Multi-scale three-dimensional detection of urban buildings using aerial LiDAR data

References

• Adolphe, L. 2001. “A Simplified Model of Urban Morphology: Application to an Analysis of the Environmental Performance of Cities.” Environment and Planning B: 28 (2): 183–200. doi:10.1068/b2631.
   Google Scholar
• Awrangjeb, M., and C. S. Fraser. 2014. “Automatic Segmentation of Raw LiDAR Data for Extraction of Building Roofs.” Remote Sensing 6 (5): 3716–3751. doi:10.3390/rs6053716.
   Web of Science ®Google Scholar
• Baltsavias, E. P. 1999. “A Comparison between Photogrammetry and Laser Scanning.” ISPRS Journal of Photogrammetry and Remote Sensing 54: 83–94. doi:10.1016/s0924-2716(99)00014-3.
   Web of Science ®Google Scholar
• Bouziani, M., K. GoTa, and D. C. He. 2010. “Automatic Change Detection of Buildings in Urban Environment from Very High Spatial Resolution Images Using Existing Geodatabase and Prior Knowledge.” ISPRS Journal of Photogrammetry and Remote Sensing 65 (1): 143–153. doi:10.1016/j.isprsjprs.2009.10.002.
   Web of Science ®Google Scholar
• Boykov, Y., and V. Kolmogorov. 2004. “An Experimental Comparison of Min-cut/maxflow Algorithms for Energy Minimization in Vision.” IEEE Transactions on Pattern Analysis and Machine Intelligence 26 (9): 1124–1137. doi:10.1109/tpami.2004.60.
   PubMed Web of Science ®Google Scholar
• Cao, S., M. Du, W. Zhao, Y. Hu, Y. Mo, S. Chen, Y. Cai, Z. Peng, and C. Zhang. 2020. “Multi-level Monitoring of Three-dimensional Building Changes for Megacities: Trajectory, Morphology, and Landscape.” ISPRS Journal of Photogrammetry and Remote Sensing 167 (2020): 54–70. doi:10.1016/j.isprsjprs.2020.06.020.
   Google Scholar
• Chen, Q., and M. Sun 2020. “Brief Description of Building Detection from Airborne LiDAR Data Based on Extraction of Planar Structure.” Accessed 1 March 2020. http://ftp.ipi.uni-hannover.de/ISPRS_WGIII_website/ISPRSIII_4_Test_results/papers/Brief_description_Qi_Chen_WHU.pdf
   Google Scholar
• Chen, Z., B. Xu, and B. Devereux. 2019. “Urban Landscape Pattern Analysis Based on 3d Landscape Models.” Applied Geography 55: 82–91. doi:10.1016/j.apgeog.2014.09.006.
   Google Scholar
• Craighead, G. 2009. Chapter 1 – High-Rise Building Definition, Development, and Use in Book High-Rise Security and Fire Life Safety. Oxford, UK: Elsevier’s Science & Technology Rights Department.
   Google Scholar
• Deng, L., Y. N. Yan, Y. He, Z. H. Mao, and J. Yu. 2019. “An Improved Building Detection Approach Using L-band Polsar Two-dimensional Time-frequency Decomposition over Oriented Built-up Areas.” GIScience & Remote Sensing 56 (1–2): 1–21. doi:10.1080/15481603.2018.1484409.
   Web of Science ®Google Scholar
• Diego González-Aguilera, Eugenia Crespo-Matellán, David Hernández-López, and Pablo Rodríguez-Gonzálvez. 2013. Automated Urban Analysis Based on LiDAR-Derived Building Models. IEEE Transactions on Geoscience and Remote Sensing, 51(3): 1844–1851.
   Google Scholar
• Dong, P., and Q. Chen. 2018. LiDAR Remote Sensing and Applications. Boca Raton: CRC Press.
