{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T11:46:27Z","timestamp":1780487187008,"version":"3.54.1"},"reference-count":53,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2013,11,15]],"date-time":"2013-11-15T00:00:00Z","timestamp":1384473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Google Earth (GE) releases free images in high spatial resolution that may provide some potential for regional land use\/cover mapping, especially for those regions with high heterogeneous landscapes. In order to test such practicability, the GE imagery was selected for a case study in Wuhan City to perform an object-based land use\/cover classification. The classification accuracy was assessed by using 570 validation points generated by a random sampling scheme and compared with a parallel classification of QuickBird (QB) imagery based on an object-based classification method. The results showed that GE has an overall classification accuracy of 78.07%, which is slightly lower than that of QB. No significant difference was found between these two classification results by the adoption of Z-test, which strongly proved the potentials of GE in land use\/cover mapping. Moreover, GE has different discriminating capacity for specific land use\/cover types. It possesses some advantages for mapping those types with good spatial characteristics in terms of geometric, shape and context. The object-based method is recommended for imagery classification when using GE imagery for mapping land use\/cover. However, GE has some limitations for those types classified by using only spectral characteristics largely due to its poor spectral characteristics.<\/jats:p>","DOI":"10.3390\/rs5116026","type":"journal-article","created":{"date-parts":[[2013,11,15]],"date-time":"2013-11-15T11:50:00Z","timestamp":1384516200000},"page":"6026-6042","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":257,"title":["Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use\/Cover Mapping"],"prefix":"10.3390","volume":"5","author":[{"given":"Qiong","family":"Hu","sequence":"first","affiliation":[{"name":"Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100081, China"},{"name":"Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenbin","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100081, China"},{"name":"Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tian","family":"Xia","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100081, China"},{"name":"Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiangyi","family":"Yu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100081, China"},{"name":"Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100081, China"},{"name":"Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengguo","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100081, China"},{"name":"Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Song","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100081, China"},{"name":"Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2013,11,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3264","DOI":"10.1016\/j.rse.2011.07.010","article-title":"Land cover classification with coarse spatial resolution data to derive continuous and discrete maps for complex regions","volume":"115","author":"Colditz","year":"2011","journal-title":"Remote Sens. 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