DSM generation and quality assessment using low-cost UAV and FOSS

The use of Unmanned Aerial Vehicles (UAV) is becoming a standard approach in photogrammetry, supported by a growing number of users and organizations. This is due to the relatively affordable costs for these technologies as well as to their readiness in obtaining high quality products such as detailed 3D models and orthophotos. Nevertheless, the comprehensive price of both photogrammetric UAVs as well as proprietary software -currently adopted in the practice- often exceeds ten thousand Euros, which may still represent an obstacle to the spreading of this technology among SMEs as well as developing countries. With this in mind, we present here a framework aimed to the generation of high resolution Digital Surface Models (DSMs) by coupling low-cost UAV with Free and Open Source Software (FOSS). This framework takes advantage of the increasing availability on the market of low-cost UAVs -which anyway incorporate many kinds of reliable sensors- as well as of the recent development of powerful FOSS tools dedicated to photogrammetry and image processing. For this study, a test area of 150 square meters -characterized by flat terrain with concrete curbs- was selected. Aerial images of the area were captured by using the 14 Mpx on board digital camera of a DJI Phantom 2 Vision+ quadcopter. Both flight plan as well as quadcopter navigation were performed by exploiting its GNSS embedded sensor through the dedicated application for mobile devices. Image pre-processing was performed using GIMP (https://www.gimp.org) while dense point-clouds were extracted from these images and georeferenced using OpenDroneMap (https://github.com/OpenDroneMap). CloudCompare (http://www.danielgm.net/cc) was then used for point-cloud post-processing and raster DSMs generation. In order to asses the quality of the UAV derived DSM, a reference DSM of the same area was created starting from 1400 points, collected by GPS survey - Real Time Kinematic (RTK) method. A statistical comparison of the two resulting raster DSM was then performed by using GRASS GIS (https://grass.osgeo.org). The presented approach allows to obtain accurate photogrammetric products while being economically advantageous. Moreover, thanks to the continuous quality increase of both low-cost UAV hardware and FOSS tools it is candidate to further improvements in the next future.