{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T04:40:34Z","timestamp":1775882434191,"version":"3.50.1"},"reference-count":96,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T00:00:00Z","timestamp":1653955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>PlanetScope (PL) high-resolution composite base maps have recently become available within Google Earth Engine (GEE) for the tropical regions thanks to the partnership between Google and the Norway\u2019s International Climate and Forest Initiative (NICFI). Object-based (OB) image classification in the GEE environment has increased rapidly due to the broadly recognized advantages of applying these approaches to medium- and high-resolution images. This work aimed to assess the advantages for land cover classification of (a) adopting an OB approach with PL data; and (b) integrating the PL datasets with Sentinel 2 and Sentinel 1 data both in Pixel-based (PB) or OB approaches. For this purpose, in this research, we compared ten LULC classification approaches (PB and OB, all based on the Random Forest (RF) algorithm), where the three satellite datasets were used according to different levels of integration and combination. The study area, which is 69,272 km2 wide and located in central Brazil, was selected within the tropical region, considering a preliminary availability of sample points and its complex landscape mosaic composed of heterogeneous agri-natural spaces, including scattered settlements. Using only the PL dataset with a typical RF PB approach produced the worse overall accuracy (OA) results (67%), whereas adopting an OB approach for the same dataset yielded very good OA (82%). The integration of PL data with the S2 and S1 datasets improved both PB and OB overall accuracy outputs (82 vs. 67% and 91 vs. 82%, respectively). Moreover, this research demonstrated the OB approaches\u2019 applicability in GEE, even in vast study areas and using high-resolution imagery. Although additional applications are necessary, the proposed methodology appears to be very promising for properly exploiting the potential of PL data in GEE.<\/jats:p>","DOI":"10.3390\/rs14112628","type":"journal-article","created":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T03:33:18Z","timestamp":1654054398000},"page":"2628","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":77,"title":["PlanetScope, Sentinel-2, and Sentinel-1 Data Integration for Object-Based Land Cover Classification in Google Earth Engine"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4238-8897","authenticated-orcid":false,"given":"Marco","family":"Vizzari","sequence":"first","affiliation":[{"name":"Department of Agricultural, Food and Environmental Sciences, University of Perugia, 06121 Perugia, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4035","DOI":"10.1080\/0143116031000103853","article-title":"Remote sensing of tropical forest environments: Towards the monitoring of environmental resources for sustainble development","volume":"24","author":"Foody","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1016\/S0169-5347(97)85755-2","article-title":"Last stand: Protected areas and the defense of tropical diversity","volume":"12","author":"Noss","year":"1997","journal-title":"Trends Ecol. Evol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.apgeog.2006.09.004","article-title":"Remote sensing and GIS for mapping and monitoring land cover and land-use changes in the Northwestern coastal zone of Egypt","volume":"27","author":"Shalaby","year":"2007","journal-title":"Appl. Geogr."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Gervasi, O., Murgante, B., Misra, S., Gavrilova, L.M., Rocha, C.A.M.A., Torre, C., Taniar, D., and Apduhan, O.B. (2015). Ecosystem Services Along the Urban\u2014Rural\u2014Natural Gradient: An Approach for a Wide Area Assessment and Mapping. Proceedings of the Part III, Computational Science and Its Applications\u2014ICCSA 2015: 15th International Conference, Banff, AB, Canada, 22\u201325 June 2015, Springer International Publishing.","DOI":"10.1007\/978-3-319-21470-2"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"453","DOI":"10.4081\/jae.2013.333","article-title":"Urban-rural gradient detection using multivariate spatial analysis and landscape metrics","volume":"44","author":"Vizzari","year":"2013","journal-title":"J. Agric. