{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T11:40:57Z","timestamp":1774352457460,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,3,30]],"date-time":"2023-03-30T00:00:00Z","timestamp":1680134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61501470"],"award-info":[{"award-number":["61501470"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Natural Science BasicResearch Plan in Shaanxi Province of China","award":["2021JQ-374"],"award-info":[{"award-number":["2021JQ-374"]}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Mapping is a fundamental application of remote sensing images, and the accurate evaluation of remote sensing image information extraction using artificial intelligence is critical. However, the existing evaluation method, based on Intersection over Union (IoU), is limited in evaluating the extracted information\u2019s boundary accuracy. It is insufficient for determining mapping accuracy. Furthermore, traditional remote sensing mapping methods struggle to match the inflection points encountered in artificial intelligence contour extraction. In order to address these issues, we propose the mean inflection point distance (MPD) as a new segmentation evaluation method. MPD can accurately calculate error values and solve the problem of multiple inflection points, which traditional remote sensing mapping cannot match. We tested three algorithms on the Vaihingen dataset: Mask R-CNN, Swin Transformer, and PointRend. The results show that MPD is highly sensitive to mapping accuracy, can calculate error values accurately, and is applicable for different scales of mapping accuracy while maintaining high visual consistency. This study helps to assess the accuracy of automatic mapping using remote sensing artificial intelligence.<\/jats:p>","DOI":"10.3390\/rs15071848","type":"journal-article","created":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T01:37:02Z","timestamp":1680226622000},"page":"1848","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Mean Inflection Point Distance: Artificial Intelligence Mapping Accuracy Evaluation Index\u2014An Experimental Case Study of Building Extraction"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5368-8713","authenticated-orcid":false,"given":"Ding","family":"Yu","sequence":"first","affiliation":[{"name":"Xi\u2019an Research Institute of High Technology, Xi\u2019an 710025, China"}]},{"given":"Aihua","family":"Li","sequence":"additional","affiliation":[{"name":"Xi\u2019an Research Institute of High Technology, Xi\u2019an 710025, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8469-338X","authenticated-orcid":false,"given":"Yinping","family":"Long","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, Chengdu University of Information Technology, Chengdu 610225, China"}]},{"given":"Yan","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430072, China"}]},{"given":"Jinrui","family":"Li","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Petroleum Geochemistry and Environment, Yangtze University, Wuhan 430100, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3035-7727","authenticated-orcid":false,"given":"Xiongwu","family":"Xiao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430072, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1109\/JPROC.2012.2211551","article-title":"Land-Cover Mapping by Markov Modeling of Spatial\u2013Contextual Information in Very-High-Resolution Remote Sensing Images","volume":"101","author":"Moser","year":"2013","journal-title":"Proc. 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