• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1009-1029
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• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.4
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• pp.61-69
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• 2010

The collapse of a slope or cut embankment brings much damage to life and property. Accordingly, it is very important to analyze the spatial distribution by calculating the landslide susceptibility in the estimation of the risk of landslide occurrence. The heuristic, statistic, deterministic, and probabilistic methods have been introduced to make landslide susceptibility maps. In many cases, however, the reliability is low due to insufficient field data, and the qualitative experience and knowledge of experts could not be combined with the quantitative mechanical?analysis model in the existing methods. In this paper, new modeling method for a probabilistic landslide susceptibility analysis combined Bayesian Network with ontology model about experts' knowledge and spatial data was proposed. The ontology model, which was made using the reasoning engine, was automatically converted into the Bayesian Network structure. Through conditional probabilistic reasoning using the created Bayesian Network, landslide susceptibility with uncertainty was analyzed, and the results were described in maps, using GIS. The developed Bayesian Network was then applied to the test-site to verify its effect, and the result corresponded to the landslide traces boundary at 86.5% accuracy. We expect that general users will be able to make a landslide susceptibility analysis over a wide area without experts' help.

• Land and Housing Review
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• v.14 no.2
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• pp.125-135
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• 2023

Drones are used in various fields, including land survey, transportation, forestry/agriculture, marine, environment, disaster prevention, water resources, cultural assets, and construction, as their industrial importance and market size have increased. In this study, image data for deep learning was collected using a mavic3 drone capturing images at a shooting altitude was 20 m with ×7 magnification. Swin Transformer and UperNet were employed as the backbone and architecture of the deep learning model. About 800 sheets of labeled data were augmented to increase the amount of data. The learning process encompassed three rounds. The Cross-Entropy loss function was used in the first and second learning; the Tversky loss function was used in the third learning. In the future, when the crack detection model is advanced through convergence with the Internet of Things (IoT) through additional research, it will be possible to detect patching or potholes. In addition, it is expected that real-time detection tasks of drones can quickly secure the detection of pavement maintenance sections.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1723-1735
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• 2022

Landslides are one of the most prevalent natural disasters, threating both humans and property. Also landslides can cause damage at the national level, so effective prediction and prevention are essential. Research to produce a landslide susceptibility map with high accuracy is steadily being conducted, and various models have been applied to landslide susceptibility analysis. Pixel-based machine learning models such as frequency ratio models, logistic regression models, ensembles models, and Artificial Neural Networks have been mainly applied. Recent studies have shown that the kernel-based convolutional neural network (CNN) technique is effective and that the spatial characteristics of input data have a significant effect on the accuracy of landslide susceptibility mapping. For this reason, the purpose of this study is to analyze landslide vulnerability using a pixel-based deep neural network model and a patch-based convolutional neural network model. The research area was set up in Gangwon-do, including Inje, Gangneung, and Pyeongchang, where landslides occurred frequently and damaged. Landslide-related factors include slope, curvature, stream power index (SPI), topographic wetness index (TWI), topographic position index (TPI), timber diameter, timber age, lithology, land use, soil depth, soil parent material, lineament density, fault density, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used. Landslide-related factors were built into a spatial database through data preprocessing, and landslide susceptibility map was predicted using deep neural network (DNN) and CNN models. The model and landslide susceptibility map were verified through average precision (AP) and root mean square errors (RMSE), and as a result of the verification, the patch-based CNN model showed 3.4% improved performance compared to the pixel-based DNN model. The results of this study can be used to predict landslides and are expected to serve as a scientific basis for establishing land use policies and landslide management policies.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2010.06a
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• pp.149-152
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• 2010

본 논문은 지구공간에 존재하는 다양한 공간정보를 이용하여 도로 및 철도 계획과 공사를 위한 최적노선을 선정하는 기법에 관한 새로운 연구이다. 사람과 물자를 수송하는데 있어서 가장 기본적인 공공교통시설인 도로 및 철도를 건설하기 위하여 초기에 가장 중요한 결정이 바로 최적노선결정이므로 환경파괴를 최대한으로 감소시키고 그 기능을 충분히 발휘할 수 있도록 대상 지역의 여러 가지 조건을 고려하여 가장 적합한 노선의 위치를 결정하여야 한다. 3차원 지형 환경의 공간영상콘텐츠는 국토계획 및 통신설비계획, 철도건설, 시공, 입체적인 유비쿼터스 도시 구현, 안전 및 방재 등에서 많은 요구와 그 중요성이 크게 부각되고 있다. 현재 지리정보 기반의 2차원적인 지도정보와 시설정보를 다차원의 도시공간으로 재현하기 위하여 기존의 등고선을 이용한 DEM 방식은 많은 한계를 가지고 있으며, 특히, 철도와 같은 노선 폭이 좁고 길이가 길어서 궤적 관리가 어려운 작은 구조물의 경우에는 그 고도모델이 무시되기 쉬우므로, 레이저 측량 기술을 이용한 공간대상물에 대한 높은 정확도 취득이 크게 필요한 실정이다. 본 연구에서는 원격탐사 영상 Data를 중심으로 하는 정사보정하고 이에 매칭 할 수 있는 수치 지도 벡터와의 통합 및 전환으로 다차원 공간에서 건물 모델의 생성과 다양한 활용을 제시하는 것을 연구목적으로 하였고, 연구방법으로는 기존의 이미 취득한 2차원적인 평면사진을 지상 기준점에 의하여 정밀기하보정을 하여 얻은 사진영상자료를 이용하여 3차원 공간정보로 구성하기 위해서는 동일지역에 대한 수준 측량결과인 높이 데이터를 매칭하여야 하므로, 항공기에 탑재한 센서로 모든 대상지에 대한 지형지물의 고밀도의 높이 값을 획득하여 위치보정 작업 후에 3D로 매칭할 수는 방법을 연구하여 실험하도록 하였다 또한 본 연구에서는 연구대상지역의 지형조건, 기존 노선과의 비교, 토지이용, 지형경사, 사면방향, 지가 등을 분석하여 각각의 경중률을 고려한 후 선택된 후보노선들을 비교분석함으로서 최적노선을 선정하고자 하였다.

