Abstract
Unmanned Aerial Vehicles (UAVs) have emerged as a rapid, low-cost and flexible acquisition system that appears feasible for application in cadastral mapping: high-resolution imagery, acquired using UAVs, enables a new approach for defining property boundaries. However, UAV-derived data are arguably not exploited to its full potential: based on UAV data, cadastral boundaries are visually detected and manually digitized. A workflow that automatically extracts boundary features from UAV data could increase the pace of current mapping procedures. This review introduces a workflow considered applicable for automated boundary delineation from UAV data. This is done by reviewing approaches for feature extraction from various application fields and synthesizing these into a hypothetical generalized cadastral workflow. The workflow consists of preprocessing, image segmentation, line extraction, contour generation and postprocessing. The review lists example methods per workflow step—including a description, trialed implementation, and a list of case studies applying individual methods. Furthermore, accuracy assessment methods are outlined. Advantages and drawbacks of each approach are discussed in terms of their applicability on UAV data. This review can serve as a basis for future work on the implementation of most suitable methods in a UAV-based cadastral mapping workflow.
Highlights
Unmanned Aerial Vehicles (UAVs) have emerged as rapid, efficient, low-cost and flexible acquisition systems for remote sensing data [1]
This review aimed to explore options to delineate boundaries for UAV-based cadastral mapping
An initial review on cadastral mapping based on high-resolution optical sensor data was done to document the recent state-of-the-art
Summary
Unmanned Aerial Vehicles (UAVs) have emerged as rapid, efficient, low-cost and flexible acquisition systems for remote sensing data [1]. A photogrammetric UAV workflow includes flight planning, image acquisition, mostly camera calibration, image orientation and data processing, which can result in Digital Surface Models (DSMs), orthoimages and point clouds [4]. UAVs are described as a capable sourcing tool for remote sensing data, since they allow flexible maneuverings, high-resolution image capture, flying under clouds, easy launch and landing and fast data acquisition at low cost. Multiple factors that influence the accuracy of derived products require extensive consideration. This includes the quality of the camera, the camera calibration, the number and location of ground control points and the choice of processing software [2,5]. UAVs have been employed in a variety of applications such as the documentation
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