# Remote Sensing for Land Administration
## 2 Overview of Contributions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & **Title** & **County** & **Applications** & **Techniques** & **Data** \\ \hline [PERSON] et al. & **Extraction of Visible Boundaries** & & Land tenure and & Exelis Visual & \\  & **for Cadastral Mapping Based on** & & cadastral & Information Solutions & UAV RGB; GNSS; \\  & **UAV Imagery** & & updating and & (ENVI) feature & cadastral map \\ \hline Claudia & **Unmanned Aerial System** & & Land Use; Land & & \\  & **Imagery, Land Data and User** & & Tenure; Land & & \\  & **Needs: A Socio-Technical** & & Development & & \\  & **Assessment in Rwanda** & & Cadastral & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cont. The results show that the new approach is more effective in terms of minimizing clicks and time requirements, compared to manual delineation of visible parcels boundaries. The most significant advantages are observed for rural areas, where the delineation effort per parcel requires 38% less time and 80% fewer clicks, compared to manual delineation. Fifth, [PERSON] et al. [36] investigate the emerging areas of 3D cadastres and indoor boundary recording. They demonstrate that most 3D cadastre research has focused on interrelations at the level of buildings and infrastructures: analysis of interrelations in terms of indoor spaces has yet to be adequately explored. The promising research illustrates the opportunity to use 3D point clouds to establish 3D cadastral boundary data in indoor environments. The internal geometry changes of a building, temporally speaking, can be automatically detected from point clouds. The geometry changes can be linked with a data model such as LADM and included in a 3D spatial database, to support updating a 3D cadastre. The permanent changes (e.g., to walls and rooms) are automatically distinguished from dynamic changes (e.g., human, furniture) and are able to be linked to the space subdivisions. Sixth, [PERSON] et al. [37] head below the surface to tackle the issue of underground land tenure mapping and land use recordation. Increasing urban density and activity is resulting in public infrastructures moving underground. However, precise and detailed spatial information of underground infrastructure, the ownership of those underground objects, and knowledge on the interdependence of infrastructures below and above the ground is still often missing. The research explores how to create reliable 3D underground utility network maps for use in land administration. They investigate current issues pertaining to existing underground utility databases, using Singapore as a case study. A framework for underground utility data governance is proposed to manage the work process from data capture to data usage. An initial design of the 3D underground utility data model is introduced. It describes 3D geometric and spatial information about underground utilities data, and connects it to the cadastral parcel for land administration. Additionally, data from mobile Ground Penetrating Radar is integrated with the existing utility data in a 3D model in support of land administration of underground utilities. Seventh, [PERSON] et al. [38] explore the potential of deep FCNs for the novel application of cadastral boundary detection in urban and semi-urban areas, based on UAV images. They test the performance of FCNs against other state-of-the-art machine learning techniques, including MRS and gPb, in two case study sites in Rwanda. Experimental results show that FCNs outperformed MRS and gPb in both study areas and achieved an average accuracy of 0.79 in precision, 0.37 in recall and 0.50 in F-score. Therefore, they are able to effectively extract cadastral boundaries, especially when a large proportion of cadastral boundaries are visible. This automated method could minimize manual digitization and reduce fieldwork, thus facilitating the current cadastral mapping and updating practices. Eigth, [PERSON] et al.