• KIPS Transactions on Software and Data Engineering
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• v.11 no.8
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• pp.347-354
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• 2022

Since the era of the 4th industrial revolution has been ripe, the use of artificial intelligence(AI) based on massive data is beginning to be actively applied in various fields. However, as the process of analyzing forest species is carried out manually, many errors are occurring. Therefore, in this paper, about 60,000 pieces of AI learning data were automatically analyzed for pine, larch, conifer, and broadleaf trees of aerial photographs and pseudo images in the metropolitan area, and an AI model was developed to distinguish tree species. Through this, it is expected to increase in work efficiency by using the tree species division image as basic data when producing forest change detection and forest field topics.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1759-1768
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• 2022

This paper proposes a model to detect depression-related emotions in a user's speech using wellness dialogue scripts provided by AI Hub, topic-specific daily conversation datasets, and chatbot datasets published on Github. There are 18 emotions, including depression and lethargy, in depression-related emotions, and emotion classification tasks are performed using KoBERT and KOELECTRA models that show high performance in language models. For model-specific performance comparisons, we build diverse datasets and compare classification results while adjusting batch sizes and learning rates for models that perform well. Furthermore, a person performs a multi-classification task by selecting all labels whose output values are higher than a specific threshold as the correct answer, in order to reflect feeling multiple emotions at the same time. The model with the best performance derived through this process is called the Depression model, and the model is then used to classify depression-related emotions for user utterances.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2004.03a
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• pp.347-347
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• 2004

임상도는 항공사진을 판독하여 얻어진 산림에 관한 정보를 지형도(1/25,000)에 도화 작성한 도면으로 전국 산림조사와 연계하여 10년을 주기로 순환제작 되며, 현재 제 4차 수치임상도가 제작 중에 있다 임상도는 여러 산림관련 주제도 중 가장 많이 활용되는 도면으로 산림 분야뿐만 아니라 다른 분야에서도 널리 활용되고 있다. 그러나 10년을 주기로 제작되므로 부분적으로 현실과 부합하지 않는 내용이 포함되어 있어 각종 계획수립 및 활용에 장애요인으로 작용하고 있다. 따라서 실제 임상정보를 획득할 수 있도록 지속적인 갱신이 필요하다. 그러나 임상도의 부분적 갱신을 위하여 별도의 항공사진을 촬영하는 것은 현실적인 어려움이 있으며, 최근 고해상도 위성영상이 활용 가능하게 됨에 따라 임상도의 갱신에도 활용될 수 있을 것으로 기대되고 있다. 본 연구에서는 고해상도 위성영상인 IKONOS를 이용하여 수치임상도를 갱신하는 방법을 제시하였다. 연구대상지는 제 4차 임상도의 수치화가 완료된 전라북도 완주 지역으로 1:25000 지형도의 도엽명 대아와 읍내의 일부지역이다. 영상자료는 2001년 8월 18일에 촬영된 IKONOS Multispectral 자료를 이용하였다. 영상의 기하보정을 위하여 RPC Model과 1:25000 수치지형도로부터 만들어진 DEM을 사용하였다. 기하보정된 영상을 이용하여 영상분할(Segmentation)을 실시하여 서로 중복되지 않는 동질한 지역으로 구분하였다. 이때 기존의 수치임상도를 Super-Object로 사용하여 영상을 분할할 때 형성될 수 있는 가장 큰 Segment로 제한하였으며 Super-Object의 경계를 벗어나지 않는 보다 작은 Sub-Object를 만들도록 하여 분할영상의 계층적 구조를 형성하였다. 어느 한 임상내에서 변화가 발생하면 변화가 발생한 지역은 변화가 발생하지 않은 지역과 서로 다른 분광특성을 나타내므로 별도의 Segment를 형성하게 된다. 따라서 임상도의 경계선으로부터 획득된 Super-Object의 분광반사 값과 그 안에서 형성된 Sub-Object의 분광반사값의 차이를 이용하여 임상도의 갱신을 위한 변화지역을 탐지하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.5-5
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• 2023

