Automating image-based cadastral boundary mapping

Abstract

Unmanned aerial vehicles (UAV), also known as drones, Unmanned Aerial Systems (UAS), or Remotely Piloted Aircraft Systems (RPAS) are small aircraft 1.1.4 Boundary Delineation for UAV-based Cadastral Mapping In all case studies, cadastral boundaries are manually detected and delineated from orthoimages. This is realized either in an office with a small group of involved stakeholders for one parcel or in a community mapping approach for several parcels at once. All case studies lack an automatic approach to extract boundary features from the UAV data. An automatic or semi-automatic feature Objective (i) is addressed in chapters 1 and 3, objective (ii) in chapters 4, 5 and 6, and objective (iii) in chapters 7 and 8. The dissertation is structured as follows: Chapter 1 introduces the Ph.D. research. We describe the research gap to be addressed and formulate corresponding research objectives. Chapter 2 provides contextual information on the Ph.D. research. This is done by reviewing approaches for feature extraction from various application fields. These are synthesized into a hypothetical workflow applicable for automated boundary delineation from UAV data. The workflow consists of image segmentation, line extraction, and contour generation. Chapter 3 investigates which method performs best for image segmentation, which is the first step of the hypothetical workflow proposed in chapter 3. This is done by analyzing the transferability of gPb contour detection, a state-ofthe-art computer vision method, to UAV-based cadastral mapping. Chapter 4 investigates which method performs best for line extraction, which is the second step of the hypothetical workflow proposed in chapter 3. This is done by analyzing a superpixel approach, namely simple linear iterative * This chapter is based on: 44.

Furthering Automatic Feature Extraction for Fit-for-Purpose Cadastral Updating: Cases from Peri-Urban Addis Ababa, Ethiopia

“…The accuracy assessment of interactively delineated cadastral boundaries in Crommelinck's [49] study delivered 67% spatial correctness and 37% completeness. Additionally, compared to manual on-screen digitizing, the interactive boundary delineation approach reduced the time required to extract boundary lines by 38% and the number of clicks by about 80%.…”

“…Several scholars have practiced and proved the potential of different machine-learning algorithms to fully or semi-automatically extract boundary features from satellite or aerial images. Although the methodological approaches and the applied algorithms vary, promising results are obtained for detecting and semi-automatically extracting farmland boundaries, which could enhance the rural cadastre [5,16,[47][48][49].…”

“…The authors tested both fully automated and expert knowledge techniques using commercial software (eCognition) and the Estimate Scale Parameter (ESP2) tool to set optimal parameterization values. Crommelinck [49] has developed a procedure for boundary feature extraction that takes advantage of visible cadastral boundaries on high-resolution aerial/satellite images. First, it employs Multi-resolution Combinational Grouping (MCG), an extended version of the globalized probability of boundary (gPb) algorithm, for identifying closed boundaries between objects or segments based on the image texture, color, and brightness information.…”

“…Thus, tenure security challenges due to rapid (peri-) urbanization and the opportunities from emerging geospatial technologies for cost-and time-effective cadastral mapping are the basis for the overarching motivation and objectives of this research. As mentioned, several authors illustrated the viability of the AFE approach for cadastral mapping in different ways, such as by using open-source tools (e.g., Wassie et al [16]), employing proprietary software solutions (e.g., Nyandwi et al [5]), and developing codes for the complete process (e.g., Crommelinck [49]). However, studies have yet to assess the significance of emerging and freely available geospatial technologies for automatically extracting cadastral boundaries, as advised in [2,18,52].…”

Automatic Extraction of Unmanned Aerial Vehicles (UAV)-based Cadastral Map: Case Study in AL-Shatrah District- Iraq

“…With the help of computer vision, algorithms are developed to automatically extract the cadastral boundaries, however, extracting boundaries was first semi-automatic (the remainder of the boundaries is completed manually). Latter process was developed to become automatic [10]. From here, a new era begins with the process of map producing, especially the cadastral, which is characterized by short time, less cost and high accuracy techniques.…”