Name
Abstract | ||
|---|---|---|
LIDAR (LIght Detection And Ranging) data are a primary data source for digital terrain model (DTM) generation and 3D city models. This paper presents an AdaBoost algorithm for the identification of rules for the classification of raw LIDAR data mainly as buildings, ground and vegetation. First raw data are filtered, interpolated over a grid and segmented. Then geometric and topological relationships among regions resulting from segmentation constitute the input to the tree-structured classification algorithm. Results obtained on data sets gathered over the town of Pavia (Italy) are compared with those obtained by a rule-based approach previously presented by the authors for the classification of the regions. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/ICIAP.2007.31 | ICIAP |
Keywords | Field | DocType |
rule-based approach,digital terrain model,adaboost algorithm,topological relationship,tree-structured classification algorithm,automatic extraction,city model,primary data source,light detection,raw lidar data,raw data,lidar data classification rule,radar imaging,tree data structures,learning artificial intelligence,interpolation,rule based,tree structure,image classification | Data mining,Data set,Computer science,Raw data,Digital elevation model,Lidar,Artificial intelligence,Contextual image classification,Computer vision,Radar imaging,Pattern recognition,Segmentation,3D city models | Conference |
ISBN | Citations | PageRank |
0-7695-2877-5 | 2 | 0.55 |
References | Authors | |
7 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Primo Zingaretti | 1 | 289 | 44.00 |
| Emanuele Frontoni | 2 | 248 | 47.04 |
| Gianfranco Forlani | 3 | 31 | 2.88 |
| Carla Nardinocchi | 4 | 34 | 4.16 |