UAVs-unmanned aerial vehicles-facilitate data acquisition at temporal and spatial scales that still remain unachievable for traditional remote sensing platforms. However, current legal frameworks that regulate UAVs present significant barriers to research and development. To highlight the importance, impact, and diversity of UAV regulations, this paper provides an exploratory investigation of UAV regulations on the global scale. For this, the methodological approach consists of a research synthesis of UAV regulations, including a thorough literature review and a comparative analysis of national regulatory frameworks. Similarities and contrasting elements in the various national UAV regulations are explored including their statuses from the perspectives of past, present, and future trends. Since the early 2000s, countries have gradually established national legal frameworks. Although all UAV regulations have one common goal-minimizing the risks to other airspace users and to both people and property on the ground-the results reveal distinct variations in all the compared variables. Furthermore, besides the clear presence of legal frameworks, market forces such as industry design standards and reliable information about UAVs as public goods are expected to shape future developments.

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ABSTRACT:For more than two decades, many efforts have been made to develop methods for extracting urban objects from data acquired by airborne sensors. In order to make the results of such algorithms more comparable, benchmarking data sets are of paramount importance. Such a data set, consisting of airborne image and laserscanner data, has been made available to the scientific community. Researchers were encouraged to submit results of urban object detection and 3D building reconstruction, which were evaluated based on reference data. This paper presents the outcomes of the evaluation for building detection, tree detection, and 3D building reconstruction. The results achieved by different methods are compared and analysed to identify promising strategies for automatic urban object extraction from current airborne sensor data, but also common problems of state-of-the-art methods.

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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.