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.apgeog.2014.09.014","article-title":"Land use and land cover changes in the Brazilian Cerrado: A multidisciplinary approach to assess the impacts of agricultural expansion","volume":"55","author":"Grecchi","year":"2014","journal-title":"Appl. Geogr."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Pelorosso, R., Apollonio, C., Rocchini, D., and Petroselli, A. (2021). Effects of Land Use-Land Cover Thematic Resolution on Environ-mental Evaluations. Remote Sens., 13.","DOI":"10.3390\/rs13071232"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/feart.2017.00017","article-title":"Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping","volume":"5","author":"Shelestov","year":"2017","journal-title":"Front. Earth Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Remote Sensing of Environment Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_10","unstructured":"Sullivan, B. (2022, February 07). NICFI\u2019s Satellite Imagery of the Global Tropics Now Available in Earth Engine for Analysis|by Google Earth|Google Earth and Earth Engine|Medium. Available online: https:\/\/medium.com\/google-earth\/nicfis-satellite-imagery-of-the-global-tropics-now-available-in-earth-engine-for-analysis-1016df52a63d."},{"key":"ref_11","unstructured":"(2022, February 07). NICFI Tropical Forest Basemaps Now Available in Google Earth Engine. Available online: https:\/\/www.planet.com\/pulse\/nicfi-tropical-forest-basemaps-now-available-in-google-earth-engine\/."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1111\/j.1466-8238.2011.00712.x","article-title":"Terrestrial ecosystems from space: A review of earth observation products for macroecology applications","volume":"21","author":"Pfeifer","year":"2011","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mather, P., and Tso, B. (2016). Classification Methods for Remotely Sensed Data, CRC Press. [2nd ed.].","DOI":"10.1201\/9781420090741"},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.compag.2015.03.019","article-title":"An automatic object-based method for optimal thresholding in UAV images: Application for vegetation detection in herbaceous crops","volume":"114","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Tassi, A., Gigante, D., Modica, G., Di Martino, L., and Vizzari, M. (2021). Pixel vs. Object-Based Landsat 8 Data Classification in Google Earth Engine Using Random Forest: The Case Study of Maiella National Park. Remote Sens., 13.","DOI":"10.3390\/rs13122299"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ren, X., and Malik, J. (2003, January 13\u201316). Learning a classification model for segmentation. Proceedings of the IEEE International Conference on Computer Vision, Nice, France.","DOI":"10.1109\/ICCV.2003.1238308"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.isprsjprs.2020.07.013","article-title":"Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples","volume":"167","author":"Ghorbanian","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5","DOI":"10.14445\/22315381\/IJETT-V38P202","article-title":"Comparative Analysis of Pixel-Based and Object-Based Classification of High Resolution Remote Sensing Images\u2014A Review","volume":"38","author":"Aggarwal","year":"2016","journal-title":"Int. J. Eng. Trends Technol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Messina, G., Pe\u00f1a, J.M., Vizzari, M., and Modica, G. (2020). A Comparison of UAV and Satellites Multispectral Imagery in Monitoring Onion Crop. An Application in the \u2018Cipolla Rossa di Tropea\u2019 (Italy). Remote Sens., 12.","DOI":"10.3390\/rs12203424"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Tassi, A., and Vizzari, M. (2020). Object-Oriented LULC Classification in Google Earth Engine Combining SNIC, GLCM, and Machine Learning Algorithms. Remote Sens., 12.","DOI":"10.3390\/rs12223776"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.1080\/01431161.2016.1278314","article-title":"Practical guidelines for choosing GLCM textures to use in landscape classification tasks over a range of moderate spatial scales","volume":"38","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"441","DOI":"10.5589\/m03-006","article-title":"Preliminary evaluation of eCognition object-based software for cut block de-lineation and feature extraction","volume":"29","author":"Flanders","year":"2003","journal-title":"Can. J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5057","DOI":"10.3390\/rs70505057","article-title":"Mapping Above-Ground Biomass in a Tropical Forest