# Remote Sensing for Land Administration
## 2 Overview of Contributions  
The works making up this Special Issue are presented in reverse chronological order of acceptance. This was considered the most straight forward approach to administer and was also deemed to be the most unbiased. Further justification lies in the fact that whilst there are demonstratable clusters of like-contributions in terms of geographic focus, applications, techniques, and data used, the number of contributions (9) is not considered onerous to comprehend, and each individual work brings its own unique contribution. First, inspired to deliver an alternative to ground-based surveying for the collection of non-boundary cadastral information, [PERSON] and [PERSON] [32] present a study aimed at remote identification of the discrepancy between existing cadastral maps (which include use information), and current on-ground land uses. The proposed method involves updating the existing land cover attributes of a land parcel maps using UAV hyperspectral imagery classified using CNN, and then composing a discrepancy map showing land use differences. The experimental results out of South Korea demonstrate performance relies heavily on the classification. An advantage of the approach is suggested to be its flexibility regarding modification of the matching criteria between land use categories and land coverage. The authors argue the method could be applied in other contexts and could significantly reduce the time and effort for land use monitoring and field surveying. Second, [PERSON] et al. [33] introduce a suite remote sensing approaches, developed, adapted, applied, and tested for the case of land tenure recording in Kenya. These include a unique ontological analysis approach using smart sketch maps (SmartSkeMa); UAV application; and automatic boundary extraction techniques, based based on the acquired UAV images. To ensure applicability of the proposed methodologies, local community needs and the broader governance implications are examined. For the case location of Kajiado, the results show that SmartSkeMa requires little expertise for immediate use, UAVs have high potential for creating up-to-date base maps, and automatic boundary extraction appears an effective method for demarcation of visible tenure boundaries, even compared to traditional methodologies and manual delineation. Third, [PERSON] and [PERSON] [34] seek to bridge the semantic gap between land tenure concepts and remotely sensed or earth observation (EO) data. Specifically, they investigate the circumstances where it is possible to rely on EO data to inform land administration applications, such as cadastral mapping and land use change monitoring. Based on reviews of available EO data sources and techniques, they hypothesize that it is possible to both qualify and quantify specific types of land tenure. Furthermore, [PERSON] and [PERSON] aim to standardize the identification and categorization of certain objects, environments, and semantics visible in EO data that can support (re-)interpretation of land tenure relations. Using North Korean as a case location, they illustrate that land tenure information, in conjunction with EO data and image interpretation, support land administration practice. Fourth, in a continuation of earlier work, [PERSON] et al. [35] present results from a semi-automatic boundary delineation workflow, comprising image segmentation, boundary classification and an interactive delineation based on UAV data. The application of CNN for boundary line classification is shown to eliminate the previous need for Random Forest (RF) feature generation, and delivers a 71% accuracy result. For the interactive delineation component, more intuitive delineation functionalities, covering more application cases, are presented for large data sets in Kenya, Rwanda, and Ethiopia.