   Google Scholar
• Du, S., Y. Zhang, Z. Zou, S. Xu, X. He, and S. Chen. 2017. “Automatic Building Extraction from Lidar Data Fusion of Point and Grid-based Features.” ISPRS Journal of Photogrammetry and Remote Sensing 130: 294–307. doi:10.1016/j.isprsjprs.2017.06.005.
   Web of Science ®Google Scholar
• Duarte, D., F. Nex, N. Kerle, and G. Vosselman. 2018. “Multi-Resolution Feature Fusion for Image Classification of Building Damages with Convolutional Neural Networks.” Remote Sensing 10: 1636. doi:10.3390/rs10101636.
   Web of Science ®Google Scholar
• ESRI. 2020. “Las Dataset to Raster.” Accessed 1 March 2020. https://pro.arcgis.com/zh-cn/pro-app/tool-reference/conversion/las-dataset-to-raster.htm
   Google Scholar
• Feng, L., 2020. “Detection Building Point Clouds from Airborne LiDAR Data Using Roof Points’ Attributes.” Accessed 1 March 2020. http://ftp.ipi.uni-hannover.de/ISPRS_WGIII_website/ISPRSIII_4_Test_results/papers/DetectionBuildingfromAirborneLiDARDataUsingRoofpointsAttributes.pdf
   Google Scholar
• Forman, R. T. T., and M. Godron. 1986. Landscape Ecology. New York: Wiley.
   Google Scholar
• Gerke, M., and J. Xiao. 2014. “Fusion of Airborne Laser Scanning Point Clouds and Images for Supervised and Unsupervised Scene Classification.” ISPRS Journal of Photogrammetry and Remote Sensing 87: 78–92. doi:10.1016/j.isprsjprs.2013.10.011.
   Web of Science ®Google Scholar
• Griffiths, D., and B. Jan. 2019. “Improving Public Data for Building Segmentation from Convolutional Neural Networks (Cnns) for Fused Airborne Lidar and Image Data Using Active Contours.” ISPRS Journal of Photogrammetry and Remote Sensing 154: 70–83. doi:10.1016/j.isprsjprs.2019.05.013.
   Web of Science ®Google Scholar
• Hao, X., C. Liang, L. Manchun, C. Yanming, and Z. Lishan. 2015. “Using Octrees to Detect Changes to Buildings and Trees in the Urban Environment from Airborne LiDAR Data.” Remote Sensing 7 (8): 9682–9704. doi:10.3390/rs70809682.
   Web of Science ®Google Scholar
• Haralick, R. M. 1979. “Statistical and Structural Approaches to Texture.” Proceedings of the IEEE 67 (5): 786–804. doi:10.1109/proc.1979.11328.
   Web of Science ®Google Scholar
• Huang, X., D. Wen, J. Li, and R. Qin. 2017. “Multi-level Monitoring of Subtle Urban Changes for the Megacities of China Using High-resolution Multi-view Satellite Imagery.” Remote Sensing of Environment 196: 56–75. doi:10.1016/j.rse.2017.05.001.
   Web of Science ®Google Scholar
• Hung, C., L. James, and M. Hodgson. 2018. “An Automated Algorithm for Mapping Building Impervious Areas from Airborne LiDAR Point-cloud Data for Flood Hydrology.” GIScience & Remote Sensing 55 (6): 793–816. doi:10.1080/15481603.2018.1452588.
   Web of Science ®Google Scholar
• Kanda, M., T. Kawai, M. Kanega, R. Moriwaki, K. Narita, and A. Hagishima. 2005b. “A Simple Energy Balance Model for Regular Building Arrays.” Boundary-Layer Meteorology 116 (3): 423–443. doi:10.1007/s10546-004-7956-x.
   Web of Science ®Google Scholar
• Kanda, M., T. Kawai, and K. Nakagawa. 2005a. “A Simple Theoretical Radiation Scheme for Regular Building Arrays.” Boundary-Layer Meteorology 114 (1): 71–90. doi:10.1007/s10546-004-8662-4.
