• Journal of Cadastre & Land InformatiX
• /
• v.52 no.1
• /
• pp.105-118
• /
• 2022

The demand for information related to 3D spatial objects model in metaverse, smart cities, digital twins, autonomous vehicles, urban air mobility will be increased. 3D model construction for spatial objects is possible with various equipments such as satellite-, aerial-, ground platforms and technologies such as modeling, artificial intelligence, image matching. However, it is not easy to quickly detect and convert spatial objects that need updating. In this study, based on spatial information (features) and attributes, using matching elements such as address code, number of floors, building name, and area, the converged building DB and the detected building DB are constructed. Both to support above and to verify the suitability of object selection that needs to be updated, one system prototype was developed. When constructing the converged building DB, the convergence of spatial information and attributes was impossible or failed in some buildings, and the matching rate was low at about 80%. It is believed that this is due to omitting of attributes about many building objects, especially in the pilot test area. This system prototype will support the establishment of an efficient drone shooting plan for the rapid update of 3D spatial objects, thereby preventing duplication and unnecessary construction of spatial objects, thereby greatly contributing to object improvement and cost reduction.

• Journal of Cadastre & Land InformatiX
• /
• v.52 no.1
• /
• pp.69-80
• /
• 2022

Drone photogrammetry generally acquires images vertically or obliquely from above, so when photographing for the purpose of three-dimensional modeling, image matching for the ground of a building and spatial accuracy of point cloud data are poor, resulting in poor 3D mesh completeness. Therefore, to overcome this, this study analyzed the spatial accuracy of each drone image by acquiring smartphone images from the ground, and evaluated the accuracy improvement and completeness of 3D mesh when the smartphone image is not combined with the drone image. As a result of the study, the horizontal (x,y) accuracy of drone photogrammetry was about 1/200,000, similar to that of traditional photogrammetry. In addition, it was analyzed that the accuracy according to the photographing method was more affected by the photographing angle of the object than the increase in the number of photos. In the case of the smartphone image combination, the accuracy was not significantly affected, but the completeness of the 3D mesh was able to obtain a 3D mesh of about LoD3 that satisfies the digital twin city standard. Therefore, it is judged that it can be sufficiently used to build a 3D model for digital twin city by combining drone images and smartphones or DSLR images taken on the ground.

• Journal of Cadastre & Land InformatiX
• /
• v.51 no.2
• /
• pp.83-106
• /
• 2021

Interests in clean fuels have been soaring because of environmental problems such as air pollution and global warming. Unlike fossil fuels, hydrogen obtains public attention as a eco-friendly energy source because it releases only water when burned. Various policy efforts have been made to establish a hydrogen based transportation network. The station that supplies hydrogen to hydrogen-powered trucks is essential for building the hydrogen based logistics system. Thus, determining the optimal location of refueling stations is an important topic in the network. Although previous studies have mostly applied optimization based methodologies, this paper adopts machine learning to review spatial attributes of candidate locations in selecting the optimal position of the refueling stations. Machine learning shows outstanding performance in various fields. However, it has not yet applied to an optimal location selection problem of hydrogen refueling stations. Therefore, several machine learning models are applied and compared in performance by setting variables relevant to the location of highway rest areas and random points on a highway. The results show that Random Forest model is superior in terms of F1-score. We believe that this work can be a starting point to utilize machine learning based methods as the preliminary review for the optimal sites of the stations before the optimization applies.

• Journal of Cadastre & Land InformatiX
• /
• v.51 no.2
• /
• pp.51-72
• /
• 2021

The purpose of this study is to explain the types and causes of errors in zoning data that occur in the topographic map notification procedure, and to prepare a data maintenance plan. In Korea, like the United States, law-based land use regulation is dominant. In other words, according to the land use regulation method in the Act, the government designates zoning for all lots in the country, and landowners check the land use regulations of their land through the Korea Land use Information System. The land use plan confirmation document is important land information that affects the results of administrative dispositions such as land transactions between individuals or permission for development activities. However, there are data errors that occur during the current topographic map notification procedure and data construction process. Therefore, four local governments that can verify data by type were selected in consideration of local government conditions. A number of errors are first, errors in data construction and management in the Korea Land use Information System, and second, errors in lack of expertise that occur while the local government officials maintain data. Third, it was analyzed as an error from the relationship between the serial cadastral map and the zoning DB. Based on the above results, it is hoped that the results of this study will be reflected in the establishment of the KLIP and the reform of the legal system, which is currently underway after the establishment of the 「3rd the Korea Land use Information System Construction Plan」.

• Journal of Cadastre & Land InformatiX
• /
• v.51 no.2
• /
• pp.151-168
• /
• 2021

This study compared and analyzed the effect of the burden of household expenditure in the metropolitan and non-metropolitan areas on the intention to childbirth intention to married women using a panel logit model. To this end, this analysis targeted married women aged 25 to 39 who are highly likely to be fertile. The main results are as follows; First of all, it was confirmed that the economic power of spouse can be an important factor in the childbirth intention regardless of region. Second, it was found that the higher the satisfaction of marriage had a positive effect on the childbirth intention, and also higher the value that children must have, the higher the childbirth intention. Third, it was confirmed that the burden of household expenditure is the most important factor in the willingness to childbirth intention, excluding factors such as the number of existing children. In particular, the burden on education spending in both the capital region and non-capital region was found to be a key reason for the decrease in the childbirth intention. Lastly, the burden of household expenditure showed different effects on childbirth intention on depending on the region. Specifically, in the capital region, medical spending and loans had a greater impact, while, in the non-capital region, transportation and communication costs had a greater impact on childbirth intentions. Through the results, this study confirmed the implication that easing the burden on household expenditure is continuously necessary to enhance childbirth, and that discriminatory policy approaches are required depending on the area of residence.

