TY - GEN
T1 - Building 3D city models
T2 - 15th International Conference on Computational Science and its Applications, ICCSA 2015
AU - Rebelo, Carla
AU - Rodrigues, Antònio Manuel
AU - Tenedorio, José Antònio
AU - Goncalves, José Alberto
AU - Marnoto, João
PY - 2015
Y1 - 2015
N2 - Presently, the use of new technologies for the acquisition of 3D geographical data on time is very important for urban planning. Applications include evaluation and monitoring of urban parameters (ie. volumetric data),indicators of an urban plan, or monitoring built-up areas and illegal buildings. This type of 3D data can be acquired through an Airborne Laser Scanning system, also known as LiDAR (Light Detection And Ranging) or by Unmanned Aerial Vehicles (UAV). The aim of this article is to use and compare these two technologies for extracting building parameters (facade height and volume). Existing literature evaluates each technology separately. This work pioneers benchmarking between LiDAR and UAV point-clouds. The basic function of LiDAR is collecting a georeferenced and dense 3D point-cloud from a laser scanner during flight. Therefore it is possible to obtain a similar 3D point-cloud using processing algorithms for stereo aerial images, obtained by large or small-format digital cameras (the small-format camera implemented in Unmanned Aerial Vehicles). The chosen study area is located in Praia de Faro, an open sandy beach in Algarve (Southern Portugal), limited west by the Ria Formosa barrier island system. The area defined has an extension of 300100m. The methodology is divided in two distinct stages: (1) building parameters extraction, (2) comparative technology analysis. Lidar point-cloud resolution is approximately 6 pts/m2 and UAV pointcloud 60 pts/m2. FOSS technologies have proven to be the most adequate adequate platform for the development and diffusion of advanced analytical tools in the Geographical Information Sciences (GISci). Data management in this paper is supported by a Geographical DatabaseManagement System (GDBMS), implemented using PostgreSQL and Post- GIS. Statistical analysis is performed using R whilst advanced spatial functions are used in GRASS.
AB - Presently, the use of new technologies for the acquisition of 3D geographical data on time is very important for urban planning. Applications include evaluation and monitoring of urban parameters (ie. volumetric data),indicators of an urban plan, or monitoring built-up areas and illegal buildings. This type of 3D data can be acquired through an Airborne Laser Scanning system, also known as LiDAR (Light Detection And Ranging) or by Unmanned Aerial Vehicles (UAV). The aim of this article is to use and compare these two technologies for extracting building parameters (facade height and volume). Existing literature evaluates each technology separately. This work pioneers benchmarking between LiDAR and UAV point-clouds. The basic function of LiDAR is collecting a georeferenced and dense 3D point-cloud from a laser scanner during flight. Therefore it is possible to obtain a similar 3D point-cloud using processing algorithms for stereo aerial images, obtained by large or small-format digital cameras (the small-format camera implemented in Unmanned Aerial Vehicles). The chosen study area is located in Praia de Faro, an open sandy beach in Algarve (Southern Portugal), limited west by the Ria Formosa barrier island system. The area defined has an extension of 300100m. The methodology is divided in two distinct stages: (1) building parameters extraction, (2) comparative technology analysis. Lidar point-cloud resolution is approximately 6 pts/m2 and UAV pointcloud 60 pts/m2. FOSS technologies have proven to be the most adequate adequate platform for the development and diffusion of advanced analytical tools in the Geographical Information Sciences (GISci). Data management in this paper is supported by a Geographical DatabaseManagement System (GDBMS), implemented using PostgreSQL and Post- GIS. Statistical analysis is performed using R whilst advanced spatial functions are used in GRASS.
KW - Building parameters
KW - FOSS
KW - LiDAR
KW - Point-cloud
KW - UAV
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U2 - 10.1007/978-3-319-21470-2_26
DO - 10.1007/978-3-319-21470-2_26
M3 - Conference contribution
AN - SCOPUS:84944463919
SN - 9783319214696
VL - 9157
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 367
EP - 379
BT - Computational Science and its Applications - ICCSA 2015 - 15th International Conference, Proceedings
PB - Springer
CY - Cham
Y2 - 22 June 2015 through 25 June 2015
ER -