{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T18:28:13Z","timestamp":1773944893229,"version":"3.50.1"},"reference-count":81,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,11]],"date-time":"2020-09-11T00:00:00Z","timestamp":1599782400000},"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>High resolution satellite imagery and modern machine learning methods hold the potential to fill existing data gaps in where crops are grown around the world at a sub-field level. However, high resolution crop type maps have remained challenging to create in developing regions due to a lack of ground truth labels for model development. In this work, we explore the use of crowdsourced data, Sentinel-2 and DigitalGlobe imagery, and convolutional neural networks (CNNs) for crop type mapping in India. Plantix, a free app that uses image recognition to help farmers diagnose crop diseases, logged 9 million geolocated photos from 2017\u20132019 in India, 2 million of which are in the states of Andhra Pradesh and Telangana in India. Crop type labels based on farmer-submitted images were added by domain experts and deep CNNs. The resulting dataset of crop type at coordinates is high in volume, but also high in noise due to location inaccuracies, submissions from out-of-field, and labeling errors. We employed a number of steps to clean the dataset, which included training a CNN on very high resolution DigitalGlobe imagery to filter for points that are within a crop field. With this cleaned dataset, we extracted Sentinel time series at each point and trained another CNN to predict the crop type at each pixel. When evaluated on the highest quality subset of crowdsourced data, the CNN distinguishes rice, cotton, and \u201cother\u201d crops with 74% accuracy in a 3-way classification and outperforms a random forest trained on harmonic regression features. Furthermore, model performance remains stable when low quality points are introduced into the training set. Our results illustrate the potential of non-traditional, high-volume\/high-noise datasets for crop type mapping, some improvements that neural networks can achieve over random forests, and the robustness of such methods against moderate levels of training set noise. Lastly, we caution that obstacles like the lack of good Sentinel-2 cloud mask, imperfect mobile device location accuracy, and preservation of privacy while improving data access will need to be addressed before crowdsourcing can widely and reliably be used to map crops in smallholder systems.<\/jats:p>","DOI":"10.3390\/rs12182957","type":"journal-article","created":{"date-parts":[[2020,9,11]],"date-time":"2020-09-11T09:05:16Z","timestamp":1599815116000},"page":"2957","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":72,"title":["Mapping Crop Types in Southeast India with Smartphone Crowdsourcing and Deep Learning"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4618-5675","authenticated-orcid":false,"given":"Sherrie","family":"Wang","sequence":"first","affiliation":[{"name":"Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA 94305, USA"},{"name":"Department of Earth System Science and Center on Food Security and the Environment, Stanford University, Stanford, CA 94305, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0664-3651","authenticated-orcid":false,"given":"Stefania","family":"Di Tommaso","sequence":"additional","affiliation":[{"name":"Department of Earth System Science and Center on Food Security and the Environment, Stanford University, Stanford, CA 94305, USA"}]},{"given":"Joey","family":"Faulkner","sequence":"additional","affiliation":[{"name":"Progressive Environmental &amp; Agricultural Technologies, 10435 Berlin, Germany"}]},{"given":"Thomas","family":"Friedel","sequence":"additional","affiliation":[{"name":"Progressive Environmental &amp; Agricultural Technologies, 10435 Berlin, Germany"}]},{"given":"Alexander","family":"Kennepohl","sequence":"additional","affiliation":[{"name":"Progressive Environmental &amp; Agricultural Technologies, 10435 Berlin, Germany"}]},{"given":"Rob","family":"Strey","sequence":"additional","affiliation":[{"name":"Progressive Environmental &amp; Agricultural Technologies, 10435 Berlin, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5969-3476","authenticated-orcid":false,"given":"David B.","family":"Lobell","sequence":"additional","affiliation":[{"name":"Department of Earth System Science and Center on Food Security and the Environment, Stanford University, Stanford, CA 94305, USA"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,11]]},"reference":[{"key":"ref_1","unstructured":"Khalil, C.A., Conforti, P., Ergin, I., and Gennari, P. (2017). Defining Small Scale Food Producers to Monitor Target 2.3. of the 2030 Agenda for Sustainable Development, Food and Agriculture Organization of the United Nations. Technical Report."