   Web of Science ®Google Scholar
• Kubota, T., M. Miura, Y. Tominaga, and A. Mochida. 2008. “Wind Tunnel Tests on the Relationship between Building Density and Pedestrian-level Wind Velocity: Development of Guidelines for Realizing Acceptable Wind Environment in Residential Neighborhoods.” Building and Environment 43 (10): 1699–1708. doi:10.1016/j.buildenv.2007.10.015.
   Web of Science ®Google Scholar
• Lam, J. C. 2000. “Shading Effects Due to Nearby Buildings and Energy Implications.” Energy Conversion and Management 41 (7): 647–659. doi:10.1016/s0196-8904(99)00138-7.
   Web of Science ®Google Scholar
• Lausch, A., T. Blaschke, D. Haase, F. Herzog, R.-U. Syrbe, L. Tischendorf, et al. 2015. “Understanding and Quantifying Landscape Structure E a Review on Relevant Process Characteristics, Data Models and Landscape Metrics.” Ecological Modelling 295:31–41. doi:10.1016/j.ecolmodel.2014.08.018.
   Web of Science ®Google Scholar
• Lin, C. H., J. Y. Chen, P. L. Su, and C. H. Chen. 2014. “Eigen-feature Analysis of Weighted Covariance Matrices for LiDAR Point Cloud Classification.” ISPRS Journal of Photogrammetry and Remote Sensing 94: 70–79. doi:10.1016/j.isprsjprs.2014.04.016.
   Web of Science ®Google Scholar
• Liu, C., B. Shi, X. Yang, N. Li, and H. Wu. 2013. “Automatic Buildings Extraction from LiDAR Data in Urban Area by Neural Oscillator Network of Visual Cortex.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 6 (4): 2008–2019. doi:10.1109/jstars.2012.2234726.
   Web of Science ®Google Scholar
• Liu, M., Y. M. Hu, and C. L. Li. 2017. “Landscape Metrics for Three-dimensional Urban Building Pattern Recognition.” Applied Geography 87: 66–72. doi:10.1016/j.apgeog.2017.07.011.
   Web of Science ®Google Scholar
• McGarigal, K., and B. J. Marks, 1995. “Spatial Pattern Analysis Program for Quantifying Landscape Structure.” Gen. Tech. Rep. PNW-GTR-351. US Department of Agriculture, Forest Service, Pacific Northwest Research Station. doi: 10.2737/pnw-gtr-351.
   Google Scholar
• Meng, X., L. Wang, and N. Currit. 2009. “Morphology-based Building Detection from Airborne LiDAR Data.” Photogrammetric Engineering & Remote Sensing 75 (4): 437–442. doi:10.14358/pers.75.4.437.
   Web of Science ®Google Scholar
• Mills, G. 1997. “Building Density and Interior Building Temperatures: A Physical Modelling Experiment.” Physical Geography 18 (3): 195–214. doi:10.1080/02723646.1997.10642616.
   Web of Science ®Google Scholar
• Murakami, H., K. Nakagawa, H. Hasegawa, T. Shibata, and E. Iwanami. 1999. “Change Detection of Buildings Using an Airborne Laser Scanner.” ISPRS Journal of Photogrammetry and Remote Sensing 54 (2): 148–152. doi:10.1016/s0924-2716(99)00006-4.
   Web of Science ®Google Scholar
• Niemeyer, J., F. Rottensteiner, and U. Soergel. 2014. “Contextual Classification of LiDAR Data and Building Object Detection in Urban Areas.” ISPRS Journal of Photogrammetry and Remote Sensing 87: 152–165. doi:10.1016/j.isprsjprs.2013.11.001.
