**1. Introduction**

Cadastral maps show the boundaries and ownership of land parcels that separate adjacent land plots. These maps contain spatial information, such as shape, size, boundary, and location, as well as non-spatial information, such as land use, value, and tenure, which are uniquely encoded in textural or attribute files [1]. Moreover, cadastral maps are available as large-scale base maps with micro-level mapping [2]. As cadastral maps are related to personal properties, accurate cadastral mapping can

improve agricultural productivity and support the national development policy [3]. Moreover, a well-structured cadastral map is a prerequisite for improved land management services [4].

A cadastral map is updated by modifying the spatial and non-spatial data of the existing cadastral maps to reflect the latest land information. High-quality cadastral mapping requires updating the changes in land use information and the spatial division of property units [5]. The land use type, which indicates the purpose of use, is registered and managed as an attribute of "land category" in a cadastral system. Therefore, the items of land category can be assigned according to their land use type, such as "Building site," "Parking lot," and "Road." Cadastral map updates are essential for not only recording the most recent land ownership and property division changes in a timely manner but also effectively managing the land information. For example, updating is necessary when the land is suddenly changed by new sub-divisions, transfer of land use, and natural disasters [6]. Furthermore, from the aspect of tax imposition, which is a main purpose of land use management by cadastral mapping [7], updating cadastral maps is crucial because the tax imposed on land owners depends on their land use type. Frequent updates of cadastral information can better manage illegal land use, whereby landowners register false land uses to reduce their taxes.

The procedure of updating cadastral maps can be divided into three steps: (1) extracting meaningful features and generating new data, (2) comparing new data with the existing base map and detecting changes, and (3) updating the base map with those changes and verifying the consistency of the updated map and actual information [8–10]. As the step of extracting relevant features, both up-to-date spatial and non-spatial information, such as parcel boundaries and land category, can be generated. Traditionally, cadastral surveying is performed by field work, aerial monitoring, and satellite data acquisition [5]. Although field surveying acquires accurate land information, it is extremely time-consuming and requires well-trained manpower for wide-area implementation. Remote sensing (RS) can be an effective alternative to field work because it is cheaper and faster compared to conventional cadastral surveys [3], and it is a useful data source for many base map-updating activities [8]. Cadastral boundaries set by roads, building, and water are visible in RS images and can be mapped from them.

To consider both generation of cadastral information and the further step of updating, which include a comparison between the generated information and existing cadastral maps, an integrated method that improves the efficiency of cadastral mapping and updating was proposed [5]. Using three bands of QuickBird satellite data, a digital and elevation model, and global navigation satellite system (GNSS) data, this method registers fused images to the existing cadastral map. After superimposing the boundaries of the cadastral map on the fused images, the map is updated by visual interpretation using a participatory geographic information system. Furthermore, the cadastral image was updated using CARTOSAT-2 panchromatic satellite images with 1.0 m resolution and Geo-eye multispectral images with GNSS data and 0.5 m resolution [6]. In this study, cadastral maps were updated by extracting the parcels from those images, along with three parameters (area, perimeter, and position) related to spatial elements, while non-spatial elements were not considered. Wassie et al. [11] proposed a procedure for extracting cadastral boundary information by semi-automatically using the WorldView-2 satellite data. In this study, the procedure of comparing the extracted information with the existing one, which is reference digital parcel boundaries, was performed in two ways: visual interpretation and quantitative analysis. The recentness of the information was not considered during the comparison, because this study aimed at verifying the accuracy of the extracted information rather than change detection for updating. Furthermore, the proposed procedure only focused on the parcel boundary, which comprises spatial elements.

Several previous studies on cadastral mapping and updating dealt with non-spatial updates, but focused only on extracting up-to-date information from the source date. The comparing and detecting changes step of updates were dependent on visual interpretation or performed in a limited range [2,9,10,12–14]. Specifically, Khadanga et al. [14] classified land use in cadastral parcels extracted from high-resolution satellite imagery through object-based image analysis (OBIA). The result layer of

OBIA was written into a shapefile and compared with a digitized map of the cadastral parcels. The digitizing was manually performed and the comparisons were visually analyzed. Avramovi´c et al. [12] updated the status of rural land use only from digital cadastral maps. Although they compared the land category items in the cadastral map with those in the real estate cadaster, they did not provide the details of the comparison.

