{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T17:05:59Z","timestamp":1774631159064,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,20]],"date-time":"2024-01-20T00:00:00Z","timestamp":1705708800000},"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>Segmentation of Agricultural Remote Sensing Images (ARSIs) stands as a pivotal component within the intelligent development path of agricultural information technology. Similarly, quick and effective delineation of urban green spaces (UGSs) in high-resolution images is also increasingly needed as input in various urban simulation models. Numerous segmentation algorithms exist for ARSIs and UGSs; however, a model with exceptional generalization capabilities and accuracy remains elusive. Notably, the newly released Segment Anything Model (SAM) by META AI is gaining significant recognition in various domains for segmenting conventional images, yielding commendable results. Nevertheless, SAM\u2019s application in ARSI and UGS segmentation has been relatively limited. ARSIs and UGSs exhibit distinct image characteristics, such as prominent boundaries, larger frame sizes, and extensive data types and volumes. Presently, there is a dearth of research on how SAM can effectively handle various ARSI and UGS image types and deliver superior segmentation outcomes. Thus, as a novel attempt in this paper, we aim to evaluate SAM\u2019s compatibility with a wide array of ARSI and UGS image types. The data acquisition platform comprises both aerial and spaceborne sensors, and the study sites encompass most regions of the United States, with images of varying resolutions and frame sizes. It is noteworthy that the segmentation effect of SAM is significantly influenced by the content of the image, as well as the stability and accuracy across images of different resolutions and sizes. However, in general, our findings indicate that resolution has a minimal impact on the effectiveness of conditional SAM-based segmentation, maintaining an overall segmentation accuracy above 90%. In contrast, the unsupervised segmentation approach, SAM, exhibits performance issues, with around 55% of images (3 m and coarser resolutions) experiencing lower accuracy on low-resolution images. Whereas frame size exerts a more substantial influence, as the image size increases, the accuracy of unsupervised segmentation methods decreases extremely fast, and conditional segmentation methods also show some degree of degradation. Additionally, SAM\u2019s segmentation efficacy diminishes considerably in the case of images featuring unclear edges and minimal color distinctions. Consequently, we propose enhancing SAM\u2019s capabilities by augmenting the training dataset and fine-tuning hyperparameters to align with the demands of ARSI and UGS image segmentation. Leveraging the multispectral nature and extensive data volumes of remote sensing images, the secondary development of SAM can harness its formidable segmentation potential to elevate the overall standard of ARSI and UGS image segmentation.<\/jats:p>","DOI":"10.3390\/rs16020414","type":"journal-article","created":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T06:49:31Z","timestamp":1705906171000},"page":"414","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Evaluating the Efficacy of Segment Anything Model for Delineating Agriculture and Urban Green Spaces in Multiresolution Aerial and Spaceborne Remote Sensing Images"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8167-3860","authenticated-orcid":false,"given":"Baoling","family":"Gui","sequence":"first","affiliation":[{"name":"School of Geosciences, University of Aberdeen, Aberdeen AB24 3UF, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2502-6384","authenticated-orcid":false,"given":"Anshuman","family":"Bhardwaj","sequence":"additional","affiliation":[{"name":"School of Geosciences, University of Aberdeen, Aberdeen AB24 3UF, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3181-2960","authenticated-orcid":false,"given":"Lydia","family":"Sam","sequence":"additional","affiliation":[{"name":"School of Geosciences, University of Aberdeen, Aberdeen AB24 3UF, UK"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"024019","DOI":"10.1088\/1748-9326\/ab68ac","article-title":"Estimating and Understanding Crop Yields with Explainable Deep Learning in the Indian Wheat Belt","volume":"15","author":"Wolanin","year":"2020","journal-title":"Environ. Res. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1007\/s12145-022-00887-4","article-title":"Simulating Crop Yield Estimation and Prediction through Geospatial Data for Specific Regional Analysis","volume":"16","author":"Mathivanan","year":"2023","journal-title":"Earth Sci. Inform."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.scs.2015.04.001","article-title":"Recent Challenges in Modeling of Urban Heat Island","volume":"19","author":"Mirzaei","year":"2015","journal-title":"Sustain. Cities Soc."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Liu, J., Zhang, L., and Zhang, Q. (2020). The Development Simulation of Urban Green Space System Layout Based on the Land Use Scenario: A Case Study of Xuchang City, China. Sustainability, 12.","DOI":"10.3390\/su12010326"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"102698","DOI":"10.1016\/j.scs.2020.102698","article-title":"Predicting the Surface Urban Heat Island Intensity of Future Urban Green Space Development Using a Multi-Scenario Simulation","volume":"66","author":"Liu","year":"2021","journal-title":"Sustain. Cities Soc."},{"key":"ref_6","unstructured":"Luo, Z., Yang, W., Yuan, Y., Gou, R., and Li, X. (2023). Information Processing in Agriculture, Elsevier."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.rse.2015.12.029","article-title":"UAVs as Remote Sensing Platform in Glaciology: Present Applications and Future Prospects","volume":"175","author":"Bhardwaj","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Gaffey, C., and Bhardwaj, A. (2020). Applications of Unmanned Aerial Vehicles in Cryosphere: Latest Advances and Prospects. Remote Sens., 12.","DOI":"10.3390\/rs12060948"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Bhardwaj, A., Sam, L., Mart\u00edn-Torres, F.J., Zorzano, M.-P., and Ram\u00edrez Luque, J.A. (2019). UAV Imaging of a Martian Brine Analogue Environment in a Fluvio-Aeolian Setting. Remote Sens., 11.","DOI":"10.3390\/rs11182104"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Sam, L., Bhardwaj, A., Singh, S., Martin-Torres, F.J., Zorzano, M.-P., and Ram\u00edrez Luque, J.A. (2020). Small Lava Caves as Possible Exploratory Targets on Mars: Analogies Drawn from UAV Imaging of an Icelandic Lava Field. Remote Sens., 12.","DOI":"10.3390\/rs12121970"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1016\/j.compag.2015.09.011","article-title":"Discrete Wavelets Transform for Improving Greenness Image Segmentation in Agricultural Images","volume":"118","author":"Guijarro","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"David, L.C.G., and Ballado, A.H. (2016, January 25\u201327). Vegetation Indices and Textures in Object-Based Weed Detection from UAV Imagery. Proceedings of the 2016 6th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), Penang, Malaysia.","DOI":"10.1109\/ICCSCE.2016.7893584"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"106977","DOI":"10.1016\/j.compag.2022.106977","article-title":"A deep learning image segmentation model for agricultural irrigation system classification","volume":"198","author":"Raei","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"105446","DOI":"10.1016\/j.compag.2020.105446","article-title":"Vine Disease Detection in UAV Multispectral Images Using Optimized Image Registration and Deep Learning Segmentation Approach","volume":"174","author":"Kerkech","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1007\/s11119-020-09777-5","article-title":"Semantic Segmentation of Citrus-Orchard Using Deep Neural Networks and Multispectral UAV-Based Imagery","volume":"22","author":"Osco","year":"2021","journal-title":"Precis. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5189","DOI":"10.1109\/ACCESS.2019.2960873","article-title":"CRowNet: Deep Network for Crop Row Detection in UAV Images","volume":"8","author":"Bah","year":"2020","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2612","DOI":"10.1109\/JSTARS.2019.2906387","article-title":"Densely Based Multi-Scale and Multi-Modal Fully Convolutional Networks for High-Resolution Remote-Sensing Image Semantic Segmentation","volume":"12","author":"Peng","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"107994","DOI":"10.1016\/j.compag.2023.107994","article-title":"Instance Segmentation Method for Weed Detection Using UAV Imagery in Soybean Fields","volume":"211","author":"Xu","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2038","DOI":"10.1016\/j.neuroimage.2011.04.014","article-title":"Manual, Semi-Automated, and Automated Delineation of Chronic Brain Lesions: A Comparison of Methods","volume":"56","author":"Wilke","year":"2011","journal-title":"NeuroImage"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"159","DOI":"10.14358\/PERS.72.2.159","article-title":"Comparison of Automated Watershed Delineations","volume":"72","author":"Baker","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_21","unstructured":"Bolch, T., Buchroithner, M., Kunert, A., and Kamp, U. (2007, January 4\u20137). Automated Delineation of Debris-Covered Glaciers Based on ASTER Data. Proceedings of the 27th EARSeL Symposium, Bolzano, Italy."