• Journal of Cadastre & Land InformatiX
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• v.52 no.1
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• pp.5-16
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• 2022

Based on the general formula for the area error of a polygon and rectangular parcel, the constant term 0.026² × M (scale denominator) of the area error calculation formula prescribed by the Enforcement Decree was analyzed. As a result, it is found that the formula appropriately reflects the characteristics of the graphical surveying as a typical rectangular parcel model, but quantitatively allows a relatively large area error. In addition, it is found that, even if the area is the same, 50% more area error than a square parcel could be calculated depending on the shape of the parcel, and that the allowable area error should be different when dividing a parcel. Based on the analysis, furthermore, this study shows a solution that can solve the problems at once from the point of cadastral surveying. These are, the problem of reflecting the accuracy of the surveying, the problem of reflecting the size and shape of the parcel, and the problem whether a single area error formula can be used without having to distinguish between graphical and numerical surveyings. The new formula that solves these problems will bring about improvements in many related factors and promote the development of digital cadastral system.

• Journal of the Korean Geographical Society
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• v.46 no.6
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• pp.724-737
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• 2011

The goal of solving a contiguous land acquisition problem is to find an optimal cluster of land parcels so that one can move from an acquired parcel to another without leaving the cluster. In many urban and regional planning applications, criteria such as acquisition cost and compactness of acquired parcels are important. Recent research has demonstrated that spatial contiguity can be formulated in a mixed integer programming framework. Spatial compactness, however, is typically formulated in an approximate manner using parameters such as external border length or other shape indices of acquired land parcels. This paper first develops an alternative measure of spatial compactness utilizing the characteristics of the internal structure of a contiguous set of land parcels and then incorporates this new measure into a mixed integer program of contiguous land acquisition problems. A set of computational experiments are designed to demonstrate the use of our model in a land acquisition context.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.3
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• pp.47-54
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• 2013

Real estate development promotes the efficient management of the land and has immense impact on national economy. So, it is necessary to establish a comprehensive policy by accumulating related information. Real estate development map must have the information about a parcel and adjacent parcel to make it possible to analysis in spatial domain, because error will be manifested in analysis using only real estate attribute information. This can be allocate the attribute of public management information about real estate on cadastral information and spatial information for social infrastructure and buildings. Especially, it must maintain the latest information of changes in the shape and the attributes of the buildings. This study suggested a method for developing a real estate development map based on road name address map and a method to maintain the latest information through the connection with related systems.

• Journal of Cadastre & Land InformatiX
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• v.54 no.1
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• pp.19-32
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• 2024

This study examined the feasibility of image-based surveys by detecting objects in facilities and agricultural land using the YOLO algorithm based on drone images and comparing them with the land category by law. As a result of detecting objects through the YOLO algorithm, buildings showed a performance of detecting objects corresponding to 96.3% of the buildings provided in the existing digital map. In addition, the YOLO algorithm developed in this study detected 136 additional buildings that were not located in the digital map. Plastic greenhouses detected a total of 297 objects, but the detection rate was low for some plastic greenhouses for fruit trees. Also, agricultural land had the lowest detection rate. This result is because agricultural land has a larger area and irregular shape than buildings, so the accuracy is lower than buildings due to the inconsistency of training data. Therefore, segmentation detection, rather than box-shaped detection, is likely to be more effective for agricultural fields. Comparing the detected objects with the land category by law, it was analyzed that some buildings exist in agricultural and forest areas where it is difficult to locate buildings. It seems that it is necessary to link with administrative information to understand that these buildings are used illegally. Therefore, at the current level, it is possible to objectively determine the existence of buildings in fields where it is difficult to locate buildings.