• Spatial Information Research
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• v.2 no.1
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• pp.85-92
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• 1994

This study examines the unique characteristics of the parcel identification number especially in developing the Korean land information system(KLIS). There are several limitations in establishing KLIS in terms of the location identification. Therefore, it is necessary to develop the appropriate parcel identification number system in Korea including the road identification systym and the three dimensional parcel identification system. The exact parcel bounary also needs accurate surveying and registration.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1D
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• pp.153-160
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• 2009

In relation to digital elevation model(DEM) production using high resolution satellite imagery, existing studies present that DEM accuracy differently show according to land cover property. This study therefore proposes auto-selection method of window size for correlation matching according to land cover property of IKONOS Geo-level stereo image. For this, land cover classified image is obtained by IKONOS color image with four bands. In addition, correlation-coefficients are computed at regular intervals in pixels of the window-search area to shorten of matching time. As the results, DEM by the proposed method showed more accurate than DEM using the fixed window-size matching. We estimate that accuracy of the proposed DEM improved more than DEM by digital map and ERDAS in agricultural land.

• Journal of Korean Society of Transportation
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• v.28 no.3
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• pp.19-28
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• 2010

Almost domestic cycleways have been established without characteristic of land uses. These cycleways can always not provide optimal condition for safety and convenience not to speak of efficiency. This research having a purpose to accomplish more safety and convenience has tried to classify cycleways detail and to analyze cycleways types by land uses. It verified the difference among the characteristic of traffic on the land uses using the Chi-square test, and found the land use that had the strongest characteristic. Finally, it has proposed the suitable cycleway types to land uses.

• Journal of Korean Society for Geospatial Information Science
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• v.1 no.1 s.1
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• pp.153-158
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• 1993

The direction of land use for the subject district (Zuksan-myun, Anseong-gun, Kyeonggi-do) were analyzed as the basic model of new rural land management system connected to production and living. General land use planning was presented by land suitability classification which was applied to geographical information system(software ARC/INFO). The course of analysis of land suitability using the geographical information system were generalized and the results of analysis for paddy and upland fields and settlement were presented as 5 criteria of the suitability rank. It was found out that the analysis of land suitability is able to use as primary data of rural land use planning.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.266-266
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• 2012

오늘날 도시는 인간 중심적으로 환경을 고려하지 않은 채 개발위주의 발전이 계속되고 있으며 도시화가 진행됨에 따라 도심지에 콘크리트와 아스팔트의 비율이 증가하고 농경지와 녹지가 감소하는 등 도시주변의 피복 변화가 빠르게 일어나고 있다. 이와 같이 열 환경을 고려하지 않은 도시의 급성장은 각종 인공 열과 대기오염 물질의 증가와 함께 도시상공의 기온을 상승시켜 도시의 기온이 주변 지역보다 높아지는 열섬현상의 원인이 되고 있다. 이에 따라 최근 미국, 유럽 등의 선진국을 비롯하여 아시아지역에서도 도시열섬현상 대책수립이 활발히 진행되어오고 있으며 국내에서도 열섬완화 대책 수립의 중요성이 인식되고 있다. 그러나 자연토지피복은 농경지, 나지, 수면과 같은 다양한 종류의 피복으로 구성되며 각 피복특성에 따라 도시내부에 영향을 미치는 정도가 다르게 나타나므로 도시계획에 반영될 수 있는 자연피복의 종류와 피복비율의 적정조합을 도출할 필요가 있다. 따라서 본 연구에서는 청주시를 대상으로 농경지를 포함하는 녹지의 감소, 공업단지의 증가에 따른 열 환경 영향을 분석하기 위하여 청주시의 연도별 토지피복 분포 현황과 과거 기상자료를 통해 토지피복 분포변화에 따른 온도 변화를 조사하였다. 토지피복은 농경지, 녹지, 주거지, 공업 단지 및 수역으로 분류하였으며, 대상지역의 기후 특성 파악을 위하여 청주시 총 면적에 대한 면적비율의 증감추이와 그에 따른 온도 변화를 정량적으로 파악하였다. 그 결과, 청주시는 녹지의 경우 정체 또는 감소추세를 나타낸 반면 공업단지는 지속적인 증가추세를 나타내었다.

