Araştırma Makalesi
Yıl 2020,
Cilt: 7 Sayı: 2, 165 - 171, 15.08.2020
Öz
Kaynakça
- Alpaydın, E. (2010). Introduction to Machine Learning, Second Edition, The MIT Press.
- Atik, M. E., Donmez, S. O., Duran, Z., İpbüker, C. (2018). Comparison Of Automatic Feature Extraction Methods For Building Roof Planes By Using Airborne Lidar Data And High Resolution Satellite Image. Proceedings Book of ICCGIS 2018, Bulgaria.
- Carrilho, A. C., Galo, M. (2018). Extraction of building roof planes with stratified random sample consensus. The Photogrammetric Record, 33(163), 363-380.
- Fischler, M. A., Bolles, R. C. (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6), 381-395.
- Huang, H., Li, Z., Gong, P., Cheng, X., Clinton, N., Cao, C., Wang, L. (2011). Automated methods for measuring DBH and tree heights with a commercial scanning lidar. Photogrammetric Engineering & Remote Sensing, 77(3), 219-227.
- Jochem, A., Höfle, B., Rutzinger, M., Pfeifer, N. (2009). Automatic roof plane detection and analysis in airborne lidar point clouds for solar potential assessment. Sensors, 9(7), 5241-5262.
- Schnabel, R., Wahl, R., Klein, R. (2007, June). Efficient RANSAC for point‐cloud shape detection. In Computer graphics forum (Vol. 26, No. 2, pp. 214-226). Oxford, UK: Blackwell Publishing Ltd.
- Tarsha-Kurdi, F., Landes, T., Grussenmeyer, P. (2008). Extended RANSAC algorithm for automatic detection of building roof planes from LiDAR data. The photogrammetric journal of Finland, 21(1), 97-109.
- Zuliani,M. (2008), RANSAC Toolbox for MATLAB. http://www.mathworks.com/matlabcentral/fileexchange/18555.
Yıl 2020,
Cilt: 7 Sayı: 2, 165 - 171, 15.08.2020
Öz
Solar energy is a renewable energy source directly from sunlight and its production depends on roof characteristics such as roof type and size. In solar potential analysis, the main purpose is to determine the suitable roofs for the placement of solar panels. Hence, roof plane detection plays a crucial role in solar energy assessment. In this study, a detailed comparison was presented between aerial photogrammetry data and LIDAR data for roof plane recognition applying RANSAC (Random Sample Consensus) algorithm. RANSAC algorithm was performed to 3D-point clouds obtained by both LIDAR (Laser Ranging and Detection) and aerial photogrammetric survey. In this regard, solar energy assessment from the results can be applied. It is shown that, the RANSAC algorithm detects building roofs better on the point cloud data acquired from airborne LIDAR regarding completeness within model, since aerial photogrammetric survey provides noisy data in spite of its high-density data. This noise in the source data leads to deformations in roof plane detection. The study area of the project is the campus of Istanbul Technical University. Accuracy information of the roof extraction of three different buildings are presented in tables.
Anahtar Kelimeler
Kaynakça
- Alpaydın, E. (2010). Introduction to Machine Learning, Second Edition, The MIT Press.
- Atik, M. E., Donmez, S. O., Duran, Z., İpbüker, C. (2018). Comparison Of Automatic Feature Extraction Methods For Building Roof Planes By Using Airborne Lidar Data And High Resolution Satellite Image. Proceedings Book of ICCGIS 2018, Bulgaria.
- Carrilho, A. C., Galo, M. (2018). Extraction of building roof planes with stratified random sample consensus. The Photogrammetric Record, 33(163), 363-380.
- Fischler, M. A., Bolles, R. C. (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6), 381-395.
- Huang, H., Li, Z., Gong, P., Cheng, X., Clinton, N., Cao, C., Wang, L. (2011). Automated methods for measuring DBH and tree heights with a commercial scanning lidar. Photogrammetric Engineering & Remote Sensing, 77(3), 219-227.
- Jochem, A., Höfle, B., Rutzinger, M., Pfeifer, N. (2009). Automatic roof plane detection and analysis in airborne lidar point clouds for solar potential assessment. Sensors, 9(7), 5241-5262.
- Schnabel, R., Wahl, R., Klein, R. (2007, June). Efficient RANSAC for point‐cloud shape detection. In Computer graphics forum (Vol. 26, No. 2, pp. 214-226). Oxford, UK: Blackwell Publishing Ltd.
- Tarsha-Kurdi, F., Landes, T., Grussenmeyer, P. (2008). Extended RANSAC algorithm for automatic detection of building roof planes from LiDAR data. The photogrammetric journal of Finland, 21(1), 97-109.
- Zuliani,M. (2008), RANSAC Toolbox for MATLAB. http://www.mathworks.com/matlabcentral/fileexchange/18555.
Toplam 9 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Mühendislik |
| Bölüm | Research Articles |
| Yazarlar | |
| Yayımlanma Tarihi | 15 Ağustos 2020 |
| Yayımlandığı Sayı | Yıl 2020 Cilt: 7 Sayı: 2 |
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