{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T01:41:39Z","timestamp":1774143699704,"version":"3.50.1"},"reference-count":162,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T00:00:00Z","timestamp":1718755200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Union","doi-asserted-by":"publisher","award":["101007702"],"award-info":[{"award-number":["101007702"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000780","name":"European Union","doi-asserted-by":"publisher","award":["UIDB\/04152\/2020"],"award-info":[{"award-number":["UIDB\/04152\/2020"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"name":"FCT (Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia)","award":["101007702"],"award-info":[{"award-number":["101007702"]}]},{"name":"FCT (Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia)","award":["UIDB\/04152\/2020"],"award-info":[{"award-number":["UIDB\/04152\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Recent advancements in deep learning have spurred the development of numerous novel semantic segmentation models for land cover mapping, showcasing exceptional performance in delineating precise boundaries and producing highly accurate land cover maps. However, to date, no systematic literature review has comprehensively examined semantic segmentation models in the context of land cover mapping. This paper addresses this gap by synthesizing recent advancements in semantic segmentation models for land cover mapping from 2017 to 2023, drawing insights on trends, data sources, model structures, and performance metrics based on a review of 106 articles. Our analysis identifies top journals in the field, including MDPI Remote Sensing, IEEE Journal of Selected Topics in Earth Science, and IEEE Transactions on Geoscience and Remote Sensing, IEEE Geoscience and Remote Sensing Letters, and ISPRS Journal Of Photogrammetry And Remote Sensing. We find that research predominantly focuses on land cover, urban areas, precision agriculture, environment, coastal areas, and forests. Geographically, 35.29% of the study areas are located in China, followed by the USA (11.76%), France (5.88%), Spain (4%), and others. Sentinel-2, Sentinel-1, and Landsat satellites emerge as the most used data sources. Benchmark datasets such as ISPRS Vaihingen and Potsdam, LandCover.ai, DeepGlobe, and GID datasets are frequently employed. Model architectures predominantly utilize encoder\u2013decoder and hybrid convolutional neural network-based structures because of their impressive performances, with limited adoption of transformer-based architectures due to its computational complexity issue and slow convergence speed. Lastly, this paper highlights existing key research gaps in the field to guide future research directions.<\/jats:p>","DOI":"10.3390\/rs16122222","type":"journal-article","created":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T08:06:06Z","timestamp":1718784366000},"page":"2222","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["A Systematic Literature Review and Bibliometric Analysis of Semantic Segmentation Models in Land Cover Mapping"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9790-6081","authenticated-orcid":false,"given":"Segun","family":"Ajibola","sequence":"first","affiliation":[{"name":"Afridat UG (haftungsbeschr\u00e4nkt), Sebastianstrasse 38, 53115 Bonn, Germany"},{"name":"NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8622-6008","authenticated-orcid":false,"given":"Pedro","family":"Cabral","sequence":"additional","affiliation":[{"name":"NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisbon, Portugal"},{"name":"School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Vali, A., Comai, S., and Matteucci, M. (2020). Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review. Remote Sens., 12.","DOI":"10.3390\/rs12152495"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ma, J., Wu, L., Tang, X., Liu, F., Zhang, X., and Jiao, L. (2020). Building Extraction of Aerial Images by a Global and Multi-Scale Encoder-Decoder Network. Remote Sens., 12.","DOI":"10.3390\/rs12152350"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"104751","DOI":"10.1016\/j.envsoft.2020.104751","article-title":"Predicting Developed Land Expansion Using Deep Convolutional Neural Networks","volume":"134","author":"Pourmohammadi","year":"2020","journal-title":"Environ. Model. Softw."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Di Pilato, A., Taggio, N., Pompili, A., Iacobellis, M., Di Florio, A., Passarelli, D., and Samarelli, S. (2021). Deep Learning Approaches to Earth Observation Change Detection. Remote Sens., 13.","DOI":"10.3390\/rs13204083"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.isprsjprs.2021.02.011","article-title":"Large-Scale Rice Mapping under Different Years Based on Time-Series Sentinel-1 Images Using Deep Semantic Segmentation Model","volume":"174","author":"Wei","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Dal Molin Jr., R., and Rizzoli, P. (2022). Potential of Convolutional Neural Networks for Forest Mapping Using Sentinel-1 Interferometric Short Time Series. Remote Sens., 14.","DOI":"10.3390\/rs14061381"},{"key":"ref_7","first-page":"8000605","article-title":"Fine-Grained Building Change Detection from Very High-Spatial-Resolution Remote Sensing Images Based on Deep Multitask Learning","volume":"19","author":"Sun","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Tren\u010danov\u00e1, B., Proen\u00e7a, V., and Bernardino, A. (2022). Development of Semantic Maps of Vegetation Cover from UAV Images to Support Planning and Management in Fine-Grained Fire-Prone Landscapes. Remote Sens., 14.","DOI":"10.3390\/rs14051262"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1249","DOI":"10.1080\/2150704X.2022.2142075","article-title":"MSANet: An Improved Semantic Segmentation Method Using Multi-Scale Attention for Remote Sensing Images","volume":"13","author":"Zhang","year":"2022","journal-title":"Remote Sens. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"10357","DOI":"10.1109\/JSTARS.2021.3116094","article-title":"Wide-Area Land Cover Mapping with Sentinel-1 Imagery Using Deep Learning Semantic Segmentation Models","volume":"14","author":"Scepanovic","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.neucom.2015.09.116","article-title":"Deep Learning for Visual Understanding: A Review","volume":"187","author":"Guo","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Huang, J., Weng, L., Chen, B., and Xia, M. (2021). DFFAN: Dual Function Feature Aggregation Network for Semantic Segmentation of Land Cover. ISPRS Int. J. Geoinf., 10.","DOI":"10.3390\/ijgi10030125"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chen, S., Wu, C., Mukherjee, M., and Zheng, Y. (2021). Ha-Mppnet: Height Aware-Multi Path Parallel Network for High Spatial Resolution Remote Sensing Image Semantic Seg-Mentation. ISPRS Int. J. Geoinf., 10.","DOI":"10.3390\/ijgi10100672"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.neucom.2019.11.118","article-title":"A Brief Survey on Semantic Segmentation with Deep Learning","volume":"406","author":"Hao","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.1109\/JSTARS.2018.2810320","article-title":"Symmetrical Dense-Shortcut Deep Fully Convolutional Networks for Semantic Segmentation of Very-High-Resolution Remote Sensing Images","volume":"11","author":"Chen","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chen, B., Xia, M., and Huang, J. (2021). Mfanet: A Multi-Level Feature Aggregation Network for Semantic Segmentation of Land Cover. Remote Sens., 13.","DOI":"10.3390\/rs13040731"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"6812","DOI":"10.1109\/JSTARS.2023.3295729","article-title":"Sgformer: A Local and Global Features Coupling Network for Semantic Segmentation of Land Cover","volume":"16","author":"Weng","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.isprsjprs.2022.06.008","article-title":"UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagery","volume":"190","author":"Wang","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"7598","DOI":"10.1080\/01431161.2023.2285738","article-title":"Csswin-Unet: A Swin-Unet Network for Semantic Segmentation of Remote Sensing Images by Aggregating Contextual Information and Extracting Spatial Information","volume":"44","author":"Xiao","year":"2023","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.asoc.2018.05.018","article-title":"A Survey on Deep Learning Techniques for Image and Video Semantic Segmentation","volume":"70","author":"Oprea","year":"2018","journal-title":"Appl. Soft Comput. J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.neucom.2019.02.003","article-title":"Survey on Semantic Segmentation Using Deep Learning Techniques","volume":"338","author":"Lateef","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"101478","DOI":"10.1016\/j.ecoser.2022.101478","article-title":"A Review of Machine Learning and Big Data Applications in Addressing Ecosystem Service Research Gaps","volume":"57","author":"Manley","year":"2022","journal-title":"Ecosyst. Serv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Tian, T., Chu, Z., Hu, Q., and Ma, L. (2021). Class-Wise Fully Convolutional Network for Semantic Segmentation of Remote Sensing Images. Remote Sens., 13.","DOI":"10.3390\/rs13163211"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5633316","DOI":"10.1109\/TGRS.2022.3213925","article-title":"D-TNet: Category-Awareness Based Difference-Threshold Alternative Learning Network for Remote Sensing Image Change Detection","volume":"60","author":"Wan","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","first-page":"199","article-title":"Deep Convolutional Neural Network for Damaged Vegetation Segmentation from RGB Images Based on Virtual NIR-Channel Estimation","volume":"6","author":"Picon","year":"2022","journal-title":"Artif. Intell. Agric."},{"key":"ref_29","first-page":"5400714","article-title":"DWin-HRFormer: A High-Resolution Transformer Model With Directional Windows for Semantic Segmentation of Urban Construction Land","volume":"61","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wang, L., Li, R., Wang, D., Duan, C., Wang, T., and Meng, X. (2021). Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images. Remote Sens., 13.","DOI":"10.3390\/rs13163065"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Akcay, O., Kinaci, A.C., Avsar, E.O., and Aydar, U. (2022). Semantic Segmentation of High-Resolution Airborne Images with Dual-Stream DeepLabV3+. ISPRS Int. J. Geoinf., 11.","DOI":"10.3390\/ijgi11010023"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Sun, Z., Zhou, W., Ding, C., and Xia, M. (2022). Multi-Resolution Transformer Network for Building and Road Segmentation of Remote Sensing Image. ISPRS Int. J. Geoinf., 11.","DOI":"10.3390\/ijgi11030165"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"112096","DOI":"10.1016\/j.rse.2020.112096","article-title":"Mapping Horizontal and Vertical Urban Densification in Denmark with Landsat Time-Series from 1985 to 2018: A Semantic Segmentation