• Journal of the Korean Geosynthetics Society
• /
• v.21 no.2
• /
• pp.49-56
• /
• 2022

This study conducted to obtain the lateral resistance of a wind power foundation reinforced with piles through an model experiment. In particular, the lateral resistance of the foundation was compared with the existing gravity-type wind power foundation by integrating the pile, the wind power generator foundation, and the rocky ground. In addition, changes in the lateral resistance and bending moment of the pile were analyzed by embeded depths of the pile. As a result, it was found that the lateral resistance increased with the depth of embedment of the piles. In particular, the pile's resistance increase ratio was 2.11 times greater in the case where the pile embedded up to the rock layer than the case where the pile was embedded into the riprap. It was found that the location of the maximum bending moment occurred at the interface between the wind turbine foundation and the riprap layer when the pile embeded to the rock layer. Through this, as the lateral resistance of the wind power foundation reinforced with piles is greater than that of the existing gravity-type wind power foundation, it is understood that it can be a more advantageous construction method in terms of safety.

• Journal of the Korean Geosynthetics Society
• /
• v.22 no.3
• /
• pp.97-103
• /
• 2023

Recently, research on the lateral resistance of pile foundations has been actively conducted. In experimental studies on the lateral resistance of pile foundations, displacement control or load control methods are used. However, in the case of the displacement control method, the lateral resistance of the pile varies depending on the rate of the load applied to the pile. Therefore, this study seeks to determine the change in lateral resistance of pile foundations according to lateral loading rate through model experiments. The experimental results showed that the lateral resistance of the pile tended to decrease as the lateral loading rate applied to the pile head increased. In order to confirm this, a model experiment of the side change of the ground and pile according to the loading rate was additionally conducted. Through inverse analysis, the change in the depth of the rotation point according to the lateral loading rate was identified. Through the change in the lateral resistance of the pile foundation and the depth of the rotating point according to the lateral loading rate, it was proposed to test the loading rate within 1.5 mm/min during the lateral loading test of the pile.

• Journal of Cadastre & Land InformatiX
• /
• v.52 no.2
• /
• pp.121-135
• /
• 2022

The purpose of this study is to explore the convergence sector of the spatial information industry based on the business transaction data of spatial information companies and to predict the market size of the industry using the Seemingly Unrelated Regression(SUR) model. The convergence part of spatial information industry, which cannot be identified in the Spatial Data Industry Survey, was analyzed by exploring keywords related to spatial information using the business DB of Korea Enterprise Data (2010-2019). The convergence of spatial information businesses mainly appeared in the business relationship between the value chain between Seoul and Gyeonggi Province. The convergence business has the largest sales in the value chain 2 (utilization, service) & 3 (convergence), and also the convergence in the value chain 1 (production, construction) & 2, 2 & 3 stages has doubled in 2019 compared to 2010. In 2019, the total sales of the spatial information industry based on the Statistical Korea were announced at about 8 trillion won, but in this study, the total sales of the spatial information industry were estimated at 28 trillion won considering convergence activities. Finally, when scenario 1 (0.38% population growth, 2020-2024) and 0.07% (2026-2030) were applied using the SUR model to predict the expected market size of the industry, sales decreased by -0.37% to 0.069% in 2025 and 2030 by respectively. When scenario 2 (average wage growth 1.2%) was applied during the same period, sales in the industry increased by 2.326% to 12.185%. In other words, the sales in the spatial information industry depends on Labor, Total Factor Productivity, and Capital Productivity so it is necessary to additional research on policy development and alternatives of enhancing each productivity.

• Journal of Cadastre & Land InformatiX
• /
• v.52 no.2
• /
• pp.17-34
• /
• 2022

With rapid advancements taking place in the Fourth Industrial Revolution, human-robot interactions have been garnering increasing attention. Robots are being actively adopted in building systems and facilities. In this study, we developed robot-friendly building certification indicators. Because these indicators were being developed for the first time, we focused only on commercial buildings. We conducted exploratory research using methodologies such as focus group interviews and the analytic hierarchy process. First, the concept of the robot-friendly building was defined through focus group interviews, and the requirements were categorized by the appropriateness of operating facilities and systems and the appropriateness of architectural and robot operating systems and networks. Next, the relative importance of the evaluation items (23 items in total) was calculated using the analytic hierarchy process. Their average score of the marks was 4.4, and the minimum and maximum were 2.0 and 11.3, respectively. This study is significant because we collected the basic data necessary to develop a one-of-its-kind evaluation system for certifying the robot-friendliness of buildings using scientific methods.

• Journal of Cadastre & Land InformatiX
• /
• v.52 no.2
• /
• pp.211-231
• /
• 2022

This study attempted to identify the effects of macroeconomic variables such as the All Industry Production Index, Consumer Price Index, CD Interest Rate, and KOSPI on apartment lease prices divided into nationwide, Seoul, metropolitan, and region, and to present a methodological prediction model of apartment lease prices by region using Long Short Term Memory (LSTM). According to VAR analysis results, the nationwide apartment lease price index and consumer price index in Lag1 and 2 had a significant effect on the nationwide apartment lease price, and likewise, the Seoul apartment lease price index, the consumer price index, and the CD interest rate in Lag1 and 2 affect the apartment lease price in Seoul. In addition, it was confirmed that the wide-area apartment jeonse price index and the consumer price index had a significant effect on Lag1, and the local apartment jeonse price index and the consumer price index had a significant effect on Lag1. As a result of the establishment of the LSTM prediction model, the predictive power was the highest with RMSE 0.008, MAE 0.006, and R-Suared values of 0.999 for the local apartment lease price prediction model. In the future, it is expected that more meaningful results can be obtained by applying an advanced model based on deep learning, including major policy variables