A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Urban Point Cloud Mining Based on Density Clustering and MapReduce
AbstractThis paper proposes an approach to classify, localize, and extract automatically urban objects such as buildings and the ground surface from a digital surface model created from aerial laser scanning data. To achieve that, the approach involves three steps: (1) dividing the original data into smaller, more manageable pieces using a method based on MapReduce gridding for subspace partitioning, (2) applying the DBSCAN algorithm to identify interesting subspaces depending on point density, and (3) grouping of identified subspaces to form potential objects. Validation of the method was conducted in an architecturally dense and complex portion of Dublin, Ireland. The best results were achieved with a 1-m3-sized clustering cube, for which the number of classified clusters most closely equaled that which was derived manually (correctness=84.91%, completeness=84.39%, and quality=84.65%).
Urban Point Cloud Mining Based on Density Clustering and MapReduce
AbstractThis paper proposes an approach to classify, localize, and extract automatically urban objects such as buildings and the ground surface from a digital surface model created from aerial laser scanning data. To achieve that, the approach involves three steps: (1) dividing the original data into smaller, more manageable pieces using a method based on MapReduce gridding for subspace partitioning, (2) applying the DBSCAN algorithm to identify interesting subspaces depending on point density, and (3) grouping of identified subspaces to form potential objects. Validation of the method was conducted in an architecturally dense and complex portion of Dublin, Ireland. The best results were achieved with a 1-m3-sized clustering cube, for which the number of classified clusters most closely equaled that which was derived manually (correctness=84.91%, completeness=84.39%, and quality=84.65%).
Urban Point Cloud Mining Based on Density Clustering and MapReduce
Aljumaily, Harith (author) / Cuadra, Dolores / Laefer, Debra F
2017
Article (Journal)
English
BKL:
56.03
/
56.03
Methoden im Bauingenieurwesen
Local classification TIB:
770/3130/6500
Urban Point Cloud Mining Based on Density Clustering and MapReduce
| British Library Online Contents | 2017
Urban Point Cloud Mining Based on Density Clustering and MapReduce
| Online Contents | 2017
Distributed Big Data Clustering using MapReduce-based Fuzzy C-Medoids
| Springer Verlag | 2022
MapReduce-based Fuzzy C-means Algorithm for Distributed Document Clustering
| Springer Verlag | 2022