{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T04:31:18Z","timestamp":1782275478536,"version":"3.54.5"},"reference-count":33,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,1,7]],"date-time":"2023-01-07T00:00:00Z","timestamp":1673049600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Faculty of Social Science, Srinakharinwirot University","award":["474\/2564"],"award-info":[{"award-number":["474\/2564"]}]},{"name":"Faculty of Social Science, Srinakharinwirot University","award":["2565-02-18-001"],"award-info":[{"award-number":["2565-02-18-001"]}]},{"name":"Faculty of Social Science, Srinakharinwirot University","award":["JRA-CO-2564-15158-TH"],"award-info":[{"award-number":["JRA-CO-2564-15158-TH"]}]},{"name":"King Mongkut\u2019s Institute of Technology Ladkrabang","award":["474\/2564"],"award-info":[{"award-number":["474\/2564"]}]},{"name":"King Mongkut\u2019s Institute of Technology Ladkrabang","award":["2565-02-18-001"],"award-info":[{"award-number":["2565-02-18-001"]}]},{"name":"King Mongkut\u2019s Institute of Technology Ladkrabang","award":["JRA-CO-2564-15158-TH"],"award-info":[{"award-number":["JRA-CO-2564-15158-TH"]}]},{"name":"National Science and Technology Development Agency","award":["474\/2564"],"award-info":[{"award-number":["474\/2564"]}]},{"name":"National Science and Technology Development Agency","award":["2565-02-18-001"],"award-info":[{"award-number":["2565-02-18-001"]}]},{"name":"National Science and Technology Development Agency","award":["JRA-CO-2564-15158-TH"],"award-info":[{"award-number":["JRA-CO-2564-15158-TH"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Using deep learning semantic segmentation for land use extraction is the most challenging problem in medium spatial resolution imagery. This is because of the deep convolution layer and multiple levels of deep steps of the baseline network, which can cause a degradation problem in small land use features. In this paper, a deep learning semantic segmentation algorithm which comprises an adjustment network architecture (LoopNet) and land use dataset is proposed for automatic land use classification using Landsat 8 imagery. The experimental results illustrate that deep learning semantic segmentation using the baseline network (SegNet, U-Net) outperforms pixel-based machine learning algorithms (MLE, SVM, RF) for land use classification. Furthermore, the LoopNet network, which comprises a convolutional loop and convolutional block, is superior to other baseline networks (SegNet, U-Net, PSPnet) and improvement networks (ResU-Net, DeeplabV3+, U-Net++), with 89.84% overall accuracy and good segmentation results. The evaluation of multispectral bands in the land use dataset demonstrates that Band 5 has good performance in terms of extraction accuracy, with 83.91% overall accuracy. Furthermore, the combination of different spectral bands (Band 1\u2013Band 7) achieved the highest accuracy result (89.84%) compared to individual bands. These results indicate the effectiveness of LoopNet and multispectral bands for land use classification using Landsat 8 imagery.<\/jats:p>","DOI":"10.3390\/ijgi12010014","type":"journal-article","created":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T02:31:30Z","timestamp":1673231490000},"page":"14","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":64,"title":["Deep Learning Semantic Segmentation for Land Use and Land Cover Types Using Landsat 8 Imagery"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5267-9380","authenticated-orcid":false,"given":"Wuttichai","family":"Boonpook","sequence":"first","affiliation":[{"name":"Department of Geography, Faculty of Social Sciences, Srinakharinwirot University, Bangkok 10110, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0447-8223","authenticated-orcid":false,"given":"Yumin","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Transportation Science and Engineering, Beihang University, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9452-4387","authenticated-orcid":false,"given":"Attawut","family":"Nardkulpat","sequence":"additional","affiliation":[{"name":"Department of Geography, Faculty of Social Sciences, Srinakharinwirot University, Bangkok 10110, Thailand"},{"name":"Faculty of Geoinformatics, Burapha University, Chonburi 20131, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2795-806X","authenticated-orcid":false,"given":"Kritanai","family":"Torsri","sequence":"additional","affiliation":[{"name":"Hydro-Informatics Institute, Ministry of Higher Education, Science, Research and Innovation, Bangkok 10900, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7454-6776","authenticated-orcid":false,"given":"Peerapong","family":"Torteeka","sequence":"additional","affiliation":[{"name":"National Astronomical Research Institute of Thailand, Chiang Mai 50180, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6656-8406","authenticated-orcid":false,"given":"Patcharin","family":"Kamsing","sequence":"additional","affiliation":[{"name":"Air-Space Control, Optimization and Management Laboratory, Department of Aeronautical Engineering, International Academy of Aviation Industry, King Mongkut\u2019s Institute of Technology, Ladkrabang, Bangkok 10520, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9852-3667","authenticated-orcid":false,"given":"Utane","family":"Sawangwit","sequence":"additional","affiliation":[{"name":"National Astronomical Research Institute of Thailand, Chiang Mai 50180, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jose","family":"Pena","sequence":"additional","affiliation":[{"name":"Venezuela Space Agency (ABAE), Caracas 1010, Venezuela"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Montri","family":"Jainaen","sequence":"additional","affiliation":[{"name":"Faculty of Management Science, Kamphaeng Phet Rajabhat University, Kamphaeng Phet 62000, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"43","DOI":"10.2478\/jengeo-2020-0005","article-title":"Machine learning techniques for land use\/land cover classification of medium resolution optical satellite imagery focusing on temporary inundated areas","volume":"13","author":"Tobak","year":"2020","journal-title":"J. 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