*4.2. Detecting Inconsistent Parcels*

According to the relevant regulations in South Korea [21], "cadastral inconsistent parcels" that need to be updated cover the following situations: (1) when the geometric information, such as parcel boundary and area, differs from the actual geometry; (2) when the parcel information is registered incorrectly in the cadastral system; (3) when the parcel information is registered differently from the land survey results; and (4) when the land owner requests an information change. Therefore, we detected the inconsistent parcels requiring update on discrepancies in the land category information.

For the inconsistency comparison, the cadastral maps were converted into a raster structure and compared with the land cover maps generated as the classification results in raster format. The rasterized cadastral map at each site was created by assigning the land category and parcel ID, as shown in Figure 9. The cadastral map comprised polygons containing the parcel boundary information; however, the geometric information representing the parcel boundary was lost during the rasterization process. As a parcel-wise comparison was required for detecting the inconsistent areas between two maps, the cadastral map was rasterized with the parcel ID, as shown in Figure 9b,d. Figure 9 shows the rasterized cadastral map with land category with the same color palette as that of the land cover map, whereas randomly selected colors were used to represent a rasterized cadastral map with parcel ID.

**Figure 9.** Cadastral maps rasterized with (**a**) land category at Site 1, (**b**) parcel ID at Site 1, (**c**) land category at Site 2, and (**d**) parcel ID at Site 2.

The query-based comparison could be automated because each polygon contained the land cover information from the HSI along with the land category information from the cadastral map. Classifying the actual land cover information from images in response to the cadastral map system, specifically to the land category framework, is a technically difficult problem [15]; the criteria for defining the land cover classes that can be extracted from HSIs are difficult to reconcile with the land category items in the cadastral map. Therefore, before identifying the inconsistent areas in the cadastral map and the actual land cover map, we must establish a mapping rule between the matched classes under each framework. Then, the consistency of the land cover and land category can be determined from the mapping rule. Because the land category items and land cover classes were classified by different criteria, they could not be matched one-to-one. Based on empirical investigations of the test sites, this study defines *M*:*N* matching pairs of land category items and land cover classes (Figure 10). Finally, a query for inconsistency comparison could be made using the mapping rule shown in Figure 10.

**Figure 10.** Mapping rule between land category and land cover.

Land cover was classified into crops, forests, buildings, roads, water, and bare soil, which can be distinguished in HSIs. Figure 10 is constructed from the 28 land category items used in the Korean cadastral system. These items and their rules can be adjusted to other cadastral systems, providing source information for other countries. Furthermore, because the query can be modified according to the mapping information, the proposed technique is applicable to the discrepancy analyses of other cadastral systems.

Panels (a) and (d) of Figure 11 present the combined raster maps of Sites 1 and 2, respectively. In these maps, the land cover, land category, and parcel boundary information were combined by encoding with the rasterized cadastral maps and the generated land cover map. The combined raster map was restructured in vector format, and the parcel boundary with the land cover and land category attributes was retrieved by decoding the assigned values (Figure 11b,e; note that each parcel contains many polygons). Finally, the discrepancy map was generated by dissolving the combined vector map based on the parcel ID, leaving only the parcel boundaries, as shown in Figure 11c,f.

**Figure 11.** Result of the proposed process: (**a**) combined raster map of Site 1, (**b**) combined vector map with attributes of Site 1, (**c**) discrepancy map of Site 1, (**d**) combined raster map of Site 2, (**e**) combined vector map with attributes of Site 2, and (**f**) discrepancy map of Site 2.

As mentioned earlier, land categories in Korea's cadastral system should be registered based on the primary use of each parcel. The regulations [21] state that if the proportion of parcels used for purposes other than the primary use exceeds a certain level, the parcels must be divided. The polygons in each parcel in the vector layer (Figure 11b,e), which is the intermediate result of the current study, contained the detailed land cover information extracted from the HSI. Therefore, the potential areas to be divided could be concurrently investigated by calculating the polygon area per usage.

The discrepancy maps included the sum of the inconsistent areas as an attribute of each parcel. Therefore, the ratio of the inconsistent area to the total area of each parcel could be calculated. This discrepancy ratio represents the degree of discrepancy (in parcel units) between the land category information registered in the cadastral system and the actual land cover information. Parcels requiring an update of their land category were then identified as those with a large discrepancy ratio. Figure 12 shows the visualization result of the discrepancy ratios between the registered land category information and the actual land cover information extracted from the hyperspectral UAV imagery. If the discrepancy ratio in a parcel exceeds a certain ratio, and the land use of the parcel differs from the registered land category information, this parcel must be separately managed through a site survey. In Figure 12, the discrepancy degree is indicated by a red scale that ranges from white (no discrepancy) to deep red (high discrepancy). In this visualization, the target parcels to be managed can be clearly identified. However, because the threshold discrepancy ratio is not systematically defined, Figure 12 presents three maps of each site with different intervals of discrepancy ratio. More specifically, the inconsistent land parcels in Figure 12c,f are extracted under more rigid criteria than those shown in Figure 12a,d. The deep red regions are the areas that need updating.

**Figure 12.** Visualization result of discrepancy ratios (the intensity of the red polygons directly relates to the discrepancy ratio): (**a**), (**d**) with five classes, (**b**), (**e**) with four classes, and (**c**), (**f**) with two classes.

Table 3 lists the numbers and land types of parcels with discrepancy ratios of 50% or higher. School sites, cemeteries, and factory sites have relatively low inconsistency probabilities because these land categories were mapped in a 1:*N* relationship over various land covers. However, paddy fields and bare fields encompass several inconsistent parcels, because many parcels classified as bare soil are actually crop lands that did not bear any crop at the image acquisition time. In particular, significantly fewer discrepancies of building sites and roads were found at Site 2 than at Site 1, because Site 2 covers many fields, paddy fields, and forests, and fewer urban areas such as building sites and roads.


**Table 3.** Numbers of parcels with discrepancy ratios of 50% or higher.
