Image-Based 3D Reconstruction for Posthurricane Residential Building Damage Assessment

AbstractStreet-level storm damage photos are an essential type of data used in postdisaster damage assessment. However, the existing approaches only leverage such data in a two-dimensional context. The research reported in this paper leveraged the photos collected during Hurricane Sandy to explore image-based three-dimensional (3D) reconstruction for posthurricane residential building damage assessment. Specifically, two commonly used image reconstruction pipelines are employed to reconstruct several impacted residential buildings to evaluate their performances regarding key measurement needs in posthurricane damage assessment. Damage data recorded by a mobile light detection and ranging (LIDAR) system were used as the ground truth for performance evaluation. The study results suggest that image-based 3D reconstruction can adequately support hurricane damage assessment needs for residential buildings. However, for damage assessment tasks which rely on very accurate estimate (generally <1  cm) of displacem...

[1]  Jean Ponce,et al.  Accurate, Dense, and Robust Multiview Stereopsis , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Ian Robertson,et al.  Tohoku Tsunami-Induced Building Failure Analysis with Implications for U.S. Tsunami and Seismic Design Codes , 2013 .

[3]  Mark A. Fonstad,et al.  Topographic structure from motion: a new development in photogrammetric measurement , 2013 .

[4]  Andrew J. Graettinger,et al.  Automated Extraction of Building Geometry from Mobile Laser Scanning Data Collected in Residential Environments , 2014 .

[5]  Gregory L. F. Chiu,et al.  Assessment and quantification of hurricane induced damage to houses , 1999 .

[6]  Yin Lu Young,et al.  Damage Assessment of the 2010 Chile Earthquake and Tsunami Using Terrestrial Laser Scanning , 2012 .

[7]  Falko Kuester,et al.  Terrestrial Laser Scanning-Based Structural Damage Assessment , 2010, J. Comput. Civ. Eng..

[8]  Silvio Savarese,et al.  Monitoring changes of 3D building elements from unordered photo collections , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[9]  Sami F. Masri,et al.  Adaptive vision-based crack detection using 3D scene reconstruction for condition assessment of structures , 2012 .

[10]  R. Eustice,et al.  Large area 3D reconstructions from underwater surveys , 2004, Oceans '04 MTS/IEEE Techno-Ocean '04 (IEEE Cat. No.04CH37600).

[11]  J. Beven,et al.  Tropical Cyclone Report Hurricane Sandy , 2013 .

[12]  Carol J. Friedland,et al.  Residential building damage from hurricane storm surge: proposed methodologies to describe, assess and model building damage , 2009 .

[13]  Mohsen Azadbakht,et al.  Tsunami Modeling, Fluid Load Simulation, and Validation Using Geospatial Field Data , 2014 .

[14]  Sami F. Masri,et al.  A new methodology for non-contact accurate crack width measurement through photogrammetry for automated structural safety evaluation , 2013 .

[15]  J. Gong,et al.  3D Building Model Reconstruction from Multi-view Aerial Imagery and Lidar Data , 2011 .

[16]  Jin Zhang,et al.  A new algorithm of building boundary extraction based on LIDAR data , 2011, 2011 19th International Conference on Geoinformatics.

[17]  Andrew J. Graettinger,et al.  Automated Tornado Damage Assessment and Wind Speed Estimation Based on Terrestrial Laser Scanning , 2015, J. Comput. Civ. Eng..

[18]  A. Dégre,et al.  The evaluation of unmanned aerial system-based photogrammetry and terrestrial laser scanning to generate DEMs of agricultural watersheds , 2014 .

[19]  Andrew J. Graettinger,et al.  GIS for the Geo-Referenced Analysis and Rapid Dissemination of Forensic Evidence Collected in the Aftermath of the Tuscaloosa Tornado , 2012 .

[20]  Erle C. Ellis,et al.  Remote Sensing of Vegetation Structure Using Computer Vision , 2010, Remote. Sens..

[21]  Richard Szeliski,et al.  Recovering 3D shape and motion from image streams using nonlinear least squares , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Minas E. Spetsakis,et al.  A multi-frame approach to visual motion perception , 1991, International Journal of Computer Vision.

[23]  Mani Golparvar-Fard,et al.  Automated Progress Monitoring Using Unordered Daily Construction Photographs and IFC-Based Building Information Models , 2015, J. Comput. Civ. Eng..