• Proceedings of the Korea Contents Association Conference
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• 2008.05a
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• pp.897-901
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• 2008

The objective of this study is to conduct land cover classification respectively using Landsat TM data collected on Oct., 1985 and KOMPSAT-1 EOC data collected on Jan., 2000 covering Gumi city, Gyeongbuk Province and to detect urban change by comparing between both land cover maps. Multispectral images of Landsat TM have spatial resolution of 30m are well known as useful data for extracting information related to landcover, vegetation classification, urban growth analysis and so forth. In contrast, as KOMPSAT-1 EOC collects panchromatic images with relatively high spatial resolution of 6.6m. We try to analyze how accurate landcover classification result is able to be derived from the panchromatic images. As the results of the study, the KOMPSAT EOC data with high resolution greater than 4 times showed higher classification degree than Landsat TM data. It was ascertained that the built-up region was extended by three to four times in the last 15 years between 1985 and 2000. In the contrast, it was shown that the forest region was decreased by 15% to 27% and the grass region including agricultural region was decreased by 28% to 45%.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1053-1065
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• 2019

Land use statistics calculation is very informative data as the activity data for calculating exact carbon absorption and emission in post-2020. To effective interpretation by land use category, This study classify automatically image interpretation by land use category applying forest aerial photography (FAP) to deep learning model and calculate national unit statistics. Dataset (DS) applied deep learning is divided into training dataset (training DS) and test dataset (test DS) by extracting image of FAP based national forest resource inventory permanent sample plot location. Training DS give label to image by definition of land use category and learn and verify deep learning model. When verified deep learning model, training accuracy of model is highest at epoch 1,500 with about 89%. As a result of applying the trained deep learning model to test DS, interpretation classification accuracy of image label was about 90%. When the estimating area of classification by category using sampling method and compare to national statistics, consistency also very high, so it judged that it is enough to be used for activity data of national GHG (Greenhouse Gas) inventory report of LULUCF sector in the future.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1663-1676
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• 2022

Recently, a number of deep-learning based land cover segmentation studies have been introduced. Some studies denoted that the performance of land cover segmentation deteriorated due to insufficient training data. In this study, we verified the improvement of land cover segmentation performance through data augmentation. U-Net was implemented for the segmentation model. And 2020 satellite-derived landcover dataset was utilized for the study data. The pixel accuracies were 0.905 and 0.923 for U-Net trained by original and augmented data respectively. And the mean F1 scores of those models were 0.720 and 0.775 respectively, indicating the better performance of data augmentation. In addition, F1 scores for building, road, paddy field, upland field, forest, and unclassified area class were 0.770, 0.568, 0.433, 0.455, 0.964, and 0.830 for the U-Net trained by original data. It is verified that data augmentation is effective in that the F1 scores of every class were improved to 0.838, 0.660, 0.791, 0.530, 0.969, and 0.860 respectively. Although, we applied data augmentation without considering class balances, we find that data augmentation can mitigate biased segmentation performance caused by data imbalance problems from the comparisons between the performances of two models. It is expected that this study would help to prove the importance and effectiveness of data augmentation in various image processing fields.

• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.2
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• pp.21-30
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• 2005

The primary purpose of this paper is to prove a clear relationship among land use type, vegetation level and surface temperature. For this purpose, this paper presents series of spatial distribution maps of the three features obtained through the visual interpretation of digital images. The result of study tells us that the spatial distribution of the vegetation level is very similar with that of surface temperature. By analyzing the relationship between surface temperature and land use types, this study categorizes the eighteen urban land uses into 7-8 groups according to their average surface temperature. The Duncan test was conducted to categorize the land uses. The surface temperature of manufacturing related land use is the highest, semi-residential use is the next, non-residential land use is the next to the lowest, and the agricultural and forest land use is the lowest. This paper provides another strong evidence of the relationship by showing the regression result.

• Journal of Environmental Impact Assessment
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• v.10 no.1
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• pp.39-47
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• 2001

본 연구는 지리정보시스템 및 원격탐사기법을 응용하여 북한의 자연환경을 조사하기 위한 기초연구로서 수행되었으며, 과거 약 20년 동안의 평양 및 남포지역의 산림면적의 변화 및 경관구조 변화측면에서의 산림 파편화 양상을 조사하였다. 조사자료로는 Landsat MSS 및 TM 영상의 NDVI값을 이용하였으며, 보다 정확한 피복분류를 위하여 변형된 Cluster-Busting 알고리즘을 활용하여 산림과 비산림지역으로 단순화시켜 분석하였다. 경관구조의 변화를 살피기 위해서 조각밀도, 형태 및 핵심내부지역의 면적 등의 경관지수(Landscape Indices)를 활용하였다. 분석과정을 거쳐서 도출된 결론은 다음과 같다. 첫째, Cluster-busting 방법을 활용한 토지피복 분류결과 87.3%의 총 분류 정확도를 얻었으며, Binary Map을 이용한 변화감지(Change Detection)기법 또한 그 결과가 정확한 것으로 판단되었다. 둘째, '79년에서 '98년에 이르는 기간동안, 평양의 경우 '79년 산림면적의 15%, 그리고 남포지역의 경우 14%가 감소하였다. 셋째, 경관지수를 이용하여 북한 산림의 파편화 변화를 조사한 결과 산림조각의 개수는 늘어나고 조각의 평균면적 및 핵심내부면적은 감소하였으며 조각크기의 다양성 또한 낮아졌다. 산림조각 형태지수 또한 매우 증가하였는데 이러한 결과들은 평양 및 남포지역의 산림조각이 파편화되고 그 형태 또한 불규칙적이며 복잡하게 변화하였음을 보여주고 있다.