   Web of Science ®Google Scholar
• NYCDITT. 2019. “2017 ALS data.” Accessed February 1. http://gis.ny.gov/elevation/lidar-coverage.htm
   Google Scholar
• NYITS. 2019. “High-resolution Orthophotos of Red, Green, Blue, and Near-infrared Bands.” Accessed 1 February 2020. http://gis.ny.gov/gateway/mg/metadata.cfm
   Google Scholar
• Pang, S., H. Xiangyun, W. Zizheng, and L. Yihui. 2014. “Object-based Analysis of Airborne Lidar Data for Building Change Detection.” Remote Sensing 6 (11): 10733–10749. doi:10.3390/rs61110733.
   Web of Science ®Google Scholar
• Parmehr, E. G., C. S. Fraser, C. Zhang, and J. Leach. 2014. “Automatic Registration of Optical Imagery with 3D LiDAR Data Using Statistical Similarity.” ISPRS Journal of Photogrammetry and Remote Sensing 88: 28–40. doi:10.1016/j.isprsjprs.2013.11.015.
   Web of Science ®Google Scholar
• Qi, M., L. Jiang, Y. Liu, Q. Xiong, C. Sun, X. Li, W. Zhao, and X. Yang. 2018. “Analysis of the Characteristics and Sources of Carbonaceous Aerosols in Pm2.5 In the Beijing, Tianjin, and Langfang Region, China.” International Journal of Environmental Research and Public Health 15 (7): 1483. doi:10.3390/ijerph15071483.
   Web of Science ®Google Scholar
• Qin, R., J. Tian, and P. Reinartz. 2016. “3D Change Detection: Approaches and Applications.” ISPRS Journal of Photogrammetry and Remote Sensing 122: 41–56. doi:10.5220/0007260607600767.
   Web of Science ®Google Scholar
• Rapidlasso. 2020. “Lastools Introduction.” Accessed 1 March 2020. https://rapidlasso.com/lastools/lasground/
   Google Scholar
• Robinson, D. 2006. “Urban Morphology and Indicators of Radiation Availability.” Solar Energy 80 (12): 1643–1648. doi:10.1016/j.solener.2006.01.007.
   Web of Science ®Google Scholar
• Rottensteiner, F., G. Sohn, M. Gerke, J. D. Wegner, U. Breitkopf, and J. Jung. 2014. “Results of the ISPRS Benchmark on Urban Object Detection and 3D Building Reconstruction.” ISPRS Journal of Photogrammetry and Remote Sensing 93: 256–271. doi:10.1016/j.isprsjprs.2013.10.004.
   Web of Science ®Google Scholar
• Rusu, R. B., Z. C. Marton, N. Blodow, M. Dolha, and M. Beetz. 2008. “Towards 3D Point Cloud Based Object Maps for Household Environment.” Robotics and Autonomous Systems 56 (11): 927–941. doi:10.1016/j.robot.2008.08.005.
   Web of Science ®Google Scholar
• Rutzinger, M., F. Rottensteiner, and N. Pfeifer. 2009. “A Comparison of Evaluation Techniques for Building Extraction from Airborne Laser Scanning.” Ieee J-stars 2 (1): 11–20. doi:10.1109/JSTARS.2009.2012488.
   Google Scholar
• Siddiqui, F., S. Teng, G. Lu, and M. Awrangjeb 2020. “Extraction of Buildings below the Height Threshold from Remotely Sense Data.” Accessed 1 March 2020. http://ftp.ipi.uni-hannover.de/ISPRS_WGIII_website/ISPRSIII_4_Test_results/papers/MON4.pdf
   Google Scholar
• Smith, R. M. 1986. “Comparing Traditional Methods for Selecting Class Intervals on Choropleth Maps.” The Professional Geographer 38 (1): 62–67. doi:10.1111/j.0033-0124.1986.00062.x.
   Web of Science ®Google Scholar
• Streutker, D. R. 2003. “Satellite-measured Growth of the Urban Heat Island of Houston, Texas.” Remote Sensing of Environment 85 (3): 282–289. doi:10.1016/S0034-4257(03)00007-5.
