Integrating Openstreetmap Data: in Object Based Land-Cover and Land-Use Classification for Disaster Recovery - Brossura

Kato, Lilian

Brossura

ISBN 10: 6202514183 ISBN 13: 9786202514187

Casa editrice: LAP LAMBERT Academic Publishing, 2020

Riassunto

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.

Dettagli dell'edizione

• Editore: LAP LAMBERT Academic Publishing
• Data di pubblicazione: 2020
• Lingua: Inglese
• ISBN 10: 6202514183
• ISBN 13: 9786202514187
• Rilegatura: Copertina flessibile
• Numero di pagine: 76
• Contatto del produttore: non disponibile
• Persona responsabile: KreGa Onlinehandel GmbH; 4950000000000; info@krega-onlinehandel.de; ; Birkenweg 31; Brakel; 33034; Germania