Predicting building ages from LiDAR data with random forests for building energy modeling

Energy and Buildings

Volumn 68, Issue PARTA, 2014, Pages 603-610

  Tooke, Thoreau Rory ^a   Coops, Nicholas C ^a   Webster, Jessica ^b  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^b NATURAL RESOURCES CANADA   (Canada)

Author keywords

Age; Classification; LiDAR; Machine learning; Simulation; Vancouver

Indexed keywords

AGE; BUILDING ENERGY MODEL; LIGHT DETECTION AND RANGING; MACHINE LEARNING APPROACHES; RESIDENTIAL NEIGHBORHOODS; SIMULATION; VANCOUVER; VARIABLE IMPORTANCES;

CLASSIFICATION (OF INFORMATION); COMPUTER SIMULATION; DECISION TREES; FORECASTING; LEARNING SYSTEMS; OPTICAL RADAR;

BUILDINGS;

EID: 84887650988     PISSN: 03787788     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.enbuild.2013.10.004     Document Type: Article

References (31)

1. P. Waide, J.T. Amann, and A. Hinge Energy efficiency in the North American existing building stock. IEA Information Paper 2007 International Energy Agency Paris, France
2. L. Pérez-Lombard, J. Ortiz, and C. Pout A review on buildings energy consumption information Energy and Buildings 40 3 2008 394 398
3. R. Pacheco, J. Ordóñez, and G. Martínez Energy efficient design of building: a review Renewable & Sustainable Energy Reviews 16 6 2012 3559 3573
4. C. Ratti, N. Baker, and K. Steemers Energy consumption and urban texture Energy and Buildings 37 7 2005 762 776
5. M. Rylatt, S. Gadsden, and K. Lomas GIS-based decision support for solar energy planning in urban environments Computers, Environment and Urban Systems 25 6 2001 579
6. D. Crawley, J. Hand, M. Kummert, and B. Griffith Contrasting the capabilities of building energy performance simulation programs Building and Environment 43 4 2008 661 673
7. I.J. Davenport, N. Holden, and R.J. Gurney Characterising errors in airborne laser altimetry data to extract soil roughness IEEE Transactions on Geoscience and Remote Sensing 10 2004 2130 2141
8. M.A. Lefsky, W.B. Cohen, G.G. Parker, and D.J. Harding LiDAR remote sensing for ecosystem studies BioScience 52 2002 19 30
9. N.R. Goodwin, N.C. Coops, T.R. Tooke, A. Christen, and J.A. Voogt Characterising urban surface cover and structure with airborne LiDAR technology Canadian Journal of Remote Sensing 35 2009 297 309
10. H. Neidhart, and M. Sester Identifying building types and building clusters using 3D-laser scanning and GIS-data Geo-Imagery Bridging Continents, XXth ISPRS Congress Istanbul 2004 715 720 Retrieved from http://cartesia.org/ geodoc/isprs2004/comm4/papers/442.pdf
11. M. Aydinalp, V. Ugursal, and A. Fung Modeling of the space and domestic hot-water heating energy-consumption in the residential sector using neural networks Applied Energy 79 2 2004 159 178
12. H. Farahbakhsh, V. Ugursal, and A. Fung A residential end-use energy consumption model for Canada International Journal of Energy Research 22 13 1998 1133 1143 Retrieved from http://onlinelibrary.wiley.com/doi/10.1002/(SICI)1099- 114X(19981025)22:13<1133::AID-ER434>3.0.CO;2-E/Abstract
13. L. Breiman Random forests Machine Learning 45 2001 15 32
14. A. Cutler, and J.R. Stevens Random forests for microarrays Methods in Enzymology 411 2006 422 432
15. D.R. Cutler, T.C. Edwards, K.H. Beard, A. Cutler, K.T. Hess, J. Gibson, and J.J. Lawler Random forests for classification in ecology Ecology 88 11 2007 2783 2792
16. R. Caruana, and A. Niculescu-Mizil An empirical comparison of supervised learning algorithms Proceedings of the 23rd International Conference on Machine Learning Pittsburgh, PA 2006
17. J.T. Walton Subpixel urban land cover estimation: comparing cubist, random forests, and support vector regression Photogrammetric Engineering & Remote Sensing 74 2008 1213 1222
18. L. Guo, N. Chehata, C. Mallet, and S. Boukir Relevance of airborne LiDAR and multispectral image data for urban scene classification using Random Forests ISPRS Journal of Photogrammetry and Remote Sensing 2011 56 66
19. W.Y. Yan, A. Shaker, A. Habib, and A.P. Kersting Improving classification accuracy of airborne LiDAR intensity data by geometric calibration and radiometric correction ISPRS Journal of Photogrammetry and Remote Sensing 67 2012 35 44
20. J.H. Song, S.H. Han, K. Yu, and Y.I. Kim Assessing the possibility of land-cover classification using LiDAR intensity data International Archives of Photogrammetry, Remote Sensing, and Spatial Information Sciences 34 Part 3B 2002 259 262
21. C. Beasy, C. Hopkinson, and T. Webster Classification of nearshore materials on the Bay of Fundy coast using LiDAR intensity data Proceedings of the Canadian Symposium for Remote Sensing Wolfville, June (on CD-ROM) 2005
22. J.A. Shufelt Performance evaluation and analysis of monocular building extraction from aerial imagery IEEE Transactions on Pattern Analysis and Machine Intelligence 21 1999 311 326
23. M. Herold, J. Scepan, and K.C. Clarke The use of remote sensing and landscape metrics to describe structures and changes in urban land uses Environment and Planning A 34 8 2002 1443 1458
24. Q. Zhou, and K. Liu Analysis of errors of derived slope and aspect related to DEM data properties Computers and Geosciences 30 2004 369 378
25. S. Salat Energy loads, CO2 emissions and building stocks: morphologies, typologies, energy systems and behaviour Building Research & Information 37 5 2009 598 609
26. L. Breiman Bagging predictors Machine Learning 24 1996 123 140
27. J.H. Friedman Greedy function approximation: a gradient boosting machine Annals of Statistics 29 2001 1189 1232
28. A. Liaw, and M. Wiener Classification and regression by randomForest R News 2 3 2002 18 22
29. R. Rylatt, S. Gadsden, and K. Lomas Methods of predicting urban domestic energy demand with reduced datasets: a review and a new GIS-based approach Building Service Engineering Research and Technology 24 2 2003 93 102
30. R. Levinson, H. Akbari, M. Pomerantz, and S. Gupta Solar access of residential rooftops in four California cities Solar Energy 83 12 2009 2120 2135
31. T.R. Tooke, N.C. Coops, A. Christen, O. Gurtuna, and A. Prévot Integrated irradiance modelling in the urban environment based on remotely sensed data Solar Energy 86 2012 2923 2934