**6. Conclusions**

Non-spatial data in cadastral maps, such as land use and land ownership is generally updated by field survey and updated manually after visual interpretation of source data, such as RS imagery. This study proposed an approach for analyzing the inconsistent areas between cadastral maps and hyperspectral UAV images. The proposed methods focus on the update of land category which is the attribute data that explain the characteristics of the parcel.

As a case study, the proposed discrepancy analysis was applied to the South Korea cadastral map, which includes 28 land categories. Land cover maps were generated from hyperspectral UAV images by using a hybrid CNN. The hybrid CNN outperformed previous 2D-CNN and 3D-CNN. The OAs of the land cover map using the hybrid CNN at Sites 1 and 2 were 99.93% and 99.75%, respectively. For comparing the two heterogeneous datasets, the existing cadastral map and the land cover map were encoded. After vectorization, the attributes of the combined vector map were decoded to recover the information of land categories and their coverage. The final discrepancy maps with different discrepancy ratios were generated through a query-based comparison. The discrepancy map reveals the inconsistent parcels, which are used illegally or which need to be subdivided. The

discrepancy ratios of 39.4% and 34.4% of the parcels at Sites 1 and 2, respectively, were 50% or higher. The discrepancy was high in parcels containing building sites or newly constructed buildings on the cadastral map, but were being used as crop land. As our approach can automate the detection of inconsistent land parcels, it is expected to be applied to large areas and various scenarios. Therefore, they are time- and cost-effective alternatives to field surveys for cadastral map updates and the update cycle can be shortened because the required imagery is taken by UAVs.

**Author Contributions:** Conceptualization, Methodology, Software, Validation, Formal analysis, Data curation, Writing (original draft preparation), Writing (review and editing), A.S. and S.P.; Funding acquisition, Supervision, Project administration, A.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by the National Research Foundation of Korea (NRF) funded by the Korean government (MSIT) (grant no. 2019R1A6A3A0109230211) and by a research and development program funded by the Spatial Information Research Institution of Korea Land and Geospatial Informatix Corporation.

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