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.worlddev.2015.10.041","article-title":"The Number, Size, and Distribution of Farms, Smallholder Farms, and Family Farms Worldwide","volume":"87","author":"Lowder","year":"2016","journal-title":"World Dev."},{"key":"ref_3","unstructured":"Rapsomanikis, G. (2015). The Economic Lives of Smallholder Farmers, Food and Agriculture Organization of the United Nations. Technical Report."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.gfs.2018.05.002","article-title":"How much of the world\u2019s food do smallholders produce?","volume":"17","author":"Ricciardi","year":"2018","journal-title":"Glob. Food Secur."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"124010","DOI":"10.1088\/1748-9326\/11\/12\/124010","article-title":"Subnational distribution of average farm size and smallholder contributions to global food production","volume":"11","author":"Samberg","year":"2016","journal-title":"Environ. Res. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2189","DOI":"10.1073\/pnas.1616919114","article-title":"Satellite-based assessment of yield variation and its determinants in smallholder African systems","volume":"114","author":"Burke","year":"2017","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.agee.2012.11.011","article-title":"Evidence for increased monoculture cropping in the Central United States","volume":"165","author":"Plourde","year":"2013","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_8","unstructured":"Espey, J. (2015). Data for Development: A Needs Assessment for SDG Monitoring and Statistical Capacity Development, Sustainable Development Solutions Network. Technical Report."},{"key":"ref_9","unstructured":"Ministry of Agriculture and Farmers\u2019 Welfare (2019, September 28). Crop Production Statistics Information System, Available online: https:\/\/aps.dac.gov.in\/APY\/Index.htm."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.foodpol.2017.02.002","article-title":"Agriculture in Africa\u2014Telling myths from facts: A synthesis","volume":"67","author":"Christiaensen","year":"2017","journal-title":"Food Policy"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"949","DOI":"10.3390\/rs5020949","article-title":"Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs","volume":"5","author":"Atzberger","year":"2013","journal-title":"Remote Sens."},{"key":"ref_12","unstructured":"USDA National Agricultural Statistics Service Cropland Data Layer (2019, August 29). Published Crop-Specific Data Layer [Online], Available online: https:\/\/nassgeodata.gmu.edu\/CropScape\/."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Fisette, T., Rollin, P., Aly, Z., Campbell, L., Daneshfar, B., Filyer, P., Smith, A., Davidson, A., Shang, J., and Jarvis, I. (2013, January 12\u201316). AAFC annual crop inventory. Proceedings of the 2013 Second International Conference on Agro-Geoinformatics (Agro-Geoinformatics), Fairfax, VA, USA.","DOI":"10.1109\/Argo-Geoinformatics.2013.6621920"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"12356","DOI":"10.3390\/rs70912356","article-title":"Assessment of an Operational System for Crop Type Map Production Using High Temporal and Spatial Resolution Satellite Optical Imagery","volume":"7","author":"Inglada","year":"2015","journal-title":"Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"44","DOI":"10.5751\/ES-05103-170444","article-title":"Diversified Farming Systems: An Agroecological, Systems-based Alternative to Modern Industrial Agriculture","volume":"17","author":"Kremen","year":"2012","journal-title":"Ecol. Soc."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.foodpol.2016.09.010","article-title":"Ten striking facts about agricultural input use in Sub-Saharan Africa","volume":"67","author":"Sheahan","year":"2017","journal-title":"Food Policy"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/j.rse.2018.11.007","article-title":"Near real-time agriculture monitoring at national scale at parcel resolution: Performance assessment of the Sen2-Agri automated system in various cropping systems around the world","volume":"221","author":"Defourny","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.rse.2019.04.016","article-title":"Smallholder maize area and yield mapping at national scales with Google Earth Engine","volume":"228","author":"Jin","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/BF02989996","article-title":"Village level crop inventory using remote sensing and field survey