{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T05:51:47Z","timestamp":1772776307796,"version":"3.50.1"},"reference-count":62,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2019,12,3]],"date-time":"2019-12-03T00:00:00Z","timestamp":1575331200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000936","name":"Gordon and Betty Moore Foundation","doi-asserted-by":"publisher","award":["GBMF6898"],"award-info":[{"award-number":["GBMF6898"]}],"id":[{"id":"10.13039\/100000936","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The rapid growth of satellites orbiting the planet is generating massive amounts of data for Earth science applications. Concurrently, state-of-the-art deep-learning-based algorithms and cloud computing infrastructure have become available with a great potential to revolutionize the image processing of satellite remote sensing. Within this context, this study evaluated, based on thousands of PlanetScope images obtained over a 12-month period, the performance of three machine learning approaches (random forest, long short-term memory-LSTM, and U-Net). We applied these approaches to mapped pasturelands in a Central Brazil region. The deep learning algorithms were implemented using TensorFlow, while the random forest utilized the Google Earth Engine platform. The accuracy assessment presented F1 scores for U-Net, LSTM, and random forest of, respectively, 96.94%, 98.83%, and 95.53% in the validation data, and 94.06%, 87.97%, and 82.57% in the test data, indicating a better classification efficiency using the deep learning approaches. Although the use of deep learning algorithms depends on a high investment in calibration samples and the generalization of these methods requires further investigations, our results suggest that the neural network architectures developed in this study can be used to map large geographic regions that consider a wide variety of satellite data (e.g., PlanetScope, Sentinel-2, Landsat-8).<\/jats:p>","DOI":"10.3390\/rs11232881","type":"journal-article","created":{"date-parts":[[2019,12,4]],"date-time":"2019-12-04T04:30:35Z","timestamp":1575433835000},"page":"2881","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":64,"title":["Next Generation Mapping: Combining Deep Learning, Cloud Computing, and Big Remote Sensing Data"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1589-0467","authenticated-orcid":false,"given":"Leandro","family":"Parente","sequence":"first","affiliation":[{"name":"Image Processing and GIS Laboratory (LAPIG), Federal University of Goi\u00e1s (UFG), Goi\u00e2nia-GO 74001-970, Brazil"}]},{"given":"Evandro","family":"Taquary","sequence":"additional","affiliation":[{"name":"Image Processing and GIS Laboratory (LAPIG), Federal University of Goi\u00e1s (UFG), Goi\u00e2nia-GO 74001-970, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8000-3046","authenticated-orcid":false,"given":"Ana","family":"Silva","sequence":"additional","affiliation":[{"name":"Image Processing and GIS Laboratory (LAPIG), Federal University of Goi\u00e1s (UFG), Goi\u00e2nia-GO 74001-970, Brazil"}]},{"given":"Carlos","family":"Souza","sequence":"additional","affiliation":[{"name":"IMAZON \u2013 Amazon Institute of People and the Environment, Bel\u00e9m-PA 66055-200, Brazil"}]},{"given":"Laerte","family":"Ferreira","sequence":"additional","affiliation":[{"name":"Image Processing and GIS Laboratory (LAPIG), Federal University of Goi\u00e1s (UFG), Goi\u00e2nia-GO 74001-970, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"45","DOI":"10.3389\/fenvs.2015.00045","article-title":"A survey of remote-sensing big data","volume":"3","author":"Liu","year":"2015","journal-title":"Front. Environ. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.rse.2018.02.067","article-title":"A Cubesat enabled Spatio-Temporal Enhancement Method (CESTEM) utilizing Planet, Landsat and MODIS data","volume":"209","author":"Houborg","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1109\/JPROC.2016.2598228","article-title":"Big Data for Remote Sensing: Challenges and Opportunities","volume":"104","author":"Chi","year":"2016","journal-title":"Proc. IEEE"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1029\/2011EO130001","article-title":"Collaborative supercomputing for global change science","volume":"92","author":"Nemani","year":"2011","journal-title":"Eos Trans. Am. Geophys. Union"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1154","DOI":"10.1016\/j.future.2017.02.044","article-title":"A cloud-based remote sensing data production system","volume":"86","author":"Yan","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/j.future.2016.06.009","article-title":"pipsCloud: High performance cloud computing for remote sensing big data management and processing","volume":"78","author":"Wang","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1038\/nature20584","article-title":"High-resolution mapping of global surface water and its long-term changes","volume":"540","author":"Pekel","year":"2016","journal-title":"Nature"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2607","DOI":"10.1080\/01431161.2012.748992","article-title":"Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data","volume":"34","author":"Gong","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1126\/science.1244693","article-title":"High-Resolution Global Maps of","volume":"850","author":"Hansen","year":"2013","journal-title":"Science"},{"key":"ref_11","first-page":"110","article-title":"Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using a random forest classifier on the Google Earth Engine Cloud","volume":"81","author":"Oliphant","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6185","DOI":"10.1080\/01431161.2019.1587207","article-title":"Comparisons