Extraction of Visible Boundaries for Cadastral Mapping Based on UAV Imagery

Abstract

In order to transcend the challenge of accelerating the establishment of cadastres and to efficiently maintain them once established, innovative, and automated cadastral mapping techniques are needed. The focus of the research is on the use of high-resolution optical sensors on unmanned aerial vehicle (UAV) platforms. More specifically, this study investigates the potential of UAV-based cadastral mapping, where the ENVI feature extraction (FX) module has been used for data processing. The paper describes the workflow, which encompasses image pre-processing, automatic extraction of visible boundaries on the UAV imagery, and data post-processing. It shows that this approach should be applied when the UAV orthoimage is resampled to a larger ground sample distance (GSD). In addition, the findings show that it is important to filter the extracted boundary maps to improve the results. The results of the accuracy assessment showed that almost 80% of the extracted visible boundaries were correct. Based on the automatic extraction method, the proposed workflow has the potential to accelerate and facilitate the creation of cadastral maps, especially for developing countries. In developed countries, the extracted visible boundaries might be used for the revision of existing cadastral maps. However, in both cases, the extracted visible boundaries must be validated by landowners and other beneficiaries.

Bridging the Semantic Gap between Land Tenure and EO Data: Conceptual and Methodological Underpinnings for a Geospatially Informed Analysis

“…For instance, both the gPb contour detection method and the ENVI feature extraction Remote Sens. 2020, 12, 255 5 of 26 (FX) module has proven accurate results of visible object delineation that coincide with cadastral boundaries at completeness and correctness of up to 80% [11,13]. To extract visible cadastral boundaries within Object-Based Image Analysis (OBIA) environment from High Resolution Satellite Imagery (HRSI), the (semi-)automatic feature extraction methods have been employed and tested in rural areas: mean-shift segmentation with the buffer overlay method [18], and both multi-resolution segmentation (MRS) and estimation of scale parameter (ESP) (only able to automatically extract 47.4%) [17].…”

“…Cadastral morphology investigation: visual interpretation from the overlay of the cadastral map over orthophotos [15] Airborne Laser Scanning (ALS) √ Semi-automatic boundary extraction: Alpha shape (α-shapes), Canny, and Skeleton algorithm [16] Unmanned Aerial Vehicles (UAVs) √ Automatic feature extraction: Globalized Probability of Boundary (gPb) contour detections [11] High Resolution Satellite Imagery (HRSI) √ Semi-automatic boundary feature extraction: mean-shift segmentation plug-in QGIS, the buffer overlay methods [18] Unmanned Aerial Vehicles (UAVs) √ Automatic boundary extraction: ENVI feature extraction (FX) module [13] High Resolution Satellite Imagery (HRSI) √ Automatic boundary extraction: Multi-Resolution Segmentation (MRS), estimation of scale parameter (ESP) [17] Unmanned Aerial Vehicles (UAVs)…”

“…Literature shows applications ranging from environmental and regional studies to economics, and peace and conflict research, for example [6][7][8][9]. More specific to the interest of this paper, there is growing body of literature on methods to extract and map cadastral boundaries using EO data [10][11][12][13][14][15][16][17][18][19]. However, this literature rarely effectively bridges the knowledge gap between social land tenure and spatial descriptions of boundaries.…”

Remote Sensing for Land Administration

“…Eigth, Fetai et al [39] seek to further developments on automated boundary extraction, based on the use of high-resolution optical sensors mounted on UAV platforms. They investigate the potential of the ENVI feature extraction (FX) module for data processing using Slovenia as a case location.…”

Automating image-based cadastral boundary mapping

“…The comparison is conducted for a rural area in Ethiopia and two peri-urban areas in Rwanda and Kenya (Figure 7.2). No urban area is selected, as indirect surveying relies on the existence of visible boundaries, which are rare in densely populated areas [355]. Furthermore, indirect surveying in urban areas saves less logistics for field surveys, due to smaller parcel sizes.…”