Solution","volume":"251","author":"Chen","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"112515","DOI":"10.1016\/j.rse.2021.112515","article-title":"Built-up Area Mapping in China from GF-3 SAR Imagery Based on the Framework of Deep Learning","volume":"262","author":"Wu","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"6124","DOI":"10.1109\/JSTARS.2020.3028158","article-title":"A Framework for Land Use Scenes Classification Based on Landscape Photos","volume":"13","author":"Xu","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1080\/19479832.2023.2199005","article-title":"A Large-Scale Remote Sensing Scene Dataset Construction for Semantic Segmentation","volume":"14","author":"Xu","year":"2023","journal-title":"Int. J. Image Data Fusion"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2529","DOI":"10.1007\/s12145-023-01050-3","article-title":"A Conditional Generative Adversarial Network for Urban Area Classification Using Multi-Source Data","volume":"16","author":"Sirous","year":"2023","journal-title":"Earth Sci. Inf."},{"key":"ref_38","first-page":"937","article-title":"Classification of Buildings from VHR Satellite Images Using Ensemble of U-Net and ResNet","volume":"26","author":"Vasavi","year":"2023","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3002805","DOI":"10.1109\/LGRS.2021.3072589","article-title":"Deep Learning-Based Building Footprint Extraction with Missing Annotations","volume":"19","author":"Kang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Yu, J., Zeng, P., Yu, Y., Yu, H., Huang, L., and Zhou, D. (2022). A Combined Convolutional Neural Network for Urban Land-Use Classification with GIS Data. Remote Sens., 14.","DOI":"10.3390\/rs14051128"},{"key":"ref_41","first-page":"102948","article-title":"Rice Mapping Based on Sentinel-1 Images Using the Coupling of Prior Knowledge and Deep Semantic Segmentation Network: A Case Study in Northeast China from 2019 to 2021","volume":"112","author":"Wei","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Liu, S., Peng, D., Zhang, B., Chen, Z., Yu, L., Chen, J., Pan, Y., Zheng, S., Hu, J., and Lou, Z. (2022). The Accuracy of Winter Wheat Identification at Different Growth Stages Using Remote Sensing. Remote Sens., 14.","DOI":"10.3390\/rs14040893"},{"key":"ref_43","first-page":"100627","article-title":"Irrigated Rice Crop Identification in Southern Brazil Using Convolutional Neural Networks and Sentinel-1 Time Series","volume":"24","author":"Bem","year":"2021","journal-title":"Remote Sens. Appl."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"4553","DOI":"10.1080\/17538947.2023.2275657","article-title":"Semantic Segmentation for Plastic-Covered Greenhouses and Plastic-Mulched Farmlands from VHR Imagery","volume":"16","author":"Niu","year":"2023","journal-title":"Int. J. Digit. Earth"},{"key":"ref_45","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 J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.5194\/essd-13-1211-2021","article-title":"High-Resolution Global Map of Smallholder and Industrial Closed-Canopy Oil Palm Plantations","volume":"13","author":"Descals","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"He, J., Lyu, D., He, L., Zhang, Y., Xu, X., Yi, H., Tian, Q., Liu, B., and Zhang, X. (2023). Combining Object-Oriented and Deep Learning Methods to Estimate Photosynthetic and Non-Photosynthetic Vegetation Cover in the Desert from Unmanned Aerial Vehicle Images with Consideration of Shadows. Remote Sens., 15.","DOI":"10.5194\/egusphere-egu23-2479"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"849531","DOI":"10.3389\/feart.2022.849531","article-title":"Application of Deep Learning in Land Use Classification for Soil Erosion Using Remote Sensing","volume":"10","author":"Wan","year":"2022","journal-title":"Front. Earth Sci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1109\/JSTARS.2022.3225070","article-title":"Burned Area Mapping Using Unitemporal PlanetScope Imagery With a Deep Learning Based Approach","volume":"16","author":"Cho","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"105276","DOI":"10.1016\/j.ssci.2021.105276","article-title":"Predicting Wildfire Burns from Big Geodata Using Deep Learning","volume":"140","author":"Bergado","year":"2021","journal-title":"Saf. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Wang, Z., Yang, P., Liang, H., Zheng, C., Yin, J., Tian, Y., and Cui, W. (2022). Semantic Segmentation and Analysis on Sensitive Parameters of Forest Fire Smoke Using Smoke-Unet and Landsat-8 Imagery. Remote Sens., 14.","DOI":"10.3390\/rs14010045"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Liu, C.-C., Zhang, Y.-C., Chen, P.-Y., Lai, C.-C., Chen, Y.-H., Cheng, J.-H., and Ko, M.-H. (2019). Clouds Classification from Sentinel-2 Imagery with Deep Residual Learning and Semantic Image Segmentation. Remote Sens., 11.","DOI":"10.3390\/rs11020119"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1109\/JSTARS.2022.3226524","article-title":"Multicascaded Feature Fusion-Based Deep Learning Network for Local Climate Zone Classification Based on the So2Sat LCZ42 Benchmark Dataset","volume":"16","author":"Ji","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Ayhan, B., and Kwan, C. (2020). Tree, Shrub, and Grass Classification Using Only RGB Images. Remote Sens., 12.","DOI":"10.3390\/rs12081333"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Maxwell, A.E., Bester, M.S., Guillen, L.A., Ramezan, C.A., Carpinello, D.J., Fan, Y., Hartley, F.M., Maynard, S.M., and Pyron, J.L. (2020). Semantic Segmentation Deep Learning for Extracting Surface Mine Extents from Historic Topographic Maps. Remote Sens., 12.","DOI":"10.3390\/rs12244145"},{"key":"ref_56","first-page":"4401220","article-title":"Deep Feature Enhancement Method for Land Cover With Irregular and Sparse Spatial Distribution Features: A Case Study on Open-Pit Mining","volume":"61","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Lee, S.