| Comparing the Performance of Roof Segmentation Methods in an Urban Environment Using Digital Elevation Data |
| Paper ID : 1209-GEOSPATIAL (R5) |
| Authors: |
|
Behaeen Farajelahi *1, Mostafa Najaf1, Hossein Arefi2 1School of Surveying and Geospatial Engineering, University of Tehran 2School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran |
| Abstract: |
| In this paper, we present a novel 3D segmentation approach using digital elevation data. Building detection has been emerging as an important area of research. It has attracted many applications, such as geomatics, architectonics, computer vision, photogrammetry, remote sensing, industry, disaster management, and city planning. Building detection techniques can basically be divided into two categories: the classical approach and the deep learning approach. The goal of this study is to outline some commonly used detection techniques in photogrammetry, like segmentation-based and classification-based methods using digital elevation data as input. The 4 different methods of roof detection with their detailed analysis and their final results are presented in this paper. This study encourages researchers to further advance research in building detection techniques. Results show that the 2D region growing can successfully segment the building components like the main facades of the complex roof and provide accurate qualitative and quantitative results compared to the other methodologies used in this study. |
| Keywords: |
| Complex Roof, Segmentation, Region Growing, Clustering, MSAC, K-means, Digital Surface Model |
| Status : Paper Accepted (Poster Presentation) |
