Building rooftop classification using random forests for large-scale PV deployment

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 10428, Issue , 2017, Pages

  Assouline, Dan ^a   Mohajeri, Nahid ^a   Scartezzini, Jean Louis ^a  

^a EPFL   (Switzerland)

Author keywords

Geographic Information Systems; LiDAR; Random Forests; Roof classification; Roof mounted Photovoltaics

Indexed keywords

BUILDINGS; DECISION TREES; GEOGRAPHIC INFORMATION SYSTEMS; LEARNING ALGORITHMS; LEARNING SYSTEMS; OPTICAL RADAR; REMOTE SENSING; RENEWABLE ENERGY RESOURCES; ROOFS; SOLAR POWER GENERATION;

ENSEMBLE LEARNING ALGORITHM; GEOMETRIC CHARACTERISTICS; PHOTOVOLTAICS; POTENTIAL ANALYSIS; RANDOM FORESTS; RENEWABLE ENERGIES; SOLAR PHOTOVOLTAICS; TYPE CLASSIFICATIONS;

CLASSIFICATION (OF INFORMATION);

EID: 85040177461     PISSN: 0277786X     EISSN: 1996756X     Source Type: Conference Proceeding    
DOI: 10.1117/12.2277692     Document Type: Conference Paper

References (17)

1. Gutschner, M., Nowak, S., and Toggweiler, P., "Potential for building integrated photovoltaics," IEA-PVPS Task 7 (2002).
2. Wiginton, L., Nguyen, H., and Pearce, J. M., "Quantifying rooftop solar photovoltaic potential for regional renewable energy policy," Computers Environment and Urban Systems 34(4), 345-357 (2010).
3. Melius, J., Margolis, R., and Ong, S., "Estimating rooftop suitability for pv: a review of methods, patents, and validation techniques," National Renewable Energy Laboratory (NREL), Golden, CO (2013).
4. Alexander, C., Smith-Voysey, S., Jarvis, C., and Tansey, K., "Integrating building footprints and lidar elevation data to classify roof structures and visualise buildings," Computers Environment and Urban Systems 33(4), 285-292 (2009).
5. Boz, M. B., Calvert, K., and Brownson, J. R., "An automated model for rooftop pv systems assessment in arcgis using lidar," AIMS Energy 3(3), 401-420 (2015).
6. Gooding, J., Crook, R., and Tomlin, A. S., "Modelling of roof geometries from low-resolution lidar data for city-scale solar energy applications using a neighbouring buildings method," Applied Energy 148, 93-104 (2015).
7. Zang, A., Zhang, X., Chen, X., and Agam, G., "Learning-based roof style classification in 2d satellite images," in [SPIE Defense+ Security], 94730K-94730K, International Society for Optics and Photonics (2015).
8. Wang, O., Lodha, S. K., and Helmbold, D. P., "A bayesian approach to building footprint extraction from aerial lidar data," in [Third International Symposium on 3D Data Processing Visualization and Transmission], 192-199, IEEE (2006).
9. Rottensteiner, F., Trinder, J., Clode, S., and Kubik, K., "Building detection using lidar data and multi-spectral images," in [Digital Image Computing: Techniques and Applications], 2, 673-682, CSIRO (2003).
10. Zhang, X., Zang, A., Agam, G., and Chen, X., "Learning from synthetic models for roof style classic cation in point clouds," in [Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems], 263-270, ACM (2014).
11. Izquierdo, S., Rodrigues, M., and Fueyo, N., "A method for estimating the geographical distribution of the available roof surface area for large-scale photovoltaic energy-potential evaluations," Solar Energy 82(10), 929-939 (2008).
12. Kohavi, R. et al., "A study of cross-validation and bootstrap for accuracy estimation and model selection," in [Ijcai], 14(2), 1137-1145 (1995).
13. Breiman, L., "Random forests," Machine learning 45(1), 5-32 (2001).
14. Breiman, L., Friedman, J., Stone, C., and Olshen, R., [Classification and Regression Trees], The Wadsworth and Brooks-Cole statistics-probability series, Taylor & Francis (1984).
15. Breiman, L., "Bagging predictors," Machine learning 24(2), 123-140 (1996).
16. Freund, Y. and Schapire, R. E., "A desicion-theoretic generalization of on-line learning and an application to boosting," in [European conference on computational learning theory], 23-37, Springer (1995).
17. Liaw, A., Wiener, M., et al., "Classification and regression by randomforest," R news 2(3), 18-22 (2002).