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/j.isprsjprs.2021.01.020","article-title":"Remote Sensing Image Segmentation Advances: A Meta-Analysis","volume":"173","author":"Kotaridis","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wu, J., Zhang, Y., Fu, R., Fang, H., Liu, Y., Wang, Z., Xu, Y., and Jin, Y. (2023). Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation. arXiv.","DOI":"10.2139\/ssrn.4495221"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"102918","DOI":"10.1016\/j.media.2023.102918","article-title":"Segment Anything Model for Medical Image Analysis: An Experimental Study","volume":"89","author":"Mazurowski","year":"2023","journal-title":"Med. Image Anal."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"103061","DOI":"10.1016\/j.media.2023.103061","article-title":"Segment Anything Model for Medical Images?","volume":"92","author":"Huang","year":"2023","journal-title":"Med. Image Anal."},{"key":"ref_26","unstructured":"Roy, S., Wald, T., Koehler, G., Rokuss, M.R., Disch, N., Holzschuh, J., Zimmerer, D., and Maier-Hein, K.H. (2023). SAM.MD: Zero-Shot Medical Image Segmentation Capabilities of the Segment Anything Model. arXiv."},{"key":"ref_27","unstructured":"Hu, M., Li, Y., and Yang, X. (2023). SkinSAM: Empowering Skin Cancer Segmentation with Segment Anything Model. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., and Lo, W.-Y. (2023). Segment Anything. arXiv.","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Liang, F., Wu, B., Dai, X., Li, K., Zhao, Y., Zhang, H., Zhang, P., Vajda, P., and Marculescu, D. (2023). Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP. arXiv.","DOI":"10.1109\/CVPR52729.2023.00682"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., and Mao, S. (2023). TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs. arXiv.","DOI":"10.34133\/icomputing.0063"},{"key":"ref_31","unstructured":"Liu, S., Zeng, Z., Ren, T., Li, F., Zhang, H., Yang, J., Li, C., Yang, J., Su, H., and Zhu, J. (2023). Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection. arXiv."},{"key":"ref_32","unstructured":"Liu, L. (2023). A Comprehensive Survey on Segment Anything Model for Vision and Beyond. arXiv."},{"key":"ref_33","unstructured":"Mo, S., and Tian, Y. (2023). AV-SAM: Segment Anything Model Meets Audio-Visual Localization and Segmentation. arXiv."},{"key":"ref_34","unstructured":"Ahmadi, M., Lonbar, A.G., Sharifi, A., Beris, A.T., Nouri, M., and Javidi, A.S. (2023). Application of Segment Anything Model for Civil Infrastructure Defect Assessment. arXiv, Available online: https:\/\/arxiv.org\/abs\/2304.12600v1."},{"key":"ref_35","unstructured":"Zhang, Z., Wei, Z., Zhang, S., Dai, Z., and Zhu, S. (2023). UVOSAM: A Mask-Free Paradigm for Unsupervised Video Object Segmentation via Segment Anything Model. arXiv, Available online: https:\/\/arxiv.org\/abs\/2305.12659v1."},{"key":"ref_36","unstructured":"Ren, S., Luzi, F., Lahrichi, S., Kassaw, K., Collins, L.M., Bradbury, K., and Malof, J.M. (2023). Segment Anything, from Space?. arXiv, Available online: https:\/\/arxiv.org\/abs\/2304.13000v4."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"115797","DOI":"10.1016\/j.icarus.2023.115797","article-title":"A Flexible Deep Learning Crater Detection Scheme Using Segment Anything Model (SAM)","volume":"408","author":"Giannakis","year":"2024","journal-title":"Icarus"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/S0034-4257(01)00295-4","article-title":"Status of Land Cover Classification Accuracy Assessment","volume":"80","author":"Foody","year":"2002","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/2\/414\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:46:33Z","timestamp":1760103993000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/2\/414"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,20]]},"references-count":38,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["rs16020414"],"URL":"https:\/\/doi.org\/10.3390\/rs16020414","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,20]]}}}