  Unmanned aerial vehicles (UAV) are evolving as an alternative tool to acquire
land tenure data. UAVs can capture geospatial data at high quality and
resolution in a cost-effective, transparent and flexible manner, from which
visible land parcel boundaries, i.e., cadastral boundaries are delineable. This
delineation is to no extent automated, even though physical objects
automatically retrievable through image analysis methods mark a large portion
of cadastral boundaries. This study proposes (i) a workflow that automatically
extracts candidate cadastral boundaries from UAV orthoimages and (ii) a tool
for their semi-automatic processing to delineate final cadastral boundaries.
The workflow consists of two state-of-the-art computer vision methods, namely
gPb contour detection and SLIC superpixels that are transferred to remote
sensing in this study. The tool combines the two methods, allows a
semi-automatic final delineation and is implemented as a publicly available
QGIS plugin. The approach does not yet aim to provide a comparable alternative
to manual cadastral mapping procedures. However, the methodological development
of the tool towards this goal is developed in this paper. A study with 13
volunteers investigates the design and implementation of the approach and
gathers initial qualitative as well as quantitate results. The study revealed
points for improvement, which are prioritized based on the study results and
which will be addressed in future work.
