Method of Defining the Parameters for UAV Point Cloud Classification Algorithm

Abstract

Image data from Drones/Unmanned Aerial Vehicles (UAVs) has been studied and used extensively for establishing maps. The process of UAV data provides three main products including (Digital Surface Model) DSM, Point cloud and Ortho-photos, in which point cloud is a valuable data source in building 3D models and topographic surfaces as well. However, processing point cloud separately to achieve secondary products has not been received much attention from researchers. This study determines parameters to develop a method for classifying point cloud data constructed from UAV images. Consequently, A 3D surface of the ground is built by applying a developed algorithm for the point cloud data for an open-pit mine. The temporal or non-ground objects such as trees, houses, vehicles are automatically subtracted from the point cloud by the algorithms. According to this line, it is possible to calculate and analyze the amount of reserves, the exploited volume to evaluate the efficiency for each mine during operation with the support of UAV integrated camera.

4D Models Generated with UAV Photogrammetry for Landfill Monitoring Thermal Control of Municipal Solid Waste (MSW) Landfills

“…The miniaturization of sensors has enabled UAVs to carry thermographic sensors [31] to capture thermal images (TIR) that determine the temperature of the imaged surface. The combination of UAV images and photogrammetry enables the representation of temperature distributions in thermal heat maps, thermal orthomosaics [32], and thermal 3D models [33] from the reconstruction of the scene based on two-dimensional images [34]. The combination of UAV thermal images and photogrammetry has been used for the identification of hot spots associated with different risk factors such as LGF emissions [1,22] and landfill fires [10,35] based on 2D orthoimages.…”