of three recent moderate resolution African land cover datasets: CGLS-LC100, ESA-S2-LC20, and FROM-GLC-Africa30","volume":"40","author":"Xu","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.rse.2017.08.027","article-title":"Detection of cropland field parcels from Landsat imagery","volume":"201","author":"Graesser","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.rse.2018.10.031","article-title":"Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping","volume":"220","author":"Griffiths","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"111301","DOI":"10.1016\/j.rse.2019.111301","article-title":"Assessing the pasturelands and livestock dynamics in Brazil, from 1985 to 2017: A novel approach based on high spatial resolution imagery and Google Earth Engine cloud computing","volume":"232","author":"Parente","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.rse.2015.10.034","article-title":"Conterminous United States crop field size quantification from multi-temporal Landsat data","volume":"172","author":"Yan","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.rse.2011.08.024","article-title":"A review of large area monitoring of land cover change using Landsat data","volume":"122","author":"Hansen","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1126\/science.aaa8415","article-title":"Machine learning: Trends, perspectives, and prospects","volume":"349","author":"Jordan","year":"2015","journal-title":"Science"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","first-page":"394","article-title":"Supervised Machine Learning: A Review of Classification Techniques S","volume":"125","author":"Kotsiantis","year":"2007","journal-title":"J. Manuf. Sci. Eng. Trans. ASME"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1016\/j.rse.2018.11.032","article-title":"Deep learning based multi-temporal crop classification","volume":"221","author":"Zhong","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_22","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_23","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_24","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_25","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1109\/JSTARS.2014.2329330","article-title":"Deep learning-based classification of hyperspectral data","volume":"7","author":"Chen","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","unstructured":"Gerke, M. (2015). Use of the Stair Vision Library within the ISPRS Use of the Stair Vision Library within the ISPRS 2D, ResearcheGate."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yang, Y., and Newsam, S. (2010, January 2\u20135). Bag-Of-Visual-Words and Spatial Extensions for Land-Use Classificatio. Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, San Jose, CA, USA.","DOI":"10.1145\/1869790.1869829"},{"key":"ref_28","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.v2"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/MGRS.2016.2641240","article-title":"Remote Sensing Image Classification: A survey of support-vector-machine-based advanced techniques","volume":"5","author":"Maulik","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"111199","DOI":"10.1016\/j.rse.2019.05.018","article-title":"Key issues in rigorous accuracy assessment of land cover products","volume":"231","author":"Stehman","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_31","first-page":"197","article-title":"Parallel processing of massive remote sensing images in a GPU architecture","volume":"33","author":"Liu","year":"2014","journal-title":"Comput. Inform."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1007\/s00607-014-0392-y","article-title":"Parallel programing templates for remote sensing image processing on GPU architectures: Design and implementation","volume":"98","author":"Ma","year":"2016","journal-title":"Computing"},{"key":"ref_33","first-page":"135","article-title":"Monitoring the brazilian pasturelands: A new mapping approach based on the landsat 8 spectral and temporal domains","volume":"62","author":"Parente","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Saraiva, M., Silva, D., Ferreira, L., Galano, S., Siqueira, J., and Souza, C. (2019). Constru\u00e7\u00e3o De Mosaicos Temporais Normalizados De Imagens Planet. Proceedings of the XIX Brazilian Symposium on Remote Sensing, INPE.","DOI":"10.29327\/xix-sbsr.a1"},{"key":"ref_35","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_36","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Schmidhuber","year":"1997","journal-title":"Neural Comput."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","article-title":"U-net: Convolutional networks for biomedical image segmentation","volume":"9351","author":"Ronneberger","year":"2015","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_38","unstructured":"Diretoria de Forma\u00e7\u00e3o e Aperfei\u00e7oamento de Pessoal, B.C. (1983). Mapas e Outros Materiais Cartogr\u00e1ficos na Biblioteca Central do IBGE, IBGE."