Könyv Integrating Openstreetmap Data Lilian Kato

Integrating Openstreetmap Data

in Object Based Land-Cover and Land-Use Classification for Disaster Recovery

Szerző: Lilian Kato
Nyelv: Angol
Kötés: Puha kötésű
Elérhetőség: Beszállítói készleten
Küldés 5-8 napon belül
13 361 Ft
Land cover and land use (LCLU) change is a very important indicator that can assist in the monitorin...

Információk a könyvről

Szerző
Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
2020
oldal
76
EAN
9786202514187
Enbook ID
28720111
Súly
122
Méretek
152 x 229 x 5

Teljes leírás

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.

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