RT info:eu-repo/semantics/article
T1 Change detection of buildings from satellite imagery and lidar data
A1 Malpica Velasco, José A.
A1 Alonso Rodríguez, María Concepción
A1 Papí Montanel, Francisco
A1 Arozarena, Antonio
A1 Martínez de Aguirre Escobar, Alejandro
K1 Geospatial objects
K1 Cartography
K1 Satellite imagery
K1 Laser scanner data
K1 Ciencia
K1 Matemáticas
K1 Mathematics
AB Geospatial objects change over time and this necessitates periodic  updating of the cartography that represents them. Currently, this  updating is done manually, by interpreting aerial photographs, but this  is an expensive and time-consuming process. While several kinds of  geospatial objects are recognized, this article focuses on buildings.  Specifically, we propose a novel automatic approach for detecting  buildings that uses satellite imagery and laser scanner data as a tool  for updating buildings for a vector geospatial database. We apply the  support vector machine (SVM) classification algorithm to a joint  satellite and laser data set for the extraction of buildings. SVM  training is automatically carried out from the vector geospatial  database. For visualization purposes, the changes are presented using a  variation of the traffic-light map. The different colours assist human  operators in performing the final cartographic updating. Most of the  important changes were detected by the proposed method. The method not  only detects changes, but also identifies inaccuracies in the  cartography of the vector database. Small houses and low buildings  surrounded by high trees present significant problems with regard to  automatic detection compared to large houses and taller buildings. In  addition to visual evaluation, this study was checked for completeness  and correctness using numerical evaluation and receiver operating  characteristic curves. The high values obtained for these parameters  confirmed the efficacy of the method.
SN 0143-1161
YR 2013
FD 2013-03-10
LK http://hdl.handle.net/10017/32223
UL http://hdl.handle.net/10017/32223
LA eng
DS MINDS@UW
RD 24-abr-2024