• Land and Housing Review
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• v.2 no.4
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• pp.429-437
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• 2011

The purpose of this research is to seek the suggestions applicable to Korean green-growth(development) policy and the realization of low carbon society by looking around the promotion policy and the process, the promotion system, main environmental policy in each city about the business for environmental model city in Japan which has been promoted in a city in order to realize low-carbon society. Japan had selected 13 local governments as an environmental model city as a part of a policy to build low-carbon society in 2008~2009, and Japan has formed information sharing between cities and provinces, the spread of information sharing and the free competition among local governments for an environmental model city through Zero Carbon City Promotion Council consisting of local governments and specialists. When examining these cases in Japan, the green-growth policy promoting currently in Korea needs to be converted from the central government-dominated policy to the local government-dominated policy and Koreaneeds to make more effort to develop software programs in order to realize green-growth social system.

• Journal of Korean Society for Geospatial Information Science
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• v.13 no.1 s.31
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• pp.63-69
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• 2005

Interferometric Synthetic Aperture Radar(InSAR) is a rapidly evolving technique. Spectacular results obtained in various fields such as the monitoring of earthquakes, volcanoes, land subsidence and glacier dynamics, as well as in the construction of Digital Elevation Models(DEMs) of the Earth's surface and the classification of different land types have demonstrated its strength. As InSAR is a remote sensing technique, it has various sources of errors due to the satellite positions and attitude, atmosphere, and others. Therefore, it is important to validate its accuracy, especially for the DEM derived from Satellite SAR images. In this study, Real Time Kinematic(RTK) GPS and Kinematic GPS positioning were chosen as tools for the validation of InSAR derived DEM. The results showed that Kinematic GPS positioning had greater coverage of test area in terms of the number of measurements than RTK GPS. But tracking the satellites near and/or under trees md transmitting data between reference and rover receivers are still pending tasks in GPS techniques.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1567-1577
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• 2020

Urban land cover classification is role in urban planning and management. So, it's important to improve classification accuracy on urban location. In this paper, machine learning model, Support Vector Machine (SVM) and Artificial Neural Network (ANN) are proposed for urban land cover classification based on high resolution satellite imagery (KOMPSAT-3A). Satellite image was trained based on 25 m rectangle grid to create training data, and training models used for classifying test area. During the validation process, we presented confusion matrix for each result with 250 Ground Truth Points (GTP). Of the four SVM kernels and the two activation functions ANN, the SVM Polynomial kernel model had the highest accuracy of 86%. In the process of comparing the SVM and ANN using GTP, the SVM model was more effective than the ANN model for KOMPSAT-3A classification. Among the four classes (building, road, vegetation, and bare-soil), building class showed the lowest classification accuracy due to the shadow caused by the high rise building.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.3
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• pp.136-145
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• 2006

SWAT includes a lot of parameters related with geography, hydrological time series, land management and water pollution, etc. So, it needs many spatial, non-spatial and time series data to run SWAT. If SWAT is operated in conjunction with GIS, we can use database which includes model input data and do all the processes which covers data creation, model input and analysis of simulation results in a system. The objective of this study is to develop HyGIS-SWAT which is the interface system to couple the SWAT model and HyGIS. To achieve this object, system operation process based on HyGIS-SWAT data model is evaluated and databases are designed and established. As a result, HyGIS-SWAT prototype system is developed. HyGIS data model and HyGIS-Model operation process can be applied effectively to the development of HyGIS-SWAT. The technologies from this study can be used as base technology to develop another HyGIS application which connect HyGIS with models.

• Spatial Information Research
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• v.5 no.2
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• pp.185-194
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• 1997

Spatial decision support systems(SDffi), a new class of decision support system(DSS), result from the melding together of GIS and DSS, Planning support systems(PS5) add more advanced spatial analysis functions than GIS and intertemporal functions to the functions of SDSS. This paper reports the development of a planning support system providing a framework that facilitates urban planners and civil engineers in conducting coherent deliberations about the generation of satisficing sewers. 1he planning support system for the generation of satisficing sewers(PS5/GSS) was designed from the understanding that land use and development drive the demand for storm and sanitary sewers. Through four stages of supply, demand, alternative generation, and evaluation, PSS/GSS integrates basic planning, preliminary design, and engineering design of sewer. GIS and graphic user interface are excellent toolboxes for designing sewer networks, estimating the quantity of wastewater, and showing generated alternative sewers. A sewer model using simulation tedmique can generate an initial sewer. Users can define alternative sewers by the direct manipulation of sewer networks or by the manipulation of parameters in the sewer model. The sewer model evaluates the performance of the user defined alternatives.