-H., Han, K.-J., Lee, K., Lee, K.-J., Oh, K.-Y., and Lee, M.-J. (2020). Classification of Landscape Affected by Deforestation Using High-resolution Remote Sensing Data and Deep-learning Techniques. Remote Sens., 12.","DOI":"10.3390\/rs12203372"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Yu, T., Wu, W., Gong, C., and Li, X. (2021). Residual Multi-Attention Classification Network for a Forest Dominated Tropical Landscape Using High-Resolution Remote Sensing Imagery. ISPRS Int. J. Geoinf., 10.","DOI":"10.3390\/ijgi10010022"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Pashaei, M., Kamangir, H., Starek, M.J., and Tissot, P. (2020). Review and Evaluation of Deep Learning Architectures for Efficient Land Cover Mapping with UAS Hyper-Spatial Imagery: A Case Study over a Wetland. Remote Sens., 12.","DOI":"10.3390\/rs12060959"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Fang, B., Chen, G., Chen, J., Ouyang, G., Kou, R., and Wang, L. (2021). Cct: Conditional Co-Training for Truly Unsupervised Remote Sensing Image Segmentation in Coastal Areas. Remote Sens., 13.","DOI":"10.3390\/rs13173521"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Buchsteiner, C., Baur, P.A., and Glatzel, S. (2023). Spatial Analysis of Intra-Annual Reed Ecosystem Dynamics at Lake Neusiedl Using RGB Drone Imagery and Deep Learning. Remote Sens., 15.","DOI":"10.3390\/rs15163961"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"4755","DOI":"10.1109\/JSTARS.2023.3275068","article-title":"Aerial Fluvial Image Dataset for Deep Semantic Segmentation Neural Networks and Its Benchmarks","volume":"16","author":"Wang","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Chen, J., Chen, G., Wang, L., Fang, B., Zhou, P., and Zhu, M. (2020). Coastal Land Cover Classification of High-Resolution Remote Sensing Images Using Attention-Driven Context Encoding Network. Sensors, 20.","DOI":"10.3390\/s20247032"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.isprsjprs.2022.02.013","article-title":"DKDFN: Domain Knowledge-Guided Deep Collaborative Fusion Network for Multimodal Unitemporal Remote Sensing Land Cover Classification","volume":"186","author":"Li","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Tzepkenlis, A., Marthoglou, K., and Grammalidis, N. (2023). Efficient Deep Semantic Segmentation for Land Cover Classification Using Sentinel Imagery. Remote Sens., 15.","DOI":"10.3390\/rs15082027"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Billson, J., Islam, M.D.S., Sun, X., and Cheng, I. (2023). Water Body Extraction from Sentinel-2 Imagery with Deep Convolutional Networks and Pixelwise Category Transplantation. Remote Sens., 15.","DOI":"10.3390\/rs15051253"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"2147","DOI":"10.1109\/JSTARS.2023.3243396","article-title":"A Dual-Branch Deep Learning Architecture for Multisensor and Multitemporal Remote Sensing Semantic Segmentation","volume":"16","author":"Bergamasco","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Yang, X., Zhang, B., Chen, Z., Bai, Y., and Chen, P. (2022). A Multi-Temporal Network for Improving Semantic Segmentation of Large-Scale Landsat Imagery. Remote Sens., 14.","DOI":"10.3390\/rs14195062"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Yang, X., Chen, Z., Zhang, B., Li, B., Bai, Y., and Chen, P. (2022). A Block Shuffle Network with Superpixel Optimization for Landsat Image Semantic Segmentation. Remote Sens., 14.","DOI":"10.3390\/rs14061432"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Boonpook, W., Tan, Y., Nardkulpat, A., Torsri, K., Torteeka, P., Kamsing, P., Sawangwit, U., Pena, J., and Jainaen, M. (2023). Deep Learning Semantic Segmentation for Land Use and Land Cover Types Using Landsat 8 Imagery. ISPRS Int. J. Geoinf., 12.","DOI":"10.3390\/ijgi12010014"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"6361","DOI":"10.1109\/TGRS.2018.2837357","article-title":"Recurrent Multiresolution Convolutional Networks for VHR Image Classification","volume":"56","author":"Bergado","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"100046","DOI":"10.1016\/j.ophoto.2023.100046","article-title":"Automatic Labelling for Semantic Segmentation of VHR Satellite Images: Application of Airborne Laser Scanner Data and Object-Based Image Analysis","volume":"9","author":"Karila","year":"2023","journal-title":"ISPRS Open J. Photogramm. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Zhang, X., Du, L., Tan, S., Wu, F., Zhu, L., Zeng, Y., and Wu, B. (2021). Land Use and Land Cover Mapping Using Rapideye Imagery Based on a Novel Band Attention Deep Learning Method in the Three Gorges Reservoir Area. Remote Sens., 13.","DOI":"10.3390\/rs13061225"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"3251","DOI":"10.1109\/JSTARS.2021.3055784","article-title":"Multitemporal Relearning with Convolutional LSTM Models for Land Use Classification","volume":"14","author":"Zhu","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"56267","DOI":"10.1109\/ACCESS.2022.3175978","article-title":"Land Cover Classification of Resources Survey Remote Sensing Images Based on Segmentation Model","volume":"10","author":"Fan","year":"2022","journal-title":"IEEE Access"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Clark, A., Phinn, S., and