*4.6. Summary of Discussion*

This section summarizes the findings to identify proxies to derive unknown land tenure relations over North Korea (see Table 4). The first set of questions aimed to address that whether the observed land is State or collective farmlands. There was no significant difference between two groups in both the general spatial arrangements in scope. However, we found that the location-specific features in line with physical and temporal characteristics helps to identify single objects on (dry) paddy fields. This is one of the most significant characteristics for detecting visually analogues arrangements of collective farmlands. On the other hand, what stands out in the State (farm) land is the combination of the geometric properties of objects characterizes a common feature of agriculture-based patches and infrastructures.

In order to assess the feasible proxies of LURs without having access to the ground, the different EO data sources have been utilized to not only detect LULC changes, but also ascertain a variety of dwellings and its morphological features. These EO datasets present a plausible interpretation with the association element that addresses the probable occurrence among different sets of entities as well as socio-legally documented local knowledge that leads to confirmation of LURs.

The cooperative farms under the collective ownership regime typically accompany a socialist morphology, with the unification of forms and construction to distinguish whether individual or group-based LURs, proxies such as building geometry, arrangement pattern, and site characteristics could define multiple LURs of a group over the same piece of land. The regular arrangement patterns of sites and building objects with other contextual knowledge is a common feature, thus representing group-based LURs that can be jointly used by the multiple groups of people.

Another question sought to determine whether there are LTRs or not. With respect to this subject, it was hypothesized that households reclaimed and cultivated vacant land as well as cleared the forests, and made transactions between households after illegal occupation. Based upon the normative concept of 'small-land (Sotoji)', the discernable proxies that prove the existence of LTRs with following elements of image interpretation are: the low elevation, slopes have gentle slopes less than 15%, small

and regular/irregular patches of vegetation cover, the length or width, location, colors and adjacency to the specific objects.

**Table 4.** Identifying proxies to derive unknown land tenure relations over North Korea in conjunction with EO data (devised by authors).


The combination of multiple man-made structures over a single parcel of land or entire property over the land provides some support for the conceptual premise. Although all land belongs to the

State, both the State and the collectives can restrict the use by restricting access for public purpose. Among the elements of image interpretation, only site or situational elements are valid and reliable in detecting the public rights of way. We then produced only few proxies by deriving similar site and situational features from nine images as shown in Table 4.

While some progress has been made for cadastral mapping, very little was found on the question of how we bridge the semantic gap between land tenure and EO data. Thus, this account, in methodological terms, seeks to propose a new notion of remote-sensing based proxies for interpreting land tenure relations that could be transferable and applicable in land administration domain at a semantic level. With regard to the research findings, some limitations need to be acknowledged. A first limitation is that since this study was only conducted from steps 1 to 3, validating was beyond the scope of this work (see Figure 2). However, the preliminary investigations indicated that the subsequent steps for validation (steps 4 to 6) will further move us closer to developing a full picture of the identification of transferrable and applicable proxies for geospatially informed analysis. In other words, it is possible that these results are only valid when a holistic methodological approach takes place. This experiment also has not suggested any technological advancements yet and the proposed proxies require a rigorous AI-based (semi-)automated image interpretation of EO data with other complementary sets of proxies. One possible implication of this is that algorithmic approaches and methodologies concerning deep-learning networks will be able to mine land tenure relations from EO data and these are divided into: supervised learning approaches trained from scratch, pre-training and fine-tuning approaches, advanced learning techniques, and novel technologies developed by the remote-sensing community [86].