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"016011","DOI":"10.1117\/1.JRS.10.016011","article-title":"Comparison of performance of object-based image analysis techniques available in open source software (Spring and Orfeo Toolbox\/Monteverdi) considering very high spatial resolution data","volume":"10","author":"Teodoro","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_40","first-page":"2596","article-title":"Analysis of The Implementation Quantum GIS: Comparative Rffect and User Performance","volume":"97","author":"Jaya","year":"2019","journal-title":"J. Theor. Appl. Inf. Technol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1016\/S0034-4257(96)00112-5","article-title":"A Comparison of Vegetation Indices over a Global Set of TM Images for EOS-MODIS","volume":"59","author":"Huete","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1007\/s004420050337","article-title":"The photochemical reflectance index: An optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels","volume":"112","author":"Gamon","year":"1997","journal-title":"Oecologia"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features","volume":"17","author":"Mcfeeters","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Parente, L., and Ferreira, L. (2018). Assessing the spatial and occupation dynamics of the Brazilian pasturelands based on the automated classification of MODIS images from 2000 to 2016. Remote Sens., 10.","DOI":"10.3390\/rs10040606"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"8076","DOI":"10.1080\/01431161.2018.1539275","article-title":"Crop classification with WorldView-2 imagery using Support Vector Machine comparing texture analysis approaches and grey relational analysis in Jianan Plain, Taiwan","volume":"40","author":"Wan","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Li, W., He, C., Fang, J., Zheng, J., Fu, H., and Yu, L. (2019). Semantic segmentation-based building footprint extraction using very high-resolution satellite images and multi-source GIS data. Remote Sens., 11.","DOI":"10.3390\/rs11040403"},{"key":"ref_47","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv."},{"key":"ref_48","first-page":"345","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_49","unstructured":"Dozat, T. (2016, January 2\u20134). Incorporating Nesterov Momentum into Adam. Proceedings of the ICLR Workshop, San Juan, Puerto Rico."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Dong, S., and Kaeli, D. (2017, January 4\u20138). DNNMark: A deep neural network benchmark suite for GPUs. Proceedings of the General Purpose GPUs, Austin, TX, USA.","DOI":"10.1145\/3038228.3038239"},{"key":"ref_51","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2\u20134). TensorFlow: A System for Large-Scale Machine Learning. Proceedings of the 12th Symposium on Operating Systems Design and Implementation, Savannah, GA, USA."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"950","DOI":"10.1111\/tgis.12068","article-title":"How to Apply the Geospatial Data Abstraction Library (GDAL) Properly to Parallel Geospatial Raster I\/O?","volume":"18","author":"Qin","year":"2014","journal-title":"Trans. GIS"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Jaiswal, S., Mehta, A., and Nandi, G.C. (2019, January 14\u201315). Investigation on the Effect of L1 an L2 Regularization on Image Features Extracted using Restricted Boltzmann Machine. Proceedings of the Second International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India.","DOI":"10.1109\/ICCONS.2018.8663071"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Rahman, M.A., and Wang, Y. (2016). Optimizing intersection-over-union in deep neural networks for image segmentation. International Symposium on Visual Computing, Springer.","DOI":"10.1007\/978-3-319-50835-1_22"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Hadad, O., Bakalo, R., Ben-Ari, R., Hashoul, S., and Amit, G. (2017, January 18\u201321). Classification of breast lesions using cross-modal deep learning. Proceedings of the IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, Australia.","DOI":"10.1109\/ISBI.2017.7950480"},{"key":"ref_56","first-page":"559","article-title":"Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning","volume":"18","author":"Nogueira","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1134\/S1054661814010155","article-title":"Detecting clusters of specified separability for multispectral data on various hierarchical levels","volume":"24","author":"Sidorova","year":"2014","journal-title":"Pattern Recognit. Image Anal."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.rse.2006.04.001","article-title":"The use of small training sets containing mixed pixels for accurate hard image classification: Training on mixed spectral responses for classification by a SVM","volume":"103","author":"Foody","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Berhane, T.M., Costa, H., Lane, C.R., Anenkhonov, O.A., Chepinoga, V.V., and Autrey, B.C. (2019). The influence of region of interest heterogeneity on classification accuracy in wetland systems. Remote Sens., 11.","DOI":"10.3390\/rs11050551"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1016\/j.rse.2007.05.018","article-title":"Hierarchical image segmentation based on similarity of NDVI time series","volume":"112","author":"Lhermitte","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.rse.2017.09.022","article-title":"Separate segmentation of multi-temporal high-resolution remote sensing images for object-based change detection in urban area","volume":"201","author":"Zhang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1002\/cpe.1058","article-title":"The Parallel Image Processing Environment (PIPE): Automated parallelization of satellite data analyses","volume":"19","author":"Simpson","year":"2007","journal-title":"Concurr. Comput. Pract. Exp."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/23\/2881\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:39:52Z","timestamp":1760189992000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/23\/2881"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,3]]},"references-count":62,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["rs11232881"],"URL":"https:\/\/doi.org\/10.3390\/rs11232881","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,3]]}}}