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

Kato, Lilian

9786202514187: Integrating Openstreetmap Data: in Object Based Land-Cover and Land-Use Classification for Disaster Recovery

Couverture souple

ISBN 10 : 6202514183 ISBN 13 : 9786202514187

Editeur : LAP LAMBERT Academic Publishing, 2020

Afficher les exemplaires de cette �dition comportant l'ISBN

0 D'occasion

6 Neuf

De EUR 39,90

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.

Les informations fournies dans la section � Synopsis � peuvent faire r�f�rence � une autre �dition de ce titre.

�diteur: LAP LAMBERT Academic Publishing
Date d'�dition: 2020
Langue: anglais
ISBN 10: 6202514183
ISBN 13: 9786202514187
Reliure: Broch�
Nombre de pages: 76
Coordonn�es du fabricant: non disponible
Personne responsable: KreGa Onlinehandel GmbH
4950000000000
info@krega-onlinehandel.de

Birkenweg 31
Brakel
33034
Allemagne

R�sultats de recherche pour Integrating Openstreetmap Data: in Object Based Land-Cover...

Image fournie par le vendeur

Integrating Openstreetmap Data

Lilian Kato

Edit� par LAP LAMBERT Academic Publishing Mrz 2020, 2020

ISBN 10 : 6202514183 ISBN 13 : 9786202514187

Neuf Taschenbuch

impression � la demande

Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Taschenbuch. Etat : 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. N� de r�f. du vendeur 9786202514187

Contacter le vendeur

Acheter neuf

EUR 39,90

Exp�dition �EUR 23
Exp�dition depuis Allemagne vers Etats-Unis

Quantit� disponible : 2 disponible(s)

Ajouter au panier

Image fournie par le vendeur

Integrating Openstreetmap Data

Lilian Kato

Edit� par LAP LAMBERT Academic Publishing, 2020

ISBN 10 : 6202514183 ISBN 13 : 9786202514187

Neuf Couverture souple

impression � la demande

Vendeur : moluna, Greven, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Etat : 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. N� de r�f. du vendeur 385945223

Contacter le vendeur

Acheter neuf

EUR 34,25

Exp�dition �EUR 48,99
Exp�dition depuis Allemagne vers Etats-Unis

Quantit� disponible : Plus de 20 disponibles

Ajouter au panier

Image fournie par le vendeur

Integrating Openstreetmap Data

Lilian Kato

Edit� par LAP LAMBERT Academic Publishing M�r 2020, 2020

ISBN 10 : 6202514183 ISBN 13 : 9786202514187

Neuf Taschenbuch

impression � la demande

Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Taschenbuch. Etat : 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 Landstra�e 99, 66123 Saarbr�cken 76 pp. Englisch. N� de r�f. du vendeur 9786202514187

Contacter le vendeur

Acheter neuf

EUR 39,90

Exp�dition �EUR 60
Exp�dition depuis Allemagne vers Etats-Unis

Quantit� disponible : 1 disponible(s)

Ajouter au panier

Image fournie par le vendeur

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

Lilian Kato

Edit� par LAP LAMBERT Academic Publishing, 2020

ISBN 10 : 6202514183 ISBN 13 : 9786202514187

Neuf Taschenbuch

impression � la demande

Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Taschenbuch. Etat : 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. N� de r�f. du vendeur 9786202514187

Contacter le vendeur