• Journal of Korean Society for Geospatial Information Science
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• v.19 no.1
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• pp.105-113
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• 2011

Since existing thematic maps have been made with medium- to low-resolution satellite images, they have several shortcomings including low positional accuracy and low precision of presented thematic information. Digital aerial photo image taken recently can express panchromatic and color bands as well as NIR (Near Infrared) bands which can be used in interpreting forest areas. High resolution images are also available, so it would be possible to conduct precision land cover classification. In this context, this paper implemented object-based land cover classification by using digital aerial photos with 0.12m GSD (Ground Sample Distance) resolution and IKONOS satellite images with 1m GSD resolution, both of which were taken on the same area, and also executed qualitative analysis with ortho images and existing land cover maps to check the possibility of object-based land cover classification using digital aerial photos and to present usability of digital aerial photos. Also, the accuracy of such classification was analyzed by generating TTA(Training and Test Area) masks and also analyzed their accuracy through comparison of classified areas using screen digitizing. The result showed that it was possible to make a land cover map with digital aerial photos, which allows more detailed classification compared to satellite images.

• Journal of the Korean Association of Geographic Information Studies
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• v.5 no.1
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• pp.27-37
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• 2002

The classification accuracy of land cover has been considered as one of the major issues to estimate pollution loads generated from diffuse landuse patterns in a watershed. This research aimed to assess the effects of the acquisition methods and sampling size of training reference data on the classification accuracy of land cover using an imagery acquired by optical sensor(OPS) on JERS-1. Two kinds of data acquisition methods were considered to prepare training data. The first was to assign a certain land cover type to a specific pixel based on the researchers subjective discriminating capacity about current land use and the second was attributed to an aerial photograph incorporated with digital maps with GIS. Three different sizes of samples, 0.3%, 0.5%, and 1.0% of all pixels, were applied to examine the consistency of the classified land cover with the training data of corresponding pixels. Maximum likelihood scheme was applied to classify the land use patterns of JERS-1 imagery. Classification run applying an aerial photograph achieved 18 % higher consistency with the training data than the run applying the researchers subjective discriminating capacity. Regarding the sample size, it was proposed that the size of training area should be selected at least over 1% of all of the pixels in the study area in order to obtain the accuracy with 95% for JERS-1 satellite imagery on a typical small-to-medium-size urbanized area.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.409-418
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• 2021

The land cover map is a very important data that is used as a basis for decision-making for land policy and environmental policy. The land cover map is mapped using remote sensing data, and the classification results may vary depending on the acquisition time of the data used even for the same area. In this study, to overcome the classification accuracy limit of single-period data, multi-series satellite images were used to learn the difference in the spectral reflectance characteristics of the land surface according to seasons on a U-Net model, one of the deep learning algorithms, to improve classification accuracy. In addition, the degree of improvement in classification accuracy is compared by comparing the accuracy of single-period data. Seoul, which consists of various land covers including 30% of green space and the Han River within the area, was set as the research target and quarterly Sentinel-2 satellite images for 2020 were aquired. The U-Net model was trained using the sub-class land cover map mapped by the Korean Ministry of Environment. As a result of learning and classifying the model into single-period, double-series, triple-series, and quadruple-series through the learned U-Net model, it showed an accuracy of 81%, 82% and 79%, which exceeds the standard for securing land cover classification accuracy of 75%, except for a single-period. Through this, it was confirmed that classification accuracy can be improved through multi-series classification.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.1047-1050
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• 2011

U-City 건설은 도시의 과밀화 및 자원부족, 경제 사회적 급격한 변화 등으로 발생하게 되는 각종 도시문제들을 첨단정보통신기술을 활용하여 저비용, 고효율적인 해결방안으로 대두되고 있다. 그러나 신도시를 중심으로 유시티 건설을 추진 중인 많은 지방자치단체에서 무분별한 서비스 개발로 인해 유지보수 및 운영에 따른 어려움을 겪고 있고 사업시행자에게도 개발 비용부담을 가중시키고 있다. 이에 따라 본 연구에서는 유시티 건설 사업에서 제기되고 있는 유시티 서비스 구축 범위에 대하여 여러 분류 및 적용 방법들을 제안하여 합리적인 유시티 서비스 건설기준 수립 방안을 제시하였다.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1771-1777
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• 2023