To automate the comparison between the newly generated and existing cadastral information, the authors of [15] suggested a map-query-based comparison between the cadastral map and the land-cover map from satellite images. They generated the land cover map from Landsat TM satellite images and matched the land cover classes with the land category items in South Korea [15]. However, the spatial resolution of satellite images is relatively high (30 m) and obtaining images at the desired time is hindered by the time resolution and noise, such as cloud. After generating the land cover map, the authors performed a binary analysis of the pixel-level inconsistency between the land cover and cadastral maps. Although they analyzed heterogeneous data at the pixel-level, they calculated only the ratio of inconsistent area to the entire test area without considering the inconsistency by parcels or land category items. In conclusion, to improve the efficiency of the overall update process, it is vital to automate the comparison of up-to-date information with existing cadastral maps and the detection of parcel discrepancies.

When improving the efficiency of updating cadastral maps, one must consider the elaborateness of the latest cadastral information generated through various cadastral surveys. Although aerial and satellite surveying techniques acquire data over large-scale regions with superior spatial resolution, they are influenced by weather conditions, old acquisition time, and military security problems [3,5]. Therefore, unmanned aerial vehicles (UAVs) have recently been deployed for extracting up-to-date cadastral information. UAVs are cost-effective, especially in local applications, and acquire real-time data at high spatial resolution [10,16,17]. Manyoky et al. [18] noted that UAVs collect detailed information. Moreover, UAV-based methods enable an efficient documentation of the non-spatial information in cadastral maps, such as land use and vegetation. Relevant features are often extracted from orthophotos generated from UAV images using various feature-extraction methods such as image classification, segmentations, and line extraction [9]. As an example of cadastral mapping and updating with UAV imagery, areas subjected to landslides, which manifest as a sudden change in land use, were automatically detected from UAV images [19]. The detected changes in land use provided the basis information for synchronizing the cadastral information. However, the target area was manually extracted through an overlay analysis between the information extracted from the UAV and the cadastral map. Moreover, the updates were performed on limited target parcels (landslide areas) rather than the whole area. Manyoky et al. [18] compared the use of UAVs with the tachymeter–GNSS combined method in cadastral mapping and updating. The acquired points for generating and updating the cadastral maps were classified by land cover, such as vegetation types, buildings, and streets. However, the authors did not thoroughly describe the data processing steps for generating and updating the cadastral information.

As mentioned earlier, the land category as non-spatial data directly affects land value estimation and thus needs to be up-to-date in a short cycle. The inconsistency between the registered land category and actual land use when updating cadastral maps is a well-reported problem in cadastral mapping [12,13,17,20]. In South Korea, items of land category are determined based on the primary use of each parcel [21], which is directly related to the assessment value of the land [15]. Therefore, it is a legal obligation to correct the registration if the registered land information differs from the actual land use information [21]. New information can be updated by the land owner's registration. Fines or imprisonment may be imposed for those who do not inform about a change in land category or a false notification [21,22].

As the accuracy of an update is associated with ownership of property, the update must be verified through a field survey. This is especially important for updates of cadastral systems (including maps). Detecting the areas requiring update is crucial for reducing the target area of the field survey and

improving the effectiveness of the field work. An automatic process would facilitate cadastral mapping and updating. To this end, the present study proposes a new discrepancy analysis method that automatically detects candidate parcels requiring an update of their land category information. The proposed method is implemented in two stages: generating up-to-date land category information and comparing the new information with the existing cadastral information. To effectively extract the land cover, we use hyperspectral UAV images and a deep learning approach. Hyperspectral UAV images can spectrally discriminate similar materials that cannot be identified in RGB or multi-spectral images captured at specific times. In the latter stage, the land category information generated from a UAV is compared with the existing cadastral map managed by the government. The comparison process generates a discrepancy map representing the parcels requiring update. The major contributions of the proposed method are stated below.


The remainder of this paper is organized as follows. Section 2 proposes our discrepancy analysis method, and Section 3 describes the datasets, environmental conditions of the experiments, and the results of a case study in South Korea. Finally, the conclusions are provided in Section 4.
