UAV photogrammetry in the post-earthquake scenario: case studies in L'Aquila

ABSTRACT The main advantage of using the Unmanned Aerial Vehicle (UAV) photogrammetry in a post-earthquake scenario is the ability to completely document the state of the structures and infrastructures, damaged by the earthquake, ensuring the safety of all operators during the data acquisition activities. The safety and accessibility aspect in the area is of crucial concern after an earthquake and sometimes many areas may be inaccessible, but, at the same time, it is necessary to collect data in order to monitor and evaluate the damage. The development of new algorithms in the field of Computer Vision drastically improved the degree of automation of the 3D point clouds generation using the photogrammetry techniques. In addition, data acquisition techniques using the UAV allow a complete 3D model with the highest possible resolution especially with respect to the conventional satellite or aerial photogrammetry to be produced. These advantages make the UAV photogrammetry highly suitable for surveys in a geo-hazard context as in a post-earthquake scenario. Some results from surveys carried out with the UAV photogrammetry after L'Aquila Earthquake occurred in 2009 will be presented and discussed.

[1]  Clive S. Fraser,et al.  Digital camera self-calibration , 1997 .

[2]  Richard Szeliski,et al.  Vision algorithms : theory and practice : International Workshop on Vision Algorithms, Corfu, Greece, September 21-22, 1999 : proceedings , 2000 .

[3]  Ronald G. Driggers,et al.  Surveillance and Reconnaissance Imaging Systems: Modeling and Performance Prediction , 2001 .

[4]  Richard Szeliski,et al.  Vision Algorithms: Theory and Practice , 2002, Lecture Notes in Computer Science.

[5]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[6]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[7]  Cordelia Schmid,et al.  A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Richard Szeliski,et al.  A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[9]  Fabio Remondino DETECTORS AND DESCRIPTORS FOR PHOTOGRAMMETRIC APPLICATIONS , 2006 .

[10]  M. Pierrot-Deseilligny,et al.  A MULTIRESOLUTION AND OPTIMIZATION-BASED IMAGE MATCHING APPROACH : AN APPLICATION TO SURFACE RECONSTRUCTION FROM SPOT 5-HRS STEREO IMAGERY , 2006 .

[11]  Richard Szeliski,et al.  Modeling the World from Internet Photo Collections , 2008, International Journal of Computer Vision.

[12]  Görres Grenzdörffer,et al.  Photogrammetric image acquisition and image analysis of oblique imagery , 2008 .

[13]  H. Hirschmüller Stereo Processing by Semiglobal Matching and Mutual Information , 2008, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

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

[16]  C. Strecha,et al.  The Accuracy of Automatic Photogrammetric Techniques on Ultra-light UAV Imagery , 2012 .

[17]  Fabio Remondino,et al.  Experiences and achievements in automated image sequence orientation for close-range photogrammetric projects , 2011, Optical Metrology.

[18]  Thomas P. Kersten,et al.  Low-Cost and Open-Source Solutions for Automated Image Orientation - A Critical Overview , 2012, EuroMed.

[19]  Diego González-Aguilera,et al.  Assessment of Stereoscopic Precision - Film to Digital Photogrammetric Cameras , 2012 .

[20]  AQUILA,et al.  MICRO UAV FOR POST SEISMIC HAZARDS SURVEYING IN OLD CITY CENTER OF L ' , 2012 .

[21]  X. Tong,et al.  Building-damage detection using pre- and post-seismic high-resolution satellite stereo imagery: A case study of the May 2008 Wenchuan earthquake , 2012 .

[22]  Thomas P. Kersten,et al.  Image-Based Low-Cost Systems for Automatic 3D Recording and Modelling of Archaeological Finds and Objects , 2012, EuroMed.

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

[24]  Valerio Baiocchi,et al.  Rapid building damage assessment using EROS B data: the case study of L'Aquila earthquake , 2012 .

[25]  M. Pierrot Deseilligny,et al.  APERO, AN OPEN SOURCE BUNDLE ADJUSMENT SOFTWARE FOR AUTOMATIC CALIBRATION AND ORIENTATION OF SET OF IMAGES , 2012 .

[26]  K. Wenzel,et al.  A comparison of dense matching algorithms for scaled surface reconstruction using stereo camera rigs , 2013 .

[27]  Ying Wu,et al.  Development of an UAS for post-earthquake disaster surveying and its application in Ms7.0 Lushan Earthquake, Sichuan, China , 2014, Comput. Geosci..

[28]  F. Nex,et al.  OBLIQUE MULTI-CAMERA SYSTEMS - ORIENTATION AND DENSE MATCHING ISSUES , 2014 .

[29]  Fabrizio Ivan Apollonio,et al.  Evaluation of feature-based methods for automated network orientation , 2014 .

[30]  Justus H. Piater,et al.  Bundle Adjustment , 2017, Encyclopedia of GIS.

[31]  Civil protection , 2018, EU–Japan Security Cooperation.