MULTITEMPORAL 3D MAPPING OF POST-EARTHQUAKE RECOVERY PHASE WITH UAS: CASE STUDY VRISA, LESVOS, GREECE

Abstract

The recovery phase of an earthquake-affected settlement is a time-consuming and complex process that requires monitoring, which is now possible using UAS. The purpose of this paper is to present the methodology followed and the results obtained by the exploitation of UAS for rapid multitemporal 3D mapping during the recovery phase of Vrisa traditional settlement, Lesvos island, Greece, which was highly damaged by the earthquake (Mw=6.3) on 12th June 2017. More analytically, three (3) flight campaigns covering the period July 2017 – May 2020 took place by means of an UAS for collecting high-resolution images on: i) 19th May 2019, ii) 29th September 2019, iii) 17th May 2020. Structure from Motion (SfM) and Multi Stereo View (MSV) methods have been applied and produced: i) Digital Surface Models – DSMs, ii) 3D Point Clouds – 3DPC and iii) Orthophoto-maps, of Vrisa. In parallel, GIS capabilities has been exploit to calculate building volumes based on: a) DSM produced by UAS image processing, b) DEM produced by 233 RTK measurements and c) building footprints derived by the digitization of the orthophoto-map of 25th July 2017. The methodology developed and implemented achieves extremely reliable results in a relatively easy, fast and economically feasible way, which is confirmed with great precision by field work. By applying the above-described methodology, it was possible to monitoring the recovery phase during July 2017 and May 2020 which 302/340 buildings that had been severely damaged by the earthquake have been demolished. A small number of new buildings have also been rebuilded and small number of buildings that have just begun excavations for their construction. An important parameter for obtaining reliable data and comparable results is the correct selection of flight parameters and their maintenance at all times when it is decided to take data, without affecting the accuracy of the results from taking photos or videos. Automation in the future of the proposed methodology can significantly accelerate the achievement of reliable results without the intermediate interpretation of orthophoto-maps.