Isbn: 9786202514187 - integrating openstreetmap data: in object based land-cover and land-use classification for disaster recovery (6 resultados)

Taschenbuch. Condición: Neu. Integrating Openstreetmap Data | in Object Based Land-Cover and Land-Use Classification for Disaster Recovery | Lilian Kato | Taschenbuch | 76 S. | Englisch | 2020 | LAP LAMBERT Academic Publishing | EAN 9786202514187 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. …

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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Land cover and land use (LCLU) change is a very important indicator that can assist in the monitoring and assessment of both physical and functional recovery. Its fundamental approach rests on change assessment comparing the situation before, the immediate post-disaster situation and later the recovery stage through image classification. Remote sensing (RS) imagery provides spatial, spectral and contextual information that can assist in the LCLU classification. However, this works well with the classification of land cover (LC) but not for land use (LU) as it fails to capture information on the building use and function which are highly significant in LU classification. OpenStreetMap (OSM) data have been an emerging data source for providing base map information (roads, buildings, etc) on the ground. Most of the studies conducted using OSM data have been mainly concentrated on OSM data quality issues, but the potential of OSM data in disaster recovery assessment has been less explored. Therefore, the main objective of this study was to investigate the potential of using RS imagery and OSM data in LCLU classification for improving the understanding of post-disaster recovery. 76 pp. Englisch.…

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Land cover and land use (LCLU) change is a very important indicator that can assist in the monitoring and assessment of both physical and functional recovery. Its fundamental approach rests on change assessment comparing the situation before, the immediate post-disaster situation and later the recovery stage through image classification. Remote sensing (RS) imagery provides spatial, spectral and contextual information that can assist in the LCLU classification. However, this works well with the classification of land cover (LC) but not for land use (LU) as it fails to capture information on the building use and function which are highly significant in LU classification. OpenStreetMap (OSM) data have been an emerging data source for providing base map information (roads, buildings, etc) on the ground. Most of the studies conducted using OSM data have been mainly concentrated on OSM data quality issues, but the potential of OSM data in disaster recovery assessment has been less explored. Therefore, the main objective of this study was to investigate the potential of using RS imagery and OSM data in LCLU classification for improving the understanding of post-disaster recovery.…

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Kato LilianLilian Kato is an assistant lecturer at the University of Dar es Salaam. Professionally she is a Land Surveyor and GIS & Remote Sensing Expert.Land cover and land use (LCLU) change is a very important indicator that ca.…

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Land cover and land use (LCLU) change is a very important indicator that can assist in the monitoring and assessment of both physical and functional recovery. Its fundamental approach rests on change assessment comparing the situation before, the immediate post-disaster situation and later the recovery stage through image classification. Remote sensing (RS) imagery provides spatial, spectral and contextual information that can assist in the LCLU classification. However, this works well with the classification of land cover (LC) but not for land use (LU) as it fails to capture information on the building use and function which are highly significant in LU classification. OpenStreetMap (OSM) data have been an emerging data source for providing base map information (roads, buildings, etc) on the ground. Most of the studies conducted using OSM data have been mainly concentrated on OSM data quality issues, but the potential of OSM data in disaster recovery assessment has been less explored. Therefore, the main objective of this study was to investigate the potential of using RS imagery and OSM data in LCLU classification for improving the understanding of post-disaster recovery.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 76 pp. Englisch.…