","article-title":"A Super-Ensemble Approach to Map Land Cover Types with High Resolution over Data-Sparse African Savanna Landscapes","volume":"116","author":"Song","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_69","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., and Lo, W.-Y. (2023). Segment Anything 2023. arXiv."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Osco, L.P., Wu, Q., de Lemos, E.L., Gon\u00e7alves, W.N., Ramos, A.P.M., Li, J., and Junior, J.M. (2023). The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shot 2023. arXiv.","DOI":"10.1016\/j.jag.2023.103540"},{"key":"ref_71","unstructured":"Quintero-Vallejo, E., Benavides, A.M., Moreno, N., and Gonz\u00e1lez-Caro, S. (2017). Bosques Andinos, Estado Actual y Retos Para Su Conservaci\u00f3n En Antioquia, Fundaci\u00f3n Jard\u00edn Bot\u00e1nico de Medell\u00edn Joaqu\u00edn Antonio Uribe\u2014Programa Bosques Andinos (COSUDE). [1st ed.]."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"68","DOI":"10.15446\/dyna.v84n201.54310","article-title":"Application of Artificial Neural Networks in Modeling Deforestation Associated with New Road Infrastructure Projects","volume":"84","year":"2017","journal-title":"Dyna"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Ibrahim, E., Jiang, J., Lema, L., Barnab\u00e9, P., Giuliani, G., Lacroix, P., and Pirard, E. (2021). Cloud and Cloud-Shadow Detection for Applications in Mapping Small-Scale Mining in Colombia Using Sentinel-2 Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13040736"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Brovelli, M.A., Sun, Y., and Yordanov, V. (2020). Monitoring Forest Change in the Amazon Using Multi-Temporal Remote Sensing Data and Machine Learning Classification on Google Earth Engine. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9100580"},{"key":"ref_75","unstructured":"Norway\u2019s International Climate and Forests Initiative (NICFI) (2022). NICFI Satellite Data Program User Guide, Norway\u2019s International Climate and Forests Initiative (NICFI)."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"534696","DOI":"10.3389\/frai.2020.534696","article-title":"Deep Learning for Understanding Satellite Imagery: An Experimental Survey","volume":"3","author":"Mohanty","year":"2020","journal-title":"Front. Artif. Intell."},{"key":"ref_77","first-page":"7288","article-title":"Mapping High-Resolution Global Impervious Surface Area: Status and Trends","volume":"15","author":"Ren","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Karra, K., Kontgis, C., Statman-Weil, Z., Mazzariello, J., Mark, M., and Brumby, S. (2021, January 11\u201316). Impact Observatory, United States Global. Land Use\/Land Cover with Sentinel-2 and Deep Learning. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9553499"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"034050","DOI":"10.1088\/1748-9326\/ac46ec","article-title":"Global Land Use Extent and Dispersion within Natural Land Cover Using Landsat Data","volume":"17","author":"Hansen","year":"2022","journal-title":"Environ. Res. Lett."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/j.rse.2017.12.002","article-title":"The Global Forest\/Non-Forest Map from TanDEM-X Interferometric SAR Data","volume":"205","author":"Martone","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1126\/science.1244693","article-title":"High-Resolution Global Maps of 21st-Century Forest Cover Change","volume":"342","author":"Hansen","year":"2013","journal-title":"Science"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.rse.2014.04.014","article-title":"New Global Forest\/Non-Forest Maps from ALOS PALSAR Data (2007\u20132010)","volume":"155","author":"Shimada","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"112997","DOI":"10.1016\/j.rse.2022.112997","article-title":"Towards Accurate Mapping of Forest in Tropical Landscapes: A Comparison of Datasets on How Forest Transition Matters","volume":"274","author":"Velasco","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.isprsjprs.2020.01.013","article-title":"ResUNet-a: A Deep Learning Framework for Semantic Segmentation of Remotely Sensed Data","volume":"162","author":"Diakogiannis","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_85","unstructured":"Murphy, K.P. (2013). Machine Learning a Probabilistic Perspective, MIT Press."},{"key":"ref_86","unstructured":"Chollet, F. (2023, June 24). Keras\u2014Deep Learning Library 2015. Available online: https:\/\/keras.io\/."},{"key":"ref_87","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2016). TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems 2015. arXiv."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"105524","DOI":"10.1016\/j.asoc.2019.105524","article-title":"Investigating the Impact of Data Normalization on Classification Performance","volume":"97","author":"Singh","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_89","unstructured":"IDEAM Leyenda Nacional de Coberturas de La Tierra (2010). Metodolog\u00eda CORINE Land Cover Adaptada Para Colombia Escala 1:100.000, Instituto de Hidrolog\u00eda, Meteorolog\u00eda y Estudios Ambientales."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"4711","DOI":"10.5194\/bg-14-4711-2017","article-title":"Challenges and Opportunities in Land Surface Modelling of Savanna Ecosystems","volume":"14","author":"Whitley","year":"2017","journal-title":"Biogeosciences"},{"key":"ref_91","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_92","doi-asserted-by":"crossref","first-page":"e1264","DOI":"10.1002\/widm.1264","article-title":"Deep Learning for Remote Sensing Image Classification: A Survey","volume":"8","author":"Li","year":"2018","journal-title":"WIREs Data Min. Knowl. Discov."}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/8\/12\/185\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:37:06Z","timestamp":1760132226000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/8\/12\/185"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,4]]},"references-count":92,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["data8120185"],"URL":"https:\/\/doi.org\/10.3390\/data8120185","relation":{},"ISSN":["2306-5729"],"issn-type":[{"value":"2306-5729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,4]]}}}