data","volume":"33","author":"Singh","year":"2005","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1029\/2011EO490002","article-title":"A library of georeferenced photos from the field","volume":"92","author":"Xiao","year":"2011","journal-title":"EOS Trans. Am. Geophys. Union"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1038\/s41597-019-0036-3","article-title":"High resolution paddy rice maps in cloud-prone Bangladesh and Northeast India using Sentinel-1 data","volume":"6","author":"Singha","year":"2019","journal-title":"Sci. Data"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"135","DOI":"10.3390\/rs6010135","article-title":"A Phenology-Based Classification of Time-Series MODIS Data for Rice Crop Monitoring in Mekong Delta, Vietnam","volume":"6","author":"Son","year":"2014","journal-title":"Remote Sens."},{"key":"ref_23","first-page":"123","article-title":"Extracting seasonal cropping patterns using multi-temporal vegetation indices from IRS LISS-III data in Muzaffarpur District of Bihar, India","volume":"17","author":"Mondal","year":"2014","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.rse.2005.10.004","article-title":"Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images","volume":"100","author":"Xiao","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"160118","DOI":"10.1038\/sdata.2016.118","article-title":"Remotely sensed high resolution irrigated area mapping in India for 2000 to 2015","volume":"3","author":"Ambika","year":"2016","journal-title":"Sci. Data"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1080\/17538947.2016.1168489","article-title":"Mapping rice-fallow cropland areas for short-season grain legumes intensification in South Asia using MODIS 250 m time-series data","volume":"9","author":"Gumma","year":"2016","journal-title":"Int. J. Digit. Earth"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"6008","DOI":"10.1080\/01431161.2015.1110259","article-title":"Mapping of rice-cropping pattern and cultural type using remote-sensing and ancillary data: A case study for South and Southeast Asian countries","volume":"36","author":"Manjunath","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","unstructured":"Ministry of Agriculture and Farmers\u2019 Welfare, Government of India (2019). All India Report on Number and Area of Operational Holdings 2015\u20132016."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1007\/s41976-019-00015-9","article-title":"Wheat Acreage Mapping and Yield Prediction Using Landsat-8 OLI Satellite Data: A Case Study in Sahibganj Province, Jharkhand (India)","volume":"2","author":"Parida","year":"2019","journal-title":"Remote Sens. Earth Syst. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1007\/s12524-012-0252-1","article-title":"Mapping a Specific Crop\u2014A Temporal Approach for Sugarcane Ratoon","volume":"42","author":"Misra","year":"2014","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1823","DOI":"10.1007\/s12524-018-0839-2","article-title":"Remote Sensing-Based Yield Forecasting for Sugarcane (Saccharum officinarum L.) Crop in India","volume":"46","author":"Dubey","year":"2018","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_32","unstructured":"Internet and Mobile Association of India (2018). Mobile Internet Report 2017, Internet and Mobile Association of India. Technical Report, Kantar IMRB."},{"key":"ref_33","unstructured":"(2019, September 24). FASAL (Forecasting Agricultural Output Using Space, Agro-Meteorology and Land Based Observations), Available online: http:\/\/www.ncfc.gov.in\/about_fasal.html."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1080\/22797254.2018.1455540","article-title":"A rule-based approach for crop identification using multi-temporal and multi-sensor phenological metrics","volume":"51","author":"Ghazaryan","year":"2018","journal-title":"Eur. J. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/S0168-1699(02)00116-3","article-title":"Crop identification using harmonic analysis of time-series AVHRR NDVI data","volume":"37","author":"Jakubauskas","year":"2002","journal-title":"Comput. Electron. Agric."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.rse.2018.12.026","article-title":"Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques","volume":"222","author":"Wang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_37","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_38","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_39","first-page":"1","article-title":"Irrigation status, issues and management in Andhra Pradesh","volume":"1532","author":"Prasuna","year":"2018","journal-title":"Ground Water"},{"key":"ref_40","unstructured":"Forest Survey of India (2018). State of Forest Report 2017, Technical Report."