**5. Concluding Remarks**

Land tenure relations, which are relevant in the Korean (unification) context, include the difference between private, State and collective land, the type and location of land use rights, the spatial allocation of rights and restrictions, the ability and spatio-temporal changes of transferring land rights, and the spatial restrictions in access and use. So far, such land tenure relations could only be derived when combining topographic data with agricultural census data at the regional or national scale, and household surveys and a participatory mapping at the local scale. However, given documented insights into the nature of spatial arrangements and the similarities and patterns when observing in features of typical land use structures in North Korea, it was possible to derive proxies for particular types of land tenure from openly accessible EO data.

The proxies consist of specific combinations and patterns of shapes, colors, textures related to physical structures such as roofs, types of buildings, infrastructures, types of land use and vicinity of comparable features. The assumptions connected to these proxies relate to fundamental notions of tenure claims and interests such as collective ownership, land lease and use, occupation (reclamation), transaction (sell and convey) and land access (servitude and rights of way). Overall, this study strengthens the idea that data mining for North Korea related land (tenure) information in the context of Korean (re-)unification is possible and feasible.

The application of EO data involves image processing and data mining technologies which can help to generate a better insight in current land and property interests (such as land tenure, land rights, land responsibilities and duties related to land and properties), and to better prepare, execute, enforce, assess and monitor land interventions. In the context of (re-)unification, the sample tests are particularly relevant for re-uniting countries where different land tenure systems exist and where the data are not coherent. For example, prior to the (re-)unification in Germany, there were two different systems of land tenure, which co-existed next to each other. Unifying the system in Germany was difficult at first partly because each of the previous countries had recorded and administrated in a significantly different manner. A similar situation exists in anticipation of a unified Korea, especially considering that little information is available about the varieties of land tenure and the missing links to individual people in North Korea.

One way to overcome this challenge is to detect land tenure with the use of remote sensing and open access aerial and satellite images. Normally, this technique is possible when having access to ground control points, civil registers and semantic interpretation of both the tenure and the people's components. When this information is however missing or this data source is unreliable—as is the case in North Korea—one has to rely on a number of assumptions and a set of test trials, which if proven right, can be generalized with artificial intelligence connected to image processing. In other words, one has to understand the socio-legal relations to land with pixel. This socio-legalizing the pixel is still largely an idea rather than an available set of techniques. In order to develop such techniques, which will ultimately facilitate the land tenure unification process in Korea, and possibly also improve existing land tenure records (including both public/private land rights, restrictions and responsibilities), one needs a collaborative research development.

The next step is to enrich and test the information quality of the above assumptions and proxies with empirical data tests, inclusion and reflections of local knowledge on the ground (focusing on North Korean defectors' perspectives) and expert knowledge in EO and land administration sciences in North and South Korea. The findings in Section 4 could also help for creating more machine-learning and deep-learning algorithms that provide reference to other papers. The construction of these algorithms was, however, beyond the scope of this paper.

**Author Contributions:** Conceptualization, C.L. and W.T.d.V.; Data curation, C.L.; Formal analysis, C.L.; Investigation, C.L.; Methodology, C.L.; Project Administration, C.L.; Resources, C.L.; Supervision, W.T.d.V.; Validation, C.L. and W.T.d.V.; Visualization, C.L.; Writing—original draft, C.L.; Writing—review and editing, C.L. and W.T.d.V. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the German Research Foundation (DFG) and the Technical University of Munich (TUM) in the framework of the Open Access Publishing Program.

**Acknowledgments:** We thank three anonymous reviewers for their insightful comments in narrowing the gap between our claims and the actual content of the manuscript. We are also immensely grateful to colleagues who provided insight and expertise that greatly assisted the research, although any errors are our own and should not tarnish the reputations of these esteemed persons.

**Conflicts of Interest:** The authors declare no conflict of interest.
