Sequential Earthquake Damage Assessment Incorporating Optimized sUAV Remote Sensing at Pescara del Tronto

A sequence of large earthquakes in central Italy ranging in moment magnitudes (Mw) from 4.2 to 6.5 caused significant damage to many small towns in the area. After each earthquake in 2016 (24 August and 26 October), automated small unmanned aerial vehicles (sUAV) acquired valuable imagery data for post-hazard reconnaissance in the mountain village of Pescara del Tronto, and were applied to 3D reconstruction using Structure-from-Motion (SfM). In July 2018, the site was again monitored to obtain additional imagery data capturing changes since the last visit following the 30 October 2016 Earthquake. A genetic-based mission-planning algorithm that delivers optimal viewpoints and path planning was field tested and reduced the required photos for 3D reconstruction by 9.1%. The optimized 3D model provides a better understanding of the current conditions of the village, when compared to the nadir models, by containing fewer holes on angled surfaces, including an additional 17% surface area, and with a comparable ground-sampling distance (GSD) of ≈2.4 cm/px (≈1.5 cm/px when adjusted for camera pixel density). The resulting three time-lapse models provide valuable metrics for ground motion, progression of damage, resilience of the village, and the recovery progress over a span of two years.

[1]  J J Koenderink,et al.  Affine structure from motion. , 1991, Journal of the Optical Society of America. A, Optics and image science.

[2]  John D. Hedengren,et al.  Evolutionary View Planning for Optimized UAV Terrain Modeling in a Simulated Environment , 2015, Remote. Sens..

[3]  Michael Affenzeller,et al.  Local Optima Networks in Solving Algorithm Selection Problem for TSP , 2018, DepCoS-RELCOMEX.

[4]  Jonathan P. Stewart,et al.  Reconnaissance of 2016 Central Italy Earthquake Sequence , 2018, Earthquake Spectra.

[5]  C. Marzo,et al.  UAV and Airborne LiDAR Data for Interpreting Kinematic Evolution of Landslide Movements: The Case Study of the Montescaglioso Landslide (Southern Italy) , 2019, Geosciences.

[6]  Filiberto Chiabrando,et al.  Engineering Reconnaissance Following the October 2016 Central Italy Earthquakes - Version 2 , 2017 .

[7]  Filiberto Chiabrando,et al.  Phased Reconnaissance Approach to Documenting Landslides following the 2016 Central Italy Earthquakes , 2018, Earthquake Spectra.

[8]  Leonidas Deligiannidis,et al.  Emerging Trends in Image Processing, Computer Vision and Pattern Recognition , 2014 .

[9]  M. J. Henriques,et al.  Unmanned aerial vehicles (UAV) as a support to visual inspections of concrete dams , 2015 .

[10]  Bakhtiar Feizizadeh,et al.  From a GIS-based hybrid site condition map to an earthquake damage assessment in Iran: Methods and trends , 2017 .

[11]  Benjamin J. Barrett,et al.  Field Validation of an Advanced Autonomous Method of Exterior Dam Inspection Using Unmanned Aerial Vehicles , 2018 .

[12]  Robert M. Dollison.  The National Map: New Viewer, Services, and Data Download , 2010 .

[13]  Jian Zhao,et al.  Optimal visual sensor planning , 2009, 2009 IEEE International Symposium on Circuits and Systems.

[14]  Andrew K. Whitten,et al.  Decentralized Planning for Autonomous Agents Cooperating in Complex Missions , 2010 .

[15]  Saul I. Gass,et al.  Encyclopedia of Operations Research and Management Science , 1997 .

[16]  Errin W. Fulp,et al.  Evolutionary Approaches for Resilient Surveillance Management , 2017, 2017 IEEE Security and Privacy Workshops (SPW).

[17]  Horst Bischof,et al.  Photogrammetric Camera Network Design for Micro Aerial Vehicles , 2012 .

[18]  Claudio De Luca,et al.  Ground Deformation and Source Geometry of the 30 October 2016 Mw 6.5 Norcia Earthquake (Central Italy) Investigated Through Seismological Data, DInSAR Measurements, and Numerical Modelling , 2018, Remote. Sens..

[19]  Sridha Sridharan,et al.  Recent Advances in Camera Planning for Large Area Surveillance , 2016, ACM Comput. Surv..

[20]  P. Barone,et al.  Forensic Investigations of Geohazards: The Norcia 2016 Earthquake , 2018, Geosciences.

[21]  Errin W. Fulp,et al.  Using Evolutionary Approaches to Manage Surveillance Cameras in Dynamic Environments , 2018, 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE).

[22]  John D. Hedengren,et al.  Application and Accuracy of Structure from Motion Computer Vision Models with Full-Scale Geotechnical Field Tests , 2015 .

[23]  Rakesh Nagi,et al.  An incremental graph-partitioning algorithm for entity resolution , 2019, Inf. Fusion.

[24]  Caijun Xu,et al.  Focal Mechanisms of the 2016 Central Italy Earthquake Sequence Inferred from High-Rate GPS and Broadband Seismic Waveforms , 2018, Remote. Sens..

[25]  Joseph K. Kearney,et al.  Optimal Camera Placement for Motion Capture Systems , 2017, IEEE Transactions on Visualization and Computer Graphics.

[26]  Engineering Reconnaissance following the October 2016 Central Italy Earthquakes , 2017 .

[27]  Eric Hsueh-Chan Lu,et al.  A hybrid route planning approach for logistics with pickup and delivery , 2019, Expert Syst. Appl..

[28]  E. Ausilio,et al.  Reconnaissance of geotechnical aspects of the 2016 Central Italy earthquakes , 2019, Bulletin of Earthquake Engineering.

[29]  L. Barazzetti,et al.  TECHNICAL ASPECTS RELATED TO THE APPLICATION OF SFM PHOTOGRAMMETRY IN HIGH MOUNTAIN , 2018 .

[30]  Oleg A. Yakimenko,et al.  New Generation of Rapid Flight Test Prototyping System for small Unmanned Air Vehicles , 2007 .

[31]  Xin-She Yang,et al.  Introduction to Algorithms , 2021, Nature-Inspired Optimization Algorithms.

[32]  Yi Dong,et al.  Sensor selection for fault diagnosis in uncertain systems , 2018, Int. J. Control.

[33]  Jaroslav Šedina,et al.  High Resolution Drone Surveying of the Pista Geoglyph in Palpa, Peru , 2018, Geosciences.

[34]  John D. Hedengren,et al.  Potential Benefits of Combining Anomaly Detection with View Planning for UAV Infrastructure Modeling , 2017, Remote. Sens..

[35]  Masashi Matsuoka,et al.  Building Damage Assessment Using Multisensor Dual-Polarized Synthetic Aperture Radar Data for the 2016 M 6.2 Amatrice Earthquake, Italy , 2017, Remote. Sens..