4D Monitoring of Active Landslides by Multi-Temporal Airborne LiDAR Data

2013 ◽  
pp. 147-151
Author(s):  
Guido Ventura ◽  
Giuseppe Vilardo ◽  
Carlo Terranova ◽  
Eliana Bellucci Sessa
Keyword(s):  
Airborne Lidar ◽  
Lidar Data ◽  
Multi Temporal ◽  
Airborne Lidar Data

The use of Otsu algorithm and multi-temporal airborne LiDAR data to detect building changes in urban space

2021 ◽  
Author(s):  
Renato César dos Santos ◽  
Mauricio Galo ◽  
André Caceres Carrilho ◽  
Guilherme Gomes Pessoa
Keyword(s):  
Urban Space ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Multi Temporal ◽  
Airborne Lidar Data

scholarly journals AUTOMATIC BUILDING CHANGE DETECTION USING MULTI-TEMPORAL AIRBORNE LIDAR DATA

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 19-24
Author(s):  
R. C. dos Santos ◽  
M. Galo ◽  
A. C. Carrilho ◽  
G. G. Pessoa ◽  
R. A. R. de Oliveira
Keyword(s):  
Change Detection ◽  
Average Density ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Qualitative And Quantitative ◽  
Multi Temporal ◽  
Quantitative Analyses ◽  
3D Information ◽  
Airborne Lidar Data ◽  
Area Monitoring

Abstract. The automatic detection of building changes is an essential process for urban area monitoring, urban planning, and database update. In this context, 3D information derived from multi-temporal airborne LiDAR scanning is one effective alternative. Despite several works in the literature, the separation of change areas in building and non-building remains a challenge. In this sense, it is proposed a new method for building change detection, having as the main contribution the use of height entropy concept to identify the building change areas. The experiments were performed considering multi-temporal airborne LiDAR data from 2012 and 2014, both with average density around 5 points/m2. Qualitative and quantitative analyses indicate that the proposed method is robust in building change detection, having the potential to identify small changes (larger than 20 m2). In general, the change detection method presented average completeness and correctness around 97% and 71%, respectively.

Estimation of forest biomass dynamics in subtropical forests using multi-temporal airborne LiDAR data

2016 ◽  
Vol 178 ◽  
pp. 158-171 ◽  
Author(s):  
Lin Cao ◽  
Nicholas C. Coops ◽  
John L. Innes ◽  
Stephen R.J. Sheppard ◽  
Liyong Fu ◽  
...  
Keyword(s):  
Forest Biomass ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Subtropical Forests ◽  
Multi Temporal ◽  
Biomass Dynamics ◽  
Airborne Lidar Data
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