in Cambodia Using Canopy Textures Derived from Google Earth","volume":"7","author":"Singh","year":"2015","journal-title":"Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, M., Zhang, R., Luo, H., Gu, S., and Qin, Z. (2022). Crop Mapping in the Sanjiang Plain Using an Improved Object-Oriented Method Based on Google Earth Engine and Combined Growth Period Attributes. Remote Sens., 14.","DOI":"10.3390\/rs14020273"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Loukika, K.N., Keesara, V.R., and Sridhar, V. (2021). Analysis of Land Use and Land Cover Using Machine Learning Algorithms on Google Earth Engine for Munneru River Basin, India. Sustainability, 13.","DOI":"10.3390\/su132413758"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Luo, C., Qi, B., Liu, H., Guo, D., Lu, L., Fu, Q., and Shao, Y. (2021). Using Time Series Sentinel-1 Images for Object-Oriented Crop Classification in Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13040561"},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.patrec.2005.08.011","article-title":"Random forests for land cover classification","volume":"27","author":"Gislason","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support vector machines in remote sensing: A review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5326","DOI":"10.1109\/JSTARS.2020.3021052","article-title":"Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review","volume":"13","author":"Amani","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Pratic\u00f2, S., Solano, F., Di Fazio, S., and Modica, G. (2021). Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation. Remote Sens., 13.","DOI":"10.3390\/rs13040586"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Nery, T., Sadler, R., Solis-Aulestia, M., White, B., Polyakov, M., and Chalak, M. (2016, January 10\u201315). Comparing supervised algorithms in Land Use and Land Cover classification of a Landsat time-series. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7730346"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","article-title":"An assessment of the effectiveness of a random forest classifier for land-cover classification","volume":"67","author":"Ghimire","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Costa, J. (2021). da S.; Liesenberg, V.; Schimalski, M.B.; de Sousa, R.V.; Biffi, L.J.; Gomes, A.R.; Neto, S.L.R.; Mitishita, E.; Bispo, P. da C. Benefits of Combining ALOS\/PALSAR-2 and Sentinel-2A Data in the Classification of Land Cover Classes in the Santa Catarina Southern Plateau. Remote Sens., 13.","DOI":"10.3390\/rs13020229"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Mizuochi, H., Iijima, Y., Nagano, H., Kotani, A., and Hiyama, T. (2021). Dynamic Mapping of Subarctic Surface Water by Fusion of Microwave and Optical Satellite Data Using Conditional Adversarial Networks. Remote Sens., 13.","DOI":"10.3390\/rs13020175"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1080\/22797254.2021.2018667","article-title":"Integrated use of Sentinel-1 and Sentinel-2 data and open-source machine learning algorithms for land cover mapping in a Mediterranean region","volume":"55","author":"Silva","year":"2022","journal-title":"Eur. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"151585","DOI":"10.1016\/j.scitotenv.2021.151585","article-title":"Flood damage assessment with Sentinel-1 and Sentinel-2 data after Sardoba dam break with GLCM features and Random Forest method","volume":"816","author":"Tavus","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Tavares, P.A., Beltr\u00e3o, N.E.S., Guimar\u00e3es, U.S., and Teodoro, A.C. (2019). Integration of Sentinel-1 and Sentinel-2 for Classification and LULC Mapping in the Urban Area of Bel\u00e9m, Eastern Brazilian Amazon. Sensors, 19.","DOI":"10.3390\/s19051140"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Carrasco, L., O\u2019Neil, A.W., Daniel Morton, R., and Rowland, C.S. (2019). Evaluating Combinations of Temporally Aggregated Sentinel-1, Sentinel-2 and Landsat 8 for Land Cover Mapping with Google Earth Engine. Remote Sens., 11.","DOI":"10.3390\/rs11030288"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Javhar, A., Chen, X., Bao, A., Jamshed, A., Yunus, M., Jovid, A., and Latipa, T. (2019). Comparison of Multi-Resolution Optical Land-sat-8, Sentinel-2 and Radar Sentinel-1 Data for Automatic Lineament Extraction: A Case Study of Alichur Area, SE Pamir. Remote Sens., 