Research

Automated feature extraction for UAV-based cadastral mapping

Student:Sophie Charlotte Crommelinck
Timeline:February 2016 - 31 January 2020

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 is arguably not exploited to its full potential: based on UAV data, cadastral boundaries are most prominently visually detected and manually digitized. A workflow that automatically extracts boundary features from UAV data could increase the pace of current mapping procedures.

This Ph.D. research investigates automated feature extraction for UAV-based cadastral boundary delineation. This comprises (i) feature extraction methods applied to UAV imagery and (ii) the alignment and integration of further spatial data sources, such as existing maps and sketchmaps. The latter are intended to improve boundary delineation in terms of automation and accuracy. In the end, tailored automated feature extraction for boundary delineation should be conceptualized, implemented and validated. The Ph.D. research is related to the European Union’s (EU) its4land project and will be based on data captured in sub Saharan African countries.

The following figure shows the research framework that has been conceptualized around the central aim of automated feature extraction for cadastral boundary delineation. The framework includes four research objectives of which the first one is based on a literature review and the remaining three consist of the technical design and realization of the boundary delineation tool taking into account different spatial data sources.

Figure 1. Contextualization of the four Ph.D. research objectives that aim at a tailored automated feature extraction for cadastral boundary delineation.

Meet the team

S.C. Crommelinck (Sophie Charlotte)
Graduate Student
prof.dr.ir. M.G. Vosselman (George)
Promotor
dr. Y. Yang (Michael)
Co-promotor
dr. M.N. Koeva (Mila)
Co-promotor