Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Samira Sadat Saleh"'
Autor:
Saeid Zare Naghadehi, Milad Asadi, Mohammad Maleki, Seyed-Mohammad Tavakkoli-Sabour, John Lodewijk Van Genderen, Samira-Sadat Saleh
Publikováno v:
ISPRS International Journal of Geo-Information, Vol 10, Iss 8, p 513 (2021)
A reliable land cover (LC) map is essential for planners, as missing proper land cover maps may deviate a project. This study is focusing on land cover classification and prediction using three well known classifiers and remote sensing data. Maximum
Externí odkaz:
https://doaj.org/article/898dd07837534a6fbf64e3cabc882a67
Autor:
Mohammad Maleki, Seyed Mohammad Tavakkoli-Sabour, Samira Sadat Saleh, Ehsan Babaee, John van Genderen
Publikováno v:
Geographia Technica. 15:93-105
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9b59072413a89cd8c818495751698760
https://doi.org/10.21163/gt_2020.152.10
https://doi.org/10.21163/gt_2020.152.10
Autor:
Milad Asadi, Amir Oshnooei-Nooshabadi, Samira-Sadat Saleh, Fattaneh Habibnezhad, Sonia Sarafraz-Asbagh, John Lodewijk Van Genderen
Publikováno v:
Sustainability; Volume 14; Issue 23; Pages: 15625
Considering urbanization can lead to irreversible land transformations, it is crucial to provide city managers, environmental resources managers, and even people with accurate predicting land use/land cover (LULC) to accomplish sustainable developmen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a55bac4526efac65b5596deb4c532b5a
https://doi.org/10.3390/su142315625
https://doi.org/10.3390/su142315625
Autor:
Seyed-Mohammad Tavakkoli-Sabour, Saeid Zare Naghadehi, John van Genderen, Samira-Sadat Saleh, Milad Asadi, Mohammad Maleki
Publikováno v:
ISPRS International Journal of Geo-Information
Volume 10
Issue 8
ISPRS International Journal of Geo-Information, Vol 10, Iss 513, p 513 (2021)
Volume 10
Issue 8
ISPRS International Journal of Geo-Information, Vol 10, Iss 513, p 513 (2021)
A reliable land cover (LC) map is essential for planners, as missing proper land cover maps may deviate a project. This study is focusing on land cover classification and prediction using three well known classifiers and remote sensing data. Maximum
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8f3d6ac15c75dc81fb766a48239feafa