11.","DOI":"10.3390\/rs11070778"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Rao, P., Zhou, W., Bhattarai, N., Srivastava, A.K., Singh, B., Poonia, S., Lobell, D.B., and Jain, M. (2021). Using Sentinel-1, Sentinel-2, and Planet Imagery to Map Crop Type of Smallholder Farms. Remote Sens., 13.","DOI":"10.3390\/rs13101870"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Kpienbaareh, D., Sun, X., Wang, J., Luginaah, I., Kerr, R.B., Lupafya, E., and Dakishoni, L. (2021). Crop Type and Land Cover Mapping in Northern Malawi Using the Integration of Sentinel-1, Sentinel-2, and PlanetScope Satellite Data. Remote Sens., 13.","DOI":"10.3390\/rs13040700"},{"key":"ref_44","unstructured":"(2022, March 25). Brazil Data Cube\u2014Plataforma para An\u00e1lise e Visualiza\u00e7\u00e3o de Grandes Volumes de Dados Geoespaciais. Available online: http:\/\/brazildatacube.org\/en\/home-page-2\/."},{"key":"ref_45","unstructured":"(2021, March 25). Carta della Natura\u2014Italiano, Available online: https:\/\/www.isprambiente.gov.it\/it\/servizi\/sistema-carta-della-natura."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2495","DOI":"10.1016\/j.rse.2007.11.012","article-title":"A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin","volume":"112","author":"Hansen","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_47","unstructured":"Llano, X.C. (2022, May 27). SMByC-IDEAM. AcATaMa\u2014QGIS Plugin for Accuracy Assessment of Thematic Maps. Available online: https:\/\/github.com\/SMByC\/AcATaMa."},{"key":"ref_48","unstructured":"ESA (2020, October 12). User Guides\u2014Sentinel-2\u2014Sentinel Online. Available online: https:\/\/sentinel.esa.int\/web\/sentinel\/user-guides\/sentinel-2-msi\/overview."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Santaga, F.S., Agnelli, A., Leccese, A., and Vizzari, M. (2021). Using Sentinel-2 for Simplifying Soil Sampling and Mapping: Two Case Studies in Umbria, Italy. Remote Sens., 13.","DOI":"10.3390\/rs13173379"},{"key":"ref_50","unstructured":"(2022, February 08). Planet GEE Delivery Overview. Available online: https:\/\/developers.planet.com\/docs\/integrations\/gee\/."},{"key":"ref_51","unstructured":"(2022, February 08). Use NICFI\u2014Planet Lab Data\u2014SEPAL Documentation. Available online: https:\/\/docs.sepal.io\/en\/latest\/setup\/nicfi.html."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Phan, T.N., Kuch, V., and Lehnert, L.W. (2020). Land Cover Classification using Google Earth Engine and Random Forest Classifier\u2014The Role of Image Composition. Remote Sens., 12.","DOI":"10.3390\/rs12152411"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.rse.2006.06.018","article-title":"Land-cover change detection using multi-temporal MODIS NDVI data","volume":"105","author":"Lunetta","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_54","unstructured":"Woodcock, C.E., Macomber, S.A., and Kumar, L. (2010). Vegetation mapping and monitoring. Environmental Modelling with GIS and Remote Sensing, Taylor & Francis."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1963","DOI":"10.23953\/cloud.ijarsg.74","article-title":"Normalized Difference Vegetation Index (NDVI) Based Classification to Assess the Change in Land Use\/Land Cover (LULC) in Lower Assam, India","volume":"5","author":"Singh","year":"2016","journal-title":"Int. J. Adv. Remote Sens. GIS"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1431","DOI":"10.1081\/PLN-200025858","article-title":"Evaluation of green, red, and near infrared bands for predicting winter wheat biomass, nitrogen uptake, and final grain yield","volume":"27","author":"Moges","year":"2004","journal-title":"J. Plant Nutr."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Yang, X., Zhao, S., Qin, X., Zhao, N., and Liang, L. (2017). Mapping of urban surface water bodies from sentinel-2 MSI imagery at 10 m resolution via NDWI-based image sharpening. Remote Sens., 9.","DOI":"10.3390\/rs9060596"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Diek, S., Fornallaz, F., Schaepman, M.E., and de Jong, R. (2017). Barest Pixel Composite for agricultural areas using landsat time series. Remote Sens., 9.","DOI":"10.3390\/rs9121245"},{"key":"ref_59","unstructured":"Chen, W., Liu, L., Zhang, C., Wang, J., Wang, J., and Pan, Y. (2004, January 20\u201324). Monitoring the seasonal bare soil areas in Beijing using multi-temporal TM images. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Anchorage, AK, USA."