Scarth, P. (2023). Pre-Processing Training Data Improves Accuracy and Generalisability of Convolutional Neural Network Based Landscape Semantic Segmentation. Land, 12.","DOI":"10.2139\/ssrn.4329498"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.isprsjprs.2019.03.015","article-title":"A New Fully Convolutional Neural Network for Semantic Segmentation of Polarimetric SAR Imagery in Complex Land Cover Ecosystem","volume":"151","author":"Mohammadimanesh","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Wenger, R., Puissant, A., Weber, J., Idoumghar, L., and Forestier, G. (2023). Multimodal and Multitemporal Land Use\/Land Cover Semantic Segmentation on Sentinel-1 and Sentinel-2 Imagery: An Application on a MultiSenGE Dataset. Remote Sens., 15.","DOI":"10.3390\/rs15010151"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"5950","DOI":"10.1109\/JSTARS.2021.3085122","article-title":"A Benchmark High-Resolution GaoFen-3 SAR Dataset for Building Semantic Segmentation","volume":"14","author":"Xia","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"3811","DOI":"10.1016\/j.asr.2022.07.078","article-title":"Development of a Generalized Model to Classify Various Land Covers for ALOS-2 L-Band Images Using Semantic Segmentation","volume":"70","author":"Kotru","year":"2022","journal-title":"Adv. Space Res."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1080\/10106049.2019.1704072","article-title":"A Novel Approach to Use Semantic Segmentation Based Deep Learning Networks to Classify Multi-Temporal SAR Data","volume":"37","author":"Mehra","year":"2022","journal-title":"Geocarto Int."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Pe\u0161ek, O., Segal-Rozenhaimer, M., and Karnieli, A. (2022). Using Convolutional Neural Networks for Cloud Detection on VEN\u03bcS Images over Multiple Land-Cover Types. Remote Sens., 14.","DOI":"10.3390\/rs14205210"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"10716","DOI":"10.1109\/JSTARS.2021.3116062","article-title":"PSRN: Polarimetric Space Reconstruction Network for PolSAR Image Semantic Segmentation","volume":"14","author":"Jing","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_84","first-page":"4014805","article-title":"A Refined Pyramid Scene Parsing Network for Polarimetric SAR Image Semantic Segmentation in Agricultural Areas","volume":"19","author":"Zhang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_85","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_86","doi-asserted-by":"crossref","first-page":"014520","DOI":"10.1117\/1.JRS.16.014520","article-title":"Land Cover Classification of Synthetic Aperture Radar Images Based on Encoder\u2014Decoder Network with an Attention Mechanism","volume":"16","author":"Zheng","year":"2022","journal-title":"J. Appl. Remote Sens."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"3107","DOI":"10.1109\/JSTARS.2021.3063797","article-title":"Object-Level Semantic Segmentation on the High-Resolution Gaofen-3 FUSAR-Map Dataset","volume":"14","author":"Shi","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"179","DOI":"10.2166\/hydro.2022.134","article-title":"Airborne LiDAR-Assisted Deep Learning Methodology for Riparian Land Cover Classification Using Aerial Photographs and Its Application for Flood Modelling","volume":"24","author":"Yoshida","year":"2022","journal-title":"J. Hydroinformatics"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Arief, H.A., Strand, G.-H., Tveite, H., and Indahl, U.G. (2018). Land Cover Segmentation of Airborne LiDAR Data Using Stochastic Atrous Network. Remote Sens., 10.","DOI":"10.3390\/rs10060973"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"3146","DOI":"10.1080\/01431161.2020.1871100","article-title":"A Semantic Segmentation Method with Category Boundary for Land Use and Land Cover (LULC) Mapping of Very-High Resolution (VHR) Remote Sensing Image","volume":"42","author":"Xu","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_91","first-page":"3002305","article-title":"An Adversarial Domain Adaptation Framework with KL-Constraint for Remote Sensing Land Cover Classification","volume":"19","author":"Liu","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"8825509","DOI":"10.1155\/2020\/8825509","article-title":"Land Cover Classification Using SegNet with Slope, Aspect, and Multidirectional Shaded Relief Images Derived from Digital Surface Model","volume":"2020","author":"Lee","year":"2020","journal-title":"J. Sens."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"5508","DOI":"10.1109\/JSTARS.2020.3023645","article-title":"Bidirectional Grid Fusion Network for Accurate Land Cover Classification of High-Resolution Remote Sensing Images","volume":"13","author":"Wang","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Shi, H., Fan, J., Wang, Y., and Chen, L. (2021). Dual Attention Feature Fusion and Adaptive Context for Accurate Segmentation of Very High-Resolution Remote Sensing Images. Remote Sens., 13.","DOI":"10.3390\/rs13183715"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"34858","DOI":"10.1109\/ACCESS.2022.3163535","article-title":"RSI-Net: Two-Stream Deep Neural Network for Remote Sensing Images-Based Semantic Segmentation","volume":"10","author":"He","year":"2022","journal-title":"IEEE Access"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Yang, N., and Tang, H. (2021). Semantic Segmentation of Satellite Images: A Deep Learning Approach Integrated with Geospatial Hash Codes. Remote Sens., 13.","DOI":"10.3390\/rs13142723"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Boguszewski, A., Batorski, D., Ziemba-Jankowska, N., Dziedzic, T., and Zambrzycka, A. (2021, January 20\u201325). LandCover.Ai: Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagery. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Nashville, TN, USA.","DOI":"10.1109\/CVPRW53098.2021.00121"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"016513","DOI":"10.1117\/1.JRS.16.016513","article-title":"MLNet: Multichannel Feature Fusion Lozenge Network for Land Segmentation","volume":"16","author":"Gao","year":"2022","journal-title":"J. Appl. Remote Sens."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Demir, I., Koperski, K., Lindenbaum, D., Pang, G., Huang, J., Basu, S., Hughes, F., Tuia, D., and Raska, R. (2018, January 18\u201323). DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00031"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Wei, H., Xu, X., Ou, N., Zhang, X., and Dai, Y. (2021). Deanet: Dual Encoder with Attention Network for Semantic Segmentation of Remote Sensing Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13193900"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Maggiori, E., Tarabalka, Y., Charpiat, G., and Alliez, P. (2017, January 23\u201328). Can Semantic Labeling Methods Generalize to Any City? The Inria Aerial Image Labeling Benchmark. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127684"},{"key":"ref_102","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_103","doi-asserted-by":"crossref","first-page":"3816","DOI":"10.1109\/TGRS.2020.3020804","article-title":"Generative Adversarial Network-Based Full-Space Domain Adaptation for Land Cover Classification from Multiple-Source Remote Sensing Images","volume":"59","author":"Ji","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.isprsjprs.2018.11.011","article-title":"Aerial Imagery for Roof Segmentation: A Large-Scale Dataset towards Automatic Mapping of Buildings","volume":"147","author":"Chen","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Audebert, N., Le Saux, B., and Lef\u00e8vre, S. (2017). Segment-before-Detect: Vehicle Detection and Classification through Semantic Segmentation of Aerial Images. Remote Sens., 9.","DOI":"10.3390\/rs9040368"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Abdollahi, A., Pradhan, B., Shukla, N., Chakraborty, S., and Alamri, A. (2021). Multi-Object Segmentation in Complex Urban Scenes from High-Resolution Remote Sensing Data. Remote Sens., 13.","DOI":"10.3390\/rs13183710"},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Khan, S.D., Alarabi, L., and Basalamah, S. (2021). Deep Hybrid Network for Land Cover Semantic Segmentation in High-Spatial Resolution Satellite Images. Information, 12.","DOI":"10.3390\/info12060230"},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Liu, R., Tao, F., Liu, X., Na, J., Leng, H., Wu, J., and Zhou, T. (2022). RAANet: A Residual ASPP with Attention Framework for Semantic Segmentation of High-Resolution Remote Sensing Images. Remote Sens., 14.","DOI":"10.3390\/rs14133109"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.1109\/LGRS.2019.2938555","article-title":"FRF-Net: Land Cover Classification from Large-Scale VHR Optical Remote Sensing Images","volume":"17","author":"Sang","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Guo, Y., Wang, F., Xiang, Y., and You, H. (2021). Article Dgfnet: Dual Gate Fusion Network for Land Cover Classification in Very High-Resolution Images. Remote Sens., 13.","DOI":"10.3390\/rs13183755"},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Niu, X., Zeng, Q., Luo, X., and Chen, L. (2022). FCAU-Net for the Semantic Segmentation of Fine-Resolution Remotely Sensed Images. Remote Sens., 14.","DOI":"10.3390\/rs14010215"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"55609","DOI":"10.1109\/ACCESS.2019.2913442","article-title":"Towards Accurate High Resolution Satellite Image Semantic Segmentation","volume":"7","author":"Wu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Li, J., Wang, H., Zhang, A., and Liu, Y. (2022). Semantic Segmentation of Hyperspectral Remote Sensing Images Based on PSE-UNet Model. Sensors, 22.","DOI":"10.3390\/s22249678"},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Salgueiro, L., Marcello, J., and Vilaplana, V. (2022). SEG-ESRGAN: A Multi-Task Network for Super-Resolution and Semantic Segmentation of Remote Sensing Images. Remote Sens., 14.","DOI":"10.3390\/rs14225862"},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Marsocci, V., Scardapane, S., and Komodakis, N. (2021). MARE: Self-Supervised Multi-Attention REsu-Net for Semantic Segmentation in Remote Sensing. Remote Sens., 13.","DOI":"10.3390\/rs13163275"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"215299","DOI":"10.1109\/ACCESS.2020.3040862","article-title":"Attention Guided Encoder-Decoder Network with Multi-Scale Context Aggregation for Land Cover Segmentation","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"77432","DOI":"10.1109\/ACCESS.2022.3193248","article-title":"A Semantic Segmentation Method for Remote Sensing Images Based on the Swin Transformer Fusion Gabor Filter","volume":"10","author":"Feng","year":"2022","journal-title":"IEEE Access"},{"key":"ref_118","first-page":"3196661","article-title":"Hyperspectral Image Classification Based on Multibranch Attention Transformer Networks","volume":"60","author":"Bai","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"6517505","DOI":"10.1109\/LGRS.2022.3215200","article-title":"Class-Guided Swin Transformer for Semantic Segmentation of Remote Sensing Imagery","volume":"19","author":"Meng","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"105340","DOI":"10.1016\/j.cageo.2023.105340","article-title":"P-Swin: Parallel Swin Transformer Multi-Scale Semantic Segmentation Network for Land Cover Classification","volume":"175","author":"Wang","year":"2023","journal-title":"Comput. Geosci."