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In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3000-3009). doi: 10.1109\/cvpr.2017.622.","DOI":"10.1109\/CVPR.2017.622"},{"key":"10.1016\/j.compag.2024.109042_b0385","doi-asserted-by":"crossref","DOI":"10.1016\/j.ecolind.2022.108961","article-title":"A multi-angle comprehensive solution based on deep learning to extract cultivated land information from high-resolution remote sensing images","volume":"141","author":"Liu","year":"2022","journal-title":"Ecological Indicators"},{"key":"10.1016\/j.compag.2024.109042_b0390","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2022.107473","article-title":"A deep learning method for individual arable field (IAF) extraction with cross-domain adversarial capability","volume":"203","author":"Liu","year":"2022","journal-title":"Computers and Electronics in Agriculture"},{"key":"10.1016\/j.compag.2024.109042_b0395","doi-asserted-by":"crossref","first-page":"3733","DOI":"10.3390\/rs12223733","article-title":"Farmland Parcel Mapping in Mountain Areas Using Time-Series SAR Data and VHR Optical Images","volume":"12","author":"Liu","year":"2020","journal-title":"Remote Sensing"},{"issue":"1","key":"10.1016\/j.compag.2024.109042_b0400","first-page":"105","article-title":"A divided and stratified extraction method of high-resolution remote sensing information for cropland in hilly and mountainous areas based on deep learning","volume":"50","author":"Liu","year":"2021","journal-title":"Acta Geodaetica Et Cartographica Sinica"},{"issue":"13","key":"10.1016\/j.compag.2024.109042_b0405","first-page":"171","article-title":"Semantic segmentation of terrace image regions based on lightweight CNN-Transformer hybrid networks","volume":"39","author":"Liu","year":"2023","journal-title":"Transactions of the Chinese Society of Agricultural Engineering (transactions of the CSAE)"},{"issue":"9","key":"10.1016\/j.compag.2024.109042_b0410","first-page":"1077","article-title":"Scene classification dataset using the Tiangong-1 hyperspectral remote sensing imagery and its applications","volume":"24","author":"Liu","year":"2020","journal-title":"J. Remote Sens."},{"key":"10.1016\/j.compag.2024.109042_b0415","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., & Darrell, T., 2015. Fully convolutional networks for semantic segmentation. 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R., Nie, C., Wang, L., & Sun, J., 2020. Farmland segmentation from remote sensing images using deep learning methods. In Remote sensing for agriculture, ecosystems, and hydrology XXII (Vol. 11528, pp. 51-57). SPIE. doi: 10.1117\/12.2573244.","DOI":"10.1117\/12.2573244"},{"issue":"4","key":"10.1016\/j.compag.2024.109042_b0575","doi-asserted-by":"crossref","first-page":"1603","DOI":"10.1080\/01431161.2019.1673916","article-title":"Geo-parcel-based crop classification in very-high-resolution images via hierarchical perception","volume":"41","author":"Sun","year":"2020","journal-title":"International Journal of Remote Sensing"},{"issue":"3","key":"10.1016\/j.compag.2024.109042_b0580","doi-asserted-by":"crossref","first-page":"1054","DOI":"10.1080\/01431161.2022.2032458","article-title":"Farmland parcel-based crop classification in cloudy\/rainy mountains using Sentinel-1 and Sentinel-2 based deep learning","volume":"43","author":"Sun","year":"2022","journal-title":"International Journal of Remote Sensing"},{"key":"10.1016\/j.compag.2024.109042_b0585","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2022.107273","article-title":"Deep edge enhancement-based semantic segmentation network for farmland segmentation with satellite imagery","volume":"202","author":"Sun","year":"2022","journal-title":"Computers and Electronics in Agriculture"},{"issue":"9","key":"10.1016\/j.compag.2024.109042_b0590","doi-asserted-by":"crossref","first-page":"1505","DOI":"10.3390\/rs16091505","article-title":"Enhancing Crop Mapping through Automated Sample Generation Based on Segment Anything Model with Medium-Resolution Satellite Imagery","volume":"16","author":"Sun","year":"2023","journal-title":"Remote Sensing"},{"key":"10.1016\/j.compag.2024.109042_b0595","doi-asserted-by":"crossref","first-page":"3323","DOI":"10.1109\/JSTARS.2022.3164771","article-title":"A sentinel-2 multiyear, multicountry benchmark dataset for crop classification and segmentation with deep learning","volume":"15","author":"Sykas","year":"2022","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"key":"10.1016\/j.compag.2024.109042_b0600","doi-asserted-by":"crossref","first-page":"722","DOI":"10.3390\/rs13040722","article-title":"Advanced Fully Convolutional Networks for Agricultural Field Boundary Detection","volume":"13","author":"Taravat","year":"2021","journal-title":"Remote Sensing"},{"issue":"4","key":"10.1016\/j.compag.2024.109042_b0605","first-page":"295","article-title":"Comparison of an Optimised Multiresolution Segmentation Approach with Deep Neural Networks for Delineating Agricultural Fields from Sentinel-2 Images. 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Environ."},{"key":"10.1016\/j.compag.2024.109042_b0650","doi-asserted-by":"crossref","first-page":"2197","DOI":"10.3390\/rs13112197","article-title":"Detect, Consolidate, Delineate: Scalable Mapping of Field Boundaries Using Satellite Images","volume":"13","author":"Waldner","year":"2021","journal-title":"Remote Sens."},{"issue":"1","key":"10.1016\/j.compag.2024.109042_b0655","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1515\/geo-2020-0272","article-title":"Crops planting structure and karst rocky desertification analysis by Sentinel-1 data","volume":"13","author":"Wang","year":"2021","journal-title":"Open Geosciences"},{"issue":"1","key":"10.1016\/j.compag.2024.109042_b0660","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s10661-022-10848-5","article-title":"Farmland quality assessment using deep fully convolutional neural networks","volume":"195","author":"Wang","year":"2023","journal-title":"Environmental Monitoring and Assessment"},{"issue":"22","key":"10.1016\/j.compag.2024.109042_b0665","doi-asserted-by":"crossref","first-page":"5738","DOI":"10.3390\/rs14225738","article-title":"Unlocking large-scale crop field delineation in smallholder farming systems with transfer learning and weak supervision","volume":"14","author":"Wang","year":"2022","journal-title":"Remote Sensing"},{"key":"10.1016\/j.compag.2024.109042_b0670","doi-asserted-by":"crossref","first-page":"2342","DOI":"10.3390\/agronomy12102342","article-title":"Agricultural Field Boundary Delineation with Satellite Image Segmentation for High-Resolution Crop Mapping: A Case Study of Rice Paddy","volume":"12","author":"Wang","year":"2022","journal-title":"Agronomy"},{"key":"10.1016\/j.compag.2024.109042_b0675","article-title":"BSNet: Boundary-semantic-fusion network for farmland parcel mapping in high-resolution satellite images","volume":"206","author":"Wang","year":"2023","journal-title":"Comput. Electron. 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