[24]  Veronica Teichrieb,et al.  Challenges in 3D Reconstruction from Images for Difficult Large-Scale Objects: A Study on the Modeling of Electrical Substations , 2012, 2012 14th Symposium on Virtual and Augmented Reality.

[25]  Ioannis K. Brilakis,et al.  A videogrammetric as-built data collection method for digital fabrication of sheet metal roof panels , 2013, Adv. Eng. Informatics.

[26]  Norman Kerle,et al.  Satellite remote sensing as a tool in Lahar disaster management. , 2002, Disasters.

[27]  Norman Kerle,et al.  Post - earthquake damage assessment using satellite and airborne data in the case of the 1999 Kocaeli earthquake, Turkey , 2004 .

[28]  Takeo Kanade,et al.  Shape and motion from image streams under orthography: a factorization method , 1992, International Journal of Computer Vision.

[29]  Yi Lin,et al.  Mini-UAV-Borne LIDAR for Fine-Scale Mapping , 2011, IEEE Geoscience and Remote Sensing Letters.

[30]  Uwe Weidner,et al.  Casualty scenarios based on laserscanning data , 2005 .

[31]  Ning Lin,et al.  Post-Sandy damage and vulnerability assessment with 3D geospatial sensing methods , 2014 .

[32]  Andrew W. Fitzgibbon,et al.  Bundle Adjustment - A Modern Synthesis , 1999, Workshop on Vision Algorithms.

[33]  Andrew Curtis,et al.  Spatial video data collection in a post-disaster landscape: The Tuscaloosa Tornado of April 27th 2011 , 2012 .

[34]  B. Koch,et al.  UAV-BASED PHOTOGRAMMETRIC POINT CLOUDS – TREE STEM MAPPING IN OPEN STANDS IN COMPARISON TO TERRESTRIAL LASER SCANNER POINT CLOUDS , 2013 .

[35]  Carol J. Friedland,et al.  Remote Sensing and Field Reconnaissance for Rapid Damage Detection in Hurricane Katrina , 2008 .

[36]  Jason Levy,et al.  Advances in Remote Sensing for Oil Spill Disaster Management: State-of-the-Art Sensors Technology for Oil Spill Surveillance , 2008, Sensors.

[37]  B. Hamann,et al.  Interactive terrain visualization enables virtual field work during rapid scientific response to the 2010 Haiti earthquake , 2012 .

[38]  V. Klemas.  The Role of Remote Sensing in Predicting and Determining Coastal Storm Impacts , 2009 .

[39]  Mani Golparvar-Fard,et al.  Image-Based Automated 3D Crack Detection for Post-disaster Building Assessment , 2014, J. Comput. Civ. Eng..

[40]  Jay H. Crandell,et al.  Scientific Damage Assessment Methodology and Practical Applications , 2005 .

[41]  Bruno Vallet,et al.  Detection and 3D reconstruction of traffic signs from multiple view color images , 2013 .

[42]  M. Westoby,et al.  ‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications , 2012 .

[43]  Jie Gong.  Mobile LiDAR Data Collection and Analysis for Post-Sandy Disaster Recovery , 2013 .

[44]  Changchang Wu,et al.  Towards Linear-Time Incremental Structure from Motion , 2013, 2013 International Conference on 3D Vision.

[45]  Ioannis K. Brilakis,et al.  Automated sparse 3D point cloud generation of infrastructure using its distinctive visual features , 2011, Adv. Eng. Informatics.

[46]  Liang Cheng,et al.  Post-earthquake assessment of building damage degree using LiDAR data and imagery , 2008 .

[47]  K. Emanuel,et al.  Physically based assessment of hurricane surge threat under climate change , 2012 .

[48]  Robert E. Kayen,et al.  Post-earthquake and tsunami 3D laser scanning forensic investigations , 2012 .

[49]  W. Paul Menzel,et al.  INTRODUCING THE NEXT-GENERATION ADVANCED BASELINE IMAGER ON GOES-R , 2005 .

[50]  Fernanda Leite,et al.  Evaluation of accuracy of as-built 3D modeling from photos taken by handheld digital cameras , 2012 .

[51]  Adam J. Mathews,et al.  Visualizing and Quantifying Vineyard Canopy LAI Using an Unmanned Aerial Vehicle (UAV) Collected High Density Structure from Motion Point Cloud , 2013, Remote. Sens..