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1007\/s11769-019-1060-0","article-title":"Exploring the Potential of Mapping Cropping Patterns on Smallholder Scale Croplands Using Sentinel-1 SAR Data","volume":"29","author":"Useya","year":"2019","journal-title":"Chin. Geogr. Sci."},{"key":"ref_42","unstructured":"(2020, August 22). SNAP\u2014Sentinel Application Platform. Available online: http:\/\/step.esa.int\/main\/toolboxes\/snap\/."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Rumora, L., Miler, M., and Medak, D. (2019). Contemporary comparative assessment of atmospheric correction influence on radiometric indices between Sentinel-2A and Landsat 8 imagery. Geocarto Int., 1\u201315.","DOI":"10.1080\/10106049.2019.1590465"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Rumora, L., Miler, M., and Medak, D. (2020). Impact of Various Atmospheric Corrections on Sentinel-2 Land Cover Classification Accuracy Using Machine Learning Classifiers. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9040277"},{"key":"ref_45","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_46","doi-asserted-by":"crossref","unstructured":"Gitelson, A.A., Vina, A., Ciganda, V., Rundquist, D.C., and Arkebauer, T.J. (2005). Remote estimation of canopy chlorophyll content in crops. Geophys. Res. Lett., 32.","DOI":"10.1029\/2005GL022688"},{"key":"ref_47","first-page":"140","article-title":"Estimating green LAI in four crops: Potential of determining optimal spectral bands for a universal algorithm","volume":"192\u2013193","author":"Peng","year":"2014","journal-title":"Agric. For. Meteorol."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Jain, M., Srivastava, A.K., Joon, R.K., McDonald, A., Royal, K., Lisaius, M.C., and Lobell, D.B. (2016). Mapping Smallholder Wheat Yields and Sowing Dates Using Micro-Satellite Data. Remote Sens., 8.","DOI":"10.3390\/rs8100860"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1016\/j.rse.2018.08.009","article-title":"A first assessment of the Sentinel-2 Level 1-C cloud mask product to support informed surface analyses","volume":"217","author":"Coluzzi","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.rse.2017.07.015","article-title":"Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications","volume":"199","author":"Veloso","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"016008","DOI":"10.1117\/1.JRS.12.016008","article-title":"Above-ground biomass prediction by Sentinel-1 multitemporal data in central Italy with integration of ALOS2 and Sentinel-2 data","volume":"12","author":"Laurin","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Dasari, K., Anjaneyulu, L., Jayasri, P.V., and Prasad, A.V.V. (2015, January 18\u201320). Importance of speckle filtering in image classification of SAR data. Proceedings of the 2015 International Conference on Microwave, Optical and Communication Engineering (ICMOCE), Bhubaneswar, India.","DOI":"10.1109\/ICMOCE.2015.7489764"},{"key":"ref_53","unstructured":"Ministry of Agriculture and Farmers\u2019 Welfare (2019, September 01). District-Wise, Season-Wise Crop Production Statistics, Available online: https:\/\/data.gov.in\/catalog\/district-wise-season-wise-crop-production-statistics."},{"key":"ref_54","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep Residual Learning for Image Recognition. arXiv.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1109\/LGRS.2015.2499239","article-title":"Deep Learning Earth Observation Classification Using ImageNet Pretrained Networks","volume":"13","author":"Marmanis","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_57","unstructured":"Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A. (OpenReview, 2017). Automatic Differentiation in PyTorch, OpenReview."},{"key":"ref_58","unstructured":"European Space Agency (2020, July 19). Sentinel-2 MSI Data Product Quality Report. July 2018. Available online: https:\/\/sentinels.copernicus.eu\/web\/sentinel\/data-product-quality-reports."},{"key":"ref_59","unstructured":"Shumway, R.H., and Stoffer, D.S. (2005). Time Series Analysis and Its Applications (Springer Texts in Statistics), Springer."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1016\/j.cageo.2004.05.006","article-title":"TIMESAT\u2014A program for analyzing time-series of satellite sensor data","volume":"30","author":"Eklundh","year":"2004","journal-title":"Comput. Geosci."},{"key":"ref_61","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_62","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.rse.2017.05.025","article-title":"Landsat-based classification in the cloud: An opportunity for a paradigm shift in land cover monitoring","volume":"202","author":"Azzari","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"421","DOI":"10.5721\/EuJRS20124535","article-title":"Evaluation of random forest method for agricultural crop classification","volume":"45","author":"Ok","year":"2012","journal-title":"Eur. J. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.isprsjprs.2016.03.008","article-title":"Optical remotely sensed time series data for land cover classification: A review","volume":"116","author":"Gomez","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_65","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"6085","DOI":"10.1038\/s41598-018-24271-9","article-title":"Recurrent Neural Networks for Multivariate Time Series with Missing Values","volume":"8","author":"Che","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Fleet, D., Pajdla, T., Schiele, B., and Tuytelaars, T. (2014, January 6\u201312). Visualizing and Understanding Convolutional Networks. Proceedings of the Computer Vision\u2014ECCV 2014, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10578-9"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A. (July, January 26). Learning Deep Features for Discriminative Localization. Proceedings of the 2016 Conference on Computer Vision and Pattern Recognition (CVPR 2016), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Ru\u00dfwurm, M., and K\u00f6rner, M. (2018). Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7040129"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Navab, N., Hornegger, J., Wells, W.M., and Frangi, A.F. (2015, January 5\u20139). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015, Munich, Germany.","DOI":"10.1007\/978-3-319-24571-3"},{"key":"ref_72","unstructured":"Gumma, M.K., Thenkabail, P.S., Teluguntla, P., Oliphant, A.J., Xiong, J., Congalton, R.G., Yadav, K., Phalke, A., and Smith, C. (2019, September 24). NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-Support Analysis Data (GFSAD) Cropland Extent 2015 South Asia, Afghanistan, Iran 30 m V001 [Data Set]. Available online: https:\/\/doi.org\/10.5067\/MEaSUREs\/GFSAD\/GFSAD30SAAFGIRCE.001."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1080\/15481603.2017.1370169","article-title":"Landsat-8 vs. Sentinel-2: Examining the added value of Sentinel-2\u2019s red-edge bands to land-use and land-cover mapping in Burkina Faso","volume":"55","author":"Forkuor","year":"2018","journal-title":"GIScience Remote Sens."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"195","DOI":"10.5194\/isprsarchives-XL-7-195-2014","article-title":"Crop Type Classification Using Vegetation Indices of RapidEye Imagery","volume":"40","author":"Ustuner","year":"2014","journal-title":"Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Radoux, J., Chom\u00e9, G., Jacques, D.C., Waldner, F., Bellemans, N., Matton, N., Lamarche, C., D\u2019Andrimont, R., and Defourny, P. (2016). Sentinel-2\u2019s Potential for Sub-Pixel Landscape Feature Detection. Remote Sens., 8.","DOI":"10.3390\/rs8060488"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Immitzer, M., Vuolo, F., and Atzberger, C. (2016). First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe. Remote Sens., 8.","DOI":"10.3390\/rs8030166"},{"key":"ref_77","unstructured":"Rolnick, D., Veit, A., Belongie, S., and Shavit, N. (2017). Deep Learning is Robust to Massive Label Noise. arXiv."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Leibe, B., Matas, J., Sebe, N., and Welling, M. (2016, January 8\u201316). The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition. Proceedings of the Computer Vision\u2014ECCV 2016, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46475-6"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1109\/TPAMI.2015.2456899","article-title":"Classification with Noisy Labels by Importance Reweighting","volume":"38","author":"Liu","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Ghosh, A., Kumar, H., and Sastry, P.S. (2017, January 4\u201310). Robust Loss Functions under Label Noise for Deep Neural Networks. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.10894"},{"key":"ref_81","unstructured":"Goldberger, J., and Ben-Reuven, E. (2017, January 24\u201326). Training deep neural-networks using a noise adaptation layer. Proceedings of the ICLR 2017, Toulon, France."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/18\/2957\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:09:13Z","timestamp":1760177353000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/18\/2957"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,11]]},"references-count":81,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["rs12182957"],"URL":"https:\/\/doi.org\/10.3390\/rs12182957","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,11]]}}}