   Web of Science ®Google Scholar
• Sundell-Turner, N. M., and A. D. Rodewald. 2008. “A Comparison of Landscape Metrics for Conservation Planning.” Landscape and Urban Planning 86: 219–225. doi:10.1016/j.landurbplan.2008.03.001.
   Web of Science ®Google Scholar
• Susaki, J., M. Kajimoto, and M. Kishimoto. 2014. “Urban Density Mapping of Global Megacities from Polarimetric SAR Images.” Remote Sensing of Environment 155: 334–348. doi:10.1016/j.rse.2014.09.006.
   Web of Science ®Google Scholar
• Tian, J., S. Cui, and P. Reinartz. 2014. “Building Change Detection Based on Satellite Stereo Imagery and Digital Surface Models.” IEEE Transactions on Geoscience and Remote Sensing 52 (1): 406–417. doi:10.1109/TGRS.2013.2240692.
   Web of Science ®Google Scholar
• Tian, J., P. Reinartz, P. d’Angelo, and M. Ehlers. 2013. “Region-based Automatic Building and Forest Change Detection on Cartosat-1 Stereo Imagery.” ISPRS Journal of Photogrammetry and Remote Sensing 79: 226–239. doi:10.1016/j.isprsjprs.2013.02.017.
   Web of Science ®Google Scholar
• Tomljenovic, I., B. Höfle, D. Tiede, and T. Blaschke. 2015. “Building Extraction from Airborne Laser Scanning Data: An Analysis of the State of the Art.” Remote Sensing 7 (4): 3826–3862. doi:10.3390/rs70403826.
   Web of Science ®Google Scholar
• Yang, B. 2020. “Procedure of Building Extraction.” Accessed 1 March 2020. http://ftp.ipi.uni-hannover.de/ISPRS_WGIII_website/ISPRSIII_4_Test_results/papers/BuildingExtraction_WHU_YD.pdf
   Google Scholar
• Yang, B., W. Xu, and Z. Dong. 2013. “Automated Extraction of Building Outlines from Airborne Laser Scanning Point Clouds.” IEEE Geoscience and Remote Sensing Letters 10 (6): 1399–1403. doi:10.1109/LGRS.2013.2258887.
   Web of Science ®Google Scholar
• Yang, J., M. S. Wong, M. Menenti, and J. Nichol. 2015. “Study of the Geometry Effect on Land Surface Temperature Retrieval in Urban Environment.” ISPRS Journal of Photogrammetry and Remote Sensing 109: 77–87. doi:10.1016/j.isprsjprs.2015.09.001.
   Web of Science ®Google Scholar
• Yu, B., H. Liu, J. Wu, Y. Hu, and L. Zhang. 2010. “Automated Derivation of Urban Building Density Information Using Airborne Lidar Data and Object-based Method.” Landscape Urban Plan 98 (3–4): 0–219. doi:10.1016/j.landurbplan.2010.08.004.
   Web of Science ®Google Scholar
• Zakšek, K., K. Oštir, and Ž. Kokalj. 2011. “Sky-view Factor as a Relief Visualization Technique.” Remote Sensing 3 (2): 398–415. doi:10.3390/rs3020398.
   Web of Science ®Google Scholar
• Zarea, A., and A. Mohammadzadeh. 2016. “A Novel Building and Tree Detection Method from LiDAR Data and Aerial Images.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9 (5): 1864–1875. doi:10.1109/JSTARS.2015.2470547.
   Web of Science ®Google Scholar
• Zheng, Y., and Q. Weng. 2019. “Modeling the Effect of Climate Change on Building Energy Demand in Los Angeles County by Using a GIS-based High Spatial- and Temporal-resolution Approach.” Energy 176: 641–655. doi:10.1016/j.energy.2019.04.052.
   Web of Science ®Google Scholar
• Zhou, Q. Y., and U. Neumann,2008. “Fast and Extensible Building Modeling from Airborne LiDAR Data.” In: Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM GIS 2008 (on CD-ROM), Irvine, CA, USA.
   Google Scholar