Sophie Crommelinck

Review of automatic feature extraction from high-resolution optical sensor data for UAV-based cadastral mapping

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.

Simulating an Autonomously Operating Low-Cost Static Terrestrial LiDAR for Multitemporal Maize Crop Height Measurements

In order to optimize agricultural processes, near real-time spatial information about in-field variations, such as crop height development (i.e., changes over time), is indispensable. This development can be captured with a LiDAR system. However, its applicability in precision agriculture is often hindered due to high costs and unstandardized processing methods. This study investigates the potential of an autonomously operating low-cost static terrestrial laser scanner (TLS) for multitemporal height monitoring of maize crops. A low-cost system is simulated by artificially reducing the point density of data captured during eight different campaigns. The data were used to derive and assess crop height models (CHM). Results show that heights calculated with CHM based on the unreduced point cloud are accurate when compared to manually measured heights (mean deviation = 0.02 m, standard deviation = 0.15 m, root mean square error (RMSE) = 0.16 m). When reducing the point cloud to 2% of its original size to simulate a low-cost system, this difference increases (mean deviation = 0.12 m, standard deviation = 0.19 m, RMSE = 0.22 m). We found that applying the simulated low-cost TLS system in precision agriculture is possible with acceptable accuracy up to an angular scan resolution of 8 mrad (i.e., point spacing of 80 mm at 10 m distance). General guidelines for the measurement set-up and an automatically executable method for CHM generation and assessment are provided and deserve consideration in further studies.

Contour Detection for UAV-Based Cadastral Mapping

Unmanned aerial vehicles (UAVs) provide a flexible and low-cost solution for the acquisition of high-resolution data. The potential of high-resolution UAV imagery to create and update cadastral maps is being increasingly investigated. Existing procedures generally involve substantial fieldwork and many manual processes. Arguably, multiple parts of UAV-based cadastral mapping workflows could be automated. Specifically, as many cadastral boundaries coincide with visible boundaries, they could be extracted automatically using image analysis methods. This study investigates the transferability of gPb contour detection, a state-of-the-art computer vision method, to remotely sensed UAV images and UAV-based cadastral mapping. Results show that the approach is transferable to UAV data and automated cadastral mapping: object contours are comprehensively detected at completeness and correctness rates of up to 80%. The detection quality is optimal when the entire scene is covered with one orthoimage, due to the global optimization of gPb contour detection. However, a balance between high completeness and correctness is hard to achieve, so a combination with area-based segmentation and further object knowledge is proposed. The localization quality exhibits the usual dependency on ground resolution. The approach has the potential to accelerate the process of general boundary delineation during the creation and updating of cadastral maps.

Object-based image analysis for cadastral mapping using satellite images

Cadasters together with land registry form a core ingredient of any land administration system. Cadastral maps comprise of the extent, ownership and value of land which are essential for recording and updating land records. Traditional methods for cadastral surveying and mapping often prove to be labor, cost and time intensive: alternative approaches are thus being researched for creating such maps. With the advent of very high resolution (VHR) imagery, satellite remote sensing offers a tremendous opportunity for (semi)-automation of cadastral boundaries detection. In this paper, we explore the potential of object-based image analysis (OBIA) approach for this purpose by applying two segmentation methods, i.e. MRS (multi-resolution segmentation) and ESP (estimation of scale parameter) to identify visible cadastral boundaries. Results show that a balance between high percentage of completeness and correctness is hard to achieve: a low error of commission often comes with a high error of omission. However, we conclude that the resulting segments/land use polygons can potentially be used as a base for further aggregation into tenure polygons using participatory mapping.