},{"key":"ref_60","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., and Deering, D.W. (1973, January 10\u201314). Monitoring vegetation systems in the great plains with erts. Proceedings of the 3rd ERTS-1 Symposium (NASA SP-351), Washington, DC, USA."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of a green channel in remote sensing of global vegetation from EOS-MODIS","volume":"58","author":"Gitelson","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_62","first-page":"77","article-title":"The influence of soil salinity, growth form, and leaf moisture on the spectral ra-diance of Spartina alterniflora canopies","volume":"49","author":"Hardisky","year":"1983","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_63","first-page":"39","article-title":"Tropical forest cover density mapping","volume":"43","author":"Rikimaru","year":"2002","journal-title":"Trop. Ecol."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1109\/TASSP.1981.1163711","article-title":"Cubic Convolution Interpolation for Digital Image Processing","volume":"29","author":"Keys","year":"1981","journal-title":"IEEE Trans. Acoust."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Achanta, R., and S\u00fcsstrunk, S. (2017, January 21\u201326). Superpixels and polygons using simple non-iterative clustering. Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.520"},{"key":"ref_66","first-page":"1155","article-title":"The effect of training strategies on supervised classification at different spatial resolutions","volume":"68","author":"Chen","year":"2002","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_67","unstructured":"Mueller, J.P., and Massaron, L. (2021, June 07). Training, Validating, and Testing in Machine Learning. Available online: https:\/\/www.dummies.com\/programming\/big-data\/data-science\/training-validating-testing-machine-learning\/."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"810","DOI":"10.1080\/10106049.2014.997303","article-title":"Testing the reliability and stability of the internal accuracy assessment of random forest for classifying tree defoliation levels using different validation methods","volume":"30","author":"Adelabu","year":"2015","journal-title":"Geocarto Int."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.catena.2016.11.032","article-title":"A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility","volume":"151","author":"Chen","year":"2017","journal-title":"Catena"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1016\/j.rse.2006.10.010","article-title":"Comparative assessment of the measures of thematic classification accuracy","volume":"107","author":"Liu","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_72","first-page":"1","article-title":"Land use and land cover (LULC) mapping and change detection in the Little Zab River Basin (LZRB), Kurdistan Region, NE Iraq and NW Iran","volume":"43","author":"Merkel","year":"2015","journal-title":"FOG\u2014Freib. Online Geosci."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Sokolova, M., Japkowicz, N., and Szpakowicz, S. (2006). Beyond accuracy, F-score and ROC: A family of discriminant measures for performance evaluation. Proceedings of the AAAI Workshop, AAAI Press. Technical Report.","DOI":"10.1007\/11941439_114"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1109\/LGRS.2010.2069083","article-title":"Pattern-based accuracy assessment of an urban footprint classifi-cation using TerraSAR-X data","volume":"8","author":"Esch","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_75","first-page":"727","article-title":"Statistical rigor and practical utility in thematic map accuracy assessment","volume":"67","author":"Stehman","year":"2001","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Weaver, J., Moore, B., Reith, A., McKee, J., and Lunga, D. (2018, January 22\u201327). A comparison of machine learning techniques to extract human set-tlements from high resolution imagery. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518528"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"111354","DOI":"10.1016\/j.rse.2019.111354","article-title":"Auxiliary datasets improve accuracy of object-based land use\/land cover classification in heterogeneous savanna landscapes","volume":"233","author":"Hurskainen","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"627","DOI":"10.14358\/PERS.70.5.627","article-title":"Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy","volume":"70","author":"Foody","year":"2004","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_79","first-page":"677","article-title":"Remote sensing and geographic information system data integration: Error sources and research issues","volume":"57","author":"Lunetta","year":"1991","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1109\/TGRS.2004.827257","article-title":"A relative evaluation of multiclass image classification by support vector machines","volume":"42","author":"Foody","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"111630","DOI":"10.1016\/j.rse.2019.111630","article-title":"Explaining the unsuitability of the kappa coefficient in the assessment and comparison of the accuracy of thematic maps obtained by image classification","volume":"239","author":"Foody","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2014.02.015","article-title":"Good practices for estimating area and assessing accuracy of land change","volume":"148","author":"Olofsson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_83","unstructured":"(2022, April 29). Mapbiomas Brasil. Available online: https:\/\/mapbiomas.org\/en."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Fernando, L., Assis, F.G., Ferreira, K.R., Vinhas, L., Maurano, L., Almeida, C., Carvalho, A., Rodrigues, J., Maciel, A., and Ca-margo, C. (2019). TerraBrasilis: A Spatial Data Analytics Infrastructure for Large-Scale Thematic Mapping. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8110513"},{"key":"ref_85","unstructured":"(2022, April 29). PRODES\u2014Coordena\u00e7\u00e3o-Geral de Observa\u00e7\u00e3o da Terra. Available online: http:\/\/www.obt.inpe.br\/OBT\/assuntos\/programas\/amazonia\/prodes."},{"key":"ref_86","unstructured":"(2022, May 27). European Space Agency WorldCover. Available online: https:\/\/esa-worldcover.org\/en."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Karra, K., Kontgis, C., Statman-Weil, Z., Mazzariello, J.C., Mathis, M., and Brumby, S.P. (2021, January 11\u201316). Global land use\/land cover with Sentinel 2 and deep learning. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9553499"},{"key":"ref_88","unstructured":"Maas, M.D. (2022, April 29). 5 Things to Consider about Google Earth Engine. Available online: https:\/\/www.matecdev.com\/posts\/disadvantages-earth-engine.html."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Chang, N.B., and Bai, K. (2018). Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing, CRC Press.","DOI":"10.1201\/9781315154602"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Thanh Noi, P., and Kappas, M. (2018). Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery. Sensors, 18.","DOI":"10.3390\/s18010018"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Yang, Y., Yang, D., Wang, X., Zhang, Z., and Nawaz, Z. (2021). Testing accuracy of land cover classification algorithms in the qilian mountains based on gee cloud platform. Remote Sens., 13.","DOI":"10.3390\/rs13245064"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Caballero, G.R., Platzeck, G., Pezzola, A., Casella, A., Winschel, C., Silva, S.S., Ludue\u00f1a, E., Pasqualotto, N., and Delegido, J. (2020). Assessment of Multi-Date Sentinel-1 Polarizations and GLCM Texture Features Capacity for Onion and Sunflower Classification in an Irrigated Valley: An Object Level Approach. Agronomy, 10.","DOI":"10.3390\/agronomy10060845"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Stromann, O., Nascetti, A., Yousif, O., and Ban, Y. (2020). Dimensionality Reduction and Feature Selection for Object-Based Land Cover Classification based on Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine. Remote Sens., 12.","DOI":"10.3390\/rs12010076"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"2860","DOI":"10.3390\/s7112860","article-title":"Object-Based Classification of Ikonos Imagery for Mapping Large-Scale Vegetation Communities in Urban Areas","volume":"7","author":"Mathieu","year":"2007","journal-title":"Sensors"},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Cai, L., Shi, W., Miao, Z., and Hao, M. (2018). Accuracy Assessment Measures for Object Extraction from Remote Sensing Images. Remote Sens., 10.","DOI":"10.3390\/rs10020303"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Radoux, J., Bogaert, P., Kerle, N., Gerke, M., Lef\u00e8vre, S., Gloaguen, R., and Thenkabail, P.S. (2017). Good Practices for Object-Based Accuracy Assessment. 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