Discrepancy Analysis for Detecting Candidate Parcels Requiring Update of Land Category in Cadastral Map Using Hyperspectral UAV Images: A Case Study in Jeonju, South Korea

Abstract

The non-spatial information of cadastral maps must be repeatedly updated to monitor recent changes in land property and to detect illegal land registrations by tax evaders. Since non-spatial information, such as land category, is usually updated by field-based surveys, it is time-consuming and only a limited area can be updated at a time. Although land categories can be updated by remote sensing techniques, the update is typically performed through manual analysis, namely through a visually interpreted comparison between the newly generated land information and the existing cadastral maps. A cost-effective, fast alternative to the current surveying methods would improve the efficiency of land management. For this purpose, the present study analyzes the discrepancy between the existing cadastral map and the actual land use. Our proposed method operates in two steps. First, an up-to-date land cover map is generated from hyperspectral unmanned aerial vehicle (UAV) images. These images are effectively classified by a hybrid two- and three-dimensional convolutional neural network. Second, a discrepancy map, which contains the ratio of the area that is being used differently from the registered land use in each parcel, is constructed through a three-stage inconsistency comparison. As a case study, the proposed method was evaluated using hyperspectral UAV images acquired at two sites of Jeonju in South Korea. The overall classification accuracies of six land classes at Sites 1 and 2 were 99.93% and 99.75% and those at Sites 1 and 2 are 39.4% and 34.4%, respectively, which had discrepancy ratios of 50% or higher. Finally, discrepancy maps between the land cover maps and existing cadastral maps were generated and visualized. The method automatically reveals the inconsistent parcels requiring updates of their land category. Although the performance of the proposed method depends on the classification results obtained from UAV imagery, the method allows a flexible modification of the matching criteria between the land categories and land coverage. Therefore, it is generalizable to various cadastral systems and the discrepancy ratios will provide practical information and significantly reduce the time and effort for land monitoring and field surveying.

Testing and Validating the Suitability of Geospatially Informed Proxies on Land Tenure in North Korea for Korean (Re-)Unification

“…It is only after knowledge (evidence) co-production processes, the finding of this study supports the view that we can bridge and close the gap between technical aspects of the EO data evidence generation and operational contexts in spatial decision making in land administration and management. Much of the available literature so far on remote sensing for land administration is too product-oriented for skilled and trained technicians [12][13][14][15][16][17][18][19] and insufficiently process-oriented for policymakers and end-users, allowing them to make decisions in the most rational and informed way possible with EO data [22]. The point is not to go against the promising ideas on RS applications, techniques, products, and methods, but to really emphasize that it is an opportune time to undertake the most engaged and negotiated knowledge for both evidence generation and provision of salient and legitimate evidence in responsible and smart decision-making in land administration.…”

“…Land administration deals with the people-to-land relationship, by describing, analyzing, designing, and measuring their relations that include social, economic, spatial, legal, and engineering perspectives [11]. Along with the growth of remote sensing applications in SES, there has been growing recognition and evidence of the vital links between remote sensing and land administration [12][13][14][15][16][17][18][19]. Geospatial data has emerged as powerful source of information for enabling both socio-technical assessment [20] and socio-legal analysis [21] in land administration sphere.…”

Remote Sensing for Land Administration

“…First, inspired to deliver an alternative to ground-based surveying for the collection of non-boundary cadastral information, Park and Song [32] present a study aimed at remote identification of the discrepancy between existing cadastral maps (which include use information), and current on-ground land uses. The proposed method involves updating the existing land cover attributes of a land parcel maps using UAV hyperspectral imagery classified using CNN, and then composing a discrepancy map showing land use differences.…”

Transfer Change Rules from Recurrent Fully Convolutional Networks for Hyperspectral Unmanned Aerial Vehicle Images without Ground Truth Data

“…Hyperspectral UAV images of two sites in Jeonju City in South Korea were used for CD. The dataset was acquired from the previous study [41]. The temporal hyperspectral UAV images were acquired on September 19, 2019 (T 1 ), and October 16, 2019 (T 2 ), respectively.…”