},{"key":"ref_121","first-page":"4402013","article-title":"High-Resolution Land Cover Mapping through Learning with Noise Correction","volume":"60","author":"Dong","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Shen, X., Weng, L., Xia, M., and Lin, H. (2022). Multi-Scale Feature Aggregation Network for Semantic Segmentation of Land Cover. Remote Sens., 14.","DOI":"10.3390\/rs14236156"},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Luo, Y., Wang, J., Yang, X., Yu, Z., and Tan, Z. (2022). Pixel Representation Augmented through Cross-Attention for High-Resolution Remote Sensing Imagery Segmentation. Remote Sens., 14.","DOI":"10.3390\/rs14215415"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"034511","DOI":"10.1117\/1.JRS.15.034511","article-title":"Land Cover Classification Based on the PSPNet and Superpixel Segmentation Methods with High Spatial Resolution Multispectral Remote Sensing Imagery","volume":"15","author":"Yuan","year":"2021","journal-title":"J. Appl. Remote Sens."},{"key":"ref_125","first-page":"102086","article-title":"A Multi-Level Context-Guided Classification Method with Object-Based Convolutional Neural Network for Land Cover Classification Using Very High Resolution Remote Sensing Images","volume":"88","author":"Zhang","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_126","doi-asserted-by":"crossref","unstructured":"Van den Broeck, W.A.J., Goedem\u00e9, T., and Loopmans, M. (2022). Multiclass Land Cover Mapping from Historical Orthophotos Using Domain Adaptation and Spatio-Temporal Transfer Learning. Remote Sens., 14.","DOI":"10.3390\/rs14235911"},{"key":"ref_127","first-page":"5611412","article-title":"Curriculum-Style Local-to-Global Adaptation for Cross-Domain Remote Sensing Image Segmentation","volume":"60","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_128","first-page":"5400314","article-title":"Multitask Semantic Boundary Awareness Network for Remote Sensing Image Segmentation","volume":"60","author":"Li","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Shan, L., and Wang, W. (2022). DenseNet-Based Land Cover Classification Network with Deep Fusion. IEEE Geosci. Remote Sens. Lett., 19.","DOI":"10.1109\/LGRS.2020.3042199"},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Safarov, F., Temurbek, K., Jamoljon, D., Temur, O., Chedjou, J.C., Abdusalomov, A.B., and Cho, Y.-I. (2022). Improved Agricultural Field Segmentation in Satellite Imagery Using TL-ResUNet Architecture. Sensors, 22.","DOI":"10.3390\/s22249784"},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Liu, Z.-Q., Tang, P., Zhang, W., and Zhang, Z. (2022). CNN-Enhanced Heterogeneous Graph Convolutional Network: Inferring Land Use from Land Cover with a Case Study of Park Segmentation. Remote Sens., 14.","DOI":"10.3390\/rs14195027"},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Wang, D., Yang, R., Liu, H., He, H., Tan, J., Li, S., Qiao, Y., Tang, K., and Wang, X. (2022). HFENet: Hierarchical Feature Extraction Network for Accurate Landcover Classification. Remote Sens., 14.","DOI":"10.3390\/rs14174244"},{"key":"ref_133","first-page":"102557","article-title":"Automated Delineation of Agricultural Field Boundaries from Sentinel-2 Images Using Recurrent Residual U-Net","volume":"105","author":"Zhang","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"5606315","DOI":"10.1109\/TGRS.2021.3095832","article-title":"A Semisupervised CRF Model for CNN-Based Semantic Segmentation with Sparse Ground Truth","volume":"60","author":"Maggiolo","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"117972","DOI":"10.1109\/ACCESS.2020.3005085","article-title":"Modified Semi-Supervised Adversarial Deep Network and Classifier Combination for Segmentation of Satellite Images","volume":"8","author":"Barthakur","year":"2020","journal-title":"IEEE Access"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"6943","DOI":"10.1109\/JSTARS.2022.3199459","article-title":"CCENet: Cascade Class-Aware Enhanced Network for High-Resolution Aerial Imagery Semantic Segmentation","volume":"15","author":"Wang","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"923","DOI":"10.1007\/s11676-021-01375-z","article-title":"Land Cover Classification in a Mixed Forest-Grassland Ecosystem Using LResU-Net and UAV Imagery","volume":"33","author":"Zhang","year":"2022","journal-title":"J. Res."},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"Xu, Y., Wu, L., Xie, Z., and Chen, Z. (2018). Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters. Remote Sens., 10.","DOI":"10.3390\/rs10010144"},{"key":"ref_139","doi-asserted-by":"crossref","unstructured":"Li, L., Yao, J., Liu, Y., Yuan, W., Shi, S., and Yuan, S. (2017). Optimal Seamline Detection for Orthoimage Mosaicking by Combining Deep Convolutional Neural Network and Graph Cuts. Remote Sens., 9.","DOI":"10.3390\/rs9070701"},{"key":"ref_140","doi-asserted-by":"crossref","unstructured":"Cecili, G., De Fioravante, P., Congedo, L., Marchetti, M., and Munaf\u00f2, M. (2022). Land Consumption Mapping with Convolutional Neural Network: Case Study in Italy. Land, 11.","DOI":"10.3390\/land11111919"},{"key":"ref_141","doi-asserted-by":"crossref","unstructured":"Abadal, S., Salgueiro, L., Marcello, J., and Vilaplana, V. (2021). A Dual Network for Super-Resolution and Semantic Segmentation of Sentinel-2 Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13224547"},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"4416","DOI":"10.1080\/01431161.2018.1563840","article-title":"Automated LULC Map Production Using Deep Neural Networks","volume":"40","author":"Henry","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1080\/17538947.2021.2017035","article-title":"An Efficient Built-up Land Expansion Model Using a Modified U-Net","volume":"15","author":"Shojaei","year":"2022","journal-title":"Int. J. Digit. Earth"},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"109695","DOI":"10.1016\/j.asoc.2022.109695","article-title":"A Deep Learning Based Framework for Remote Sensing Image Ground Object Segmentation","volume":"130","author":"Dong","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"016520","DOI":"10.1117\/1.JRS.15.016520","article-title":"Fully Convolutional Densenet with Adversarial Training for Semantic Segmentation of High-Resolution Remote Sensing Images","volume":"15","author":"Guo","year":"2021","journal-title":"J. Appl. Remote Sens."},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"6307","DOI":"10.1080\/01431161.2022.2135410","article-title":"JSH-Net: Joint Semantic Segmentation and Height Estimation Using Deep Convolutional Networks from Single High-Resolution Remote Sensing Imagery","volume":"43","author":"Zhang","year":"2022","journal-title":"Int. J. Remote Sens."},{"key":"ref_147","first-page":"5411520","article-title":"Geographical Supervision Correction for Remote Sensing Representation Learning","volume":"60","author":"Li","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_148","doi-asserted-by":"crossref","unstructured":"Shi, W., Qin, W., and Chen, A. (2022). Towards Robust Semantic Segmentation of Land Covers in Foggy Conditions. Remote Sens., 14.","DOI":"10.3390\/rs14184551"},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"3277","DOI":"10.1080\/01431161.2020.1871094","article-title":"Fast and Accurate Land Cover Classification on Medium Resolution Remote Sensing Images Using Segmentation Models","volume":"42","author":"Zhang","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.isprsjprs.2017.02.011","article-title":"Semantic Segmentation of Forest Stands of Pure Species Combining Airborne Lidar Data and Very High Resolution Multispectral Imagery","volume":"126","author":"Dechesne","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_151","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Lu, W., Cao, J., and Xie, G. (2022). MKANet: An Efficient Network with Sobel Boundary Loss for Land-Cover Classification of Satellite Remote Sensing Imagery. Remote Sens., 14.","DOI":"10.3390\/rs14184514"},{"key":"ref_152","first-page":"5405516","article-title":"Geographical Knowledge-Driven Representation Learning for Remote Sensing Images","volume":"60","author":"Li","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_153","first-page":"102931","article-title":"Weakly Supervised High Spatial Resolution Land Cover Mapping Based on Self-Training with Weighted Pseudo-Labels","volume":"112","author":"Liu","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"6309","DOI":"10.1109\/TGRS.2020.2976658","article-title":"Dense Dilated Convolutions Merging Network for Land Cover Classification","volume":"58","author":"Liu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.isprsjprs.2022.08.008","article-title":"Breaking the Resolution Barrier: A Low-to-High Network for Large-Scale High-Resolution Land-Cover Mapping Using Low-Resolution Labels","volume":"192","author":"Li","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_156","doi-asserted-by":"crossref","unstructured":"Yuan, Q., and Mohd Shafri, H.Z. (2022). Multi-Modal Feature Fusion Network with Adaptive Center Point Detector for Building Instance Extraction. Remote Sens., 14.","DOI":"10.3390\/rs14194920"},{"key":"ref_157","doi-asserted-by":"crossref","unstructured":"Mboga, N., D\u2019aronco, S., Grippa, T., Pelletier, C., Georganos, S., Vanhuysse, S., Wolff, E., Smets, B., Dewitte, O., and Lennert, M. (2021). Domain Adaptation for Semantic Segmentation of Historical Panchromatic Orthomosaics in Central Africa. ISPRS Int. J. Geoinf., 10.","DOI":"10.3390\/ijgi10080523"},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1109\/LGRS.2020.2982783","article-title":"Unsupervised Domain Adaptation of High-Resolution Aerial Images via Correlation Alignment and Self Training","volume":"18","author":"Zhang","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"1080","DOI":"10.1080\/2150704X.2020.1821120","article-title":"Fully Convolutional Neural Nets In-the-Wild","volume":"11","author":"Simms","year":"2020","journal-title":"Remote Sens. Lett."},{"key":"ref_160","first-page":"5000105","article-title":"Multispectral Semantic Land Cover Segmentation from Aerial Imagery with Deep Encoder-Decoder Network","volume":"19","author":"Liu","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"9355","DOI":"10.1080\/10106049.2021.2017017","article-title":"Mapping Land Cover Using a Developed U-Net Model with Weighted Cross Entropy","volume":"37","author":"Sun","year":"2022","journal-title":"Geocarto Int."},{"key":"ref_162","first-page":"102881","article-title":"Semi-Supervised Semantic Segmentation Framework with Pseudo Supervisions for Land-Use\/Land-Cover Mapping in Coastal Areas","volume":"112","author":"Chen","year":"2022","journal-title":"Int. J. Appl. Earth Obs. 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