With the advancements of deep learning, many semantic segmentation-based methods for land cover classification have been proposed. However, existing deep learning-based models only use image information and cannot guarantee spatiotemporal consistency. In this study, we propose a land cover classification model using geographical coordinates. First, the coordinate features are extracted through the Coordinate Hash Encoder, which is an extension of the Multi-resolution Hash Encoder, an implicit neural representation technique, to the longitude-latitude coordinate system. Next, we propose an architecture that combines the extracted coordinate features with different levels of U-net decoder. Experimental results show that the proposed method improves the mean intersection over union by about 32% and improves the spatiotemporal consistency.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.5
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• pp.451-457
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• 2014

Recently, the land degradation and pasture erosion in Tuv, located around Ulaanbaatar of Mongolia, have been increasing sharply due to escalating developments of mining sectors, well as the density of populations. Because of that, we have chosen the urban and mining area of Tuv for our study target. During the study, the temporal changes of land cover in Tuv, Mongolia were observed by the Landsat TM/+ETM satellite images from 2001 to 2009 that provided the fundamental dataset to apply NDVI and K-Mean algorithm of Unsupervised Classification and Maximum likelihood classification(MLC) of Supervised Classification in order to conclude in land cover change analyzation. The result of our study implies that the growth of mining area, the climate change, and the density of population led the land degradation to desertification.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.1251-1256
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• 2004

본 연구는 토양도 및 Landsat 위성영상을 GIS 및 R/S기법으로 토지이용의 공간적 분포를 분석하여 토지피복의 경년적 변화에 따른 유출곡선지수를 산정하고 유출곡선지수의 변화에 따른 유출상태의 변화를 분석하는데 그 목적이 있다. 이른 위하여 개략토양도의 토양분류에 대한 기존분류방법과 토지이용에 따른 CN분류법을 조사 검토한 후 CN을 산정하였으며, 향후 보다 정확한 산정기법이 제시될 수 있을 것으로 판단된다. 본 연구를 위하여 활용한 기초자료는 건설교통부와 한국수자원공사에서 시행하고 있는 전국유역조사의 자료로서 연구 분석에 이용하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.407-408
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• 2019

토지 피복과 관련된 지표면 파라미터는 일반적으로 지표에서 감지되어 위성영상에 나타난 많은 물리적 프로세스에 의존하며 계절적 주기성을 갖는 시간적 변화를 보인다. 하모닉 모형은 복잡한 파형을 정현파 성분의 합으로 표시함으로써 레벨, 주기, 진폭 및 위상 요소를 통한 변동을 분석함으로써 표면에서 관찰되는 계절적 변화 패턴을 모델링하는 데 적합한 모형이다. 본 연구에서는 MODIS NDVI (Normalized Difference Vegetation Index) 시계열 자료를 이용하여 하모닉 패턴의 특성에 따라 토지 피복을 분류하는 방법론을 제안하였다.

• Spatial Information Research
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• v.23 no.1
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• pp.33-40
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• 2015

Recently, land pollution makes various environment problems according to existing land use. So, there is an urgent need for management about these problems. This study categorize land pollution area using the land category information according to main land usage for reasonable analysis of land pollution area by point and non-point pollution sources. And also there was able to collect land pollution sources information efficiently by analysing the land category information. The land use information that categorized important factor for management and land pollution survey will be utilized Soil environment management and preservation. And land use information will be used land use regulation, resonable preservation and management.

• Korean Journal of Environment and Ecology
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• v.29 no.3
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• pp.454-461
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• 2015

This study aims to classify the biotope types based on the vegetation community in built-up areas by different land use and to map the plant communities. By classifying biotopes according to a taxonomic system, the characteristics of a biological community can be well-represented. The biotope classification indexes for the target area include human behavioral factors such as land use intensity, land-use patterns and land-cover types. The type classification was divided into four hierarchic ranks starting with Biotope Class, next by Biotope Group and Biotope Type and lastly by Biotope Sub-Type. The Biotope Class was first divided into two areas: the areas improved by humans and the areas unimproved by humans. The improved areas were again divided into permeable and non-permeable regions on the Biotope Group level. In the Biotope Type level, permeable paving areas were divided into areas with wide gap pavers and those with narrow gap pavers. The differential species of each biotope type are Lindera glauca, Conyza canadensis, Mazus pumilus, Vicia tetrasperma, Crepidiastrum sonchifolium, Zoysis japonica, Potentilla supina and Festuca arundinacea. The results of this study suggest that the biotope classification methodology, using a subjective phytosociological approach, is a useful and valuable tool and the results also suggest the possibility of applying more objective and scientific methods in mapping and classifying various environments.