**1. Introduction**

Recording land rights provides land owners tenure security, a sustainable livelihood and increases financial opportunities. Estimates suggest that about 75% of the world population does not have access to a formal system to register and safeguard their land rights [1]. This lack of recorded land rights increases insecure land tenure and fosters existence-threatening conflicts, particularly in developing countries. Recording land rights spatially, i.e., cadastral mapping, is considered the most expensive part of a land administration system [2]. Recent developments in technology allow us to rethink contemporary cadastral mapping. The aim of this study is to make use of technological developments to create automated and thus more efficient approaches for cadastral mapping.

Automated cadastral boundary delineation based on remote sensing data has been rarely investigated, even though physical objects, which can be extracted using image analysis, often define visible cadastral boundaries [3].

Visible cadastral boundaries demarcated by physical objects such as hedges, fences, walls, bushes, roads, or rivers, can make up a large portion of all cadastral boundaries [4,5]. Indirect surveying relies on the delineation of such visible parcel boundaries from remote sensing imagery.

Automatically extracting visible cadastral boundaries combined with (legal) adjudication and an incorporation of local knowledge from human operators offers the potential to improve current cadastral mapping approaches in terms of time, cost, accuracy and acceptance [6]. High-resolution remote sensing imagery, increasingly captured with Unmanned Aerial Vehicles (UAVs) in land administration [7–11], provides the basis for such a semi-automated delineation of visible boundaries.