제3차 우주개발 진흥 기본계획의 일환으로써 개발되는 차세대 중형위성 5호인 수자원위성은 수자원/수재해 감시 전용 위성으로 2025년 발사 예정이다. 수자원위성의 메인 센서인 C-band 영상레이더(Synthetic Aperture Radar, SAR)는 기상조건 및 주야 상관없이 지표면 관측이 가능한 센서로 급변하는 수재해 양상에 효과적으로 대응하기 위해 탑재된 센서이다. 본 연구사업은 차세대 수자원위성의 효과적 활용 방안 및 SAR 자료기반의 활용산출물 및 주제도 서비스를 위한 알고리즘 구조설계 및 표출시스템 시범개발을 목표로 하고 있으며, 홍수/가뭄/안전/환경모니터링을 주제로 수자원 및 원격탐사 분야의 다학제적 전문가들로 구성된 컨소시엄을 구성하여 추진하고 있다. 본 연구의 내용은 가뭄 모니터링을 위해 개발 중인 SAR 기반 토양수분과 농업적 가뭄지수 산정 알고리즘 개발 및 공간적 표출을 포함한다. 토양수분은 SAR 영상에서 지표피복별로 추출된 후방산란계수와 수문학적 개념의 융합을 통해 논/밭/산림에 대해 산정한다. 물리적 특성에 기반한 변화탐지모델을 활용해 토양수분량을 추출 후, 기계학습기법과 S C S - C N 방법에서 파생된 수문학적 개념 5일 선행강우량과 결합한 토양수분 산정 알고리즘을 개발하였다. 산정된 토양수분을 기반으로, 논 지역은 벼 재배에 따른 담수 시기를 고려한 토양의 포화/불포화상태, 밭 지역은 토양 종류에 따른 토양의 물리적 특성, 산림 지역은 수문학적 개념 및 식생지수를 활용하여 가뭄 판단 기준을 구축하고, 가뭄의 해갈 여부와 해갈되는 시점의 강우량을 산정 가능한 알고리즘을 개발하였다. 개발된 가뭄 모니터링 기법은 향후 고도화, 최적화 및 안정화를 통해 수자원위성의 핵심 활용기술로써 구현할 계획이다.

• Journal of Korea Water Resources Association
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• v.50 no.6
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• pp.429-440
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• 2017

A series of multispectral high-resolution Korean Multi-Purpose Satellite (KOMPSAT) images were analyzed to detect the geographical changes in four different tidal flats in the west coast of South Korea. The method of unsupervised classification was used to generate a series of land use/land cover (LULC) maps from the satellite images, which were used as the input of the temporal trajectory analysis to detect the temporal change of coastal wetlands and its association with natural and anthropogenic activities. The accurately classified LULC maps extracted from the KOMPSAT images indicate that these multispectral high-resolution satellite data is highly applicable to generate good quality thematic maps for extracting wetlands. The result of the trajectory analysis showed that, while the tidal flat area of Gyeonggi and Jeollabuk provinces was estimated to have changed due to tidal effects, the reductive trajectory of the wetland areas belonging to the Saemangeum province was caused by a high degree of human-induced activities including large reclamation and urbanization. The conservation of the Jeungdo Wetland Protected Area in Jeollanam province revealed that the social and environmental policies can effectively protect coastal wetlands from degradation. Therefore, monitoring for wetland change using high resolution KOMPSAT is expected to be useful to coastal environment management and policy making.

• Spatial Information Research
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• v.19 no.5
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• pp.1-11
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• 2011

This study mainly focused on the method of accurately extracting damage information in the im agery change detection process using the constructed high resolution aerial im agery. Bongwha-gun in Gyungsangbuk-do which had been severely damaged from a localized torrential downpour at the end of July, 2008 was selected as study area. This study utilized aerial im agery having photographing scale of 30cm gray image of pre-disaster and 40cm color image of post-disaster. In order to correct errors from the differences of the image resolution of pre-/post-disaster and time series, the prelim inary phase of image processing techniques such as normalizing, contrast enhancement and equalizing were applied to reduce errors. The extent of the damage was calculated using one to one comparison of the intensity of each pixel of pre-/post-disaster im aged. In this step, threshold values which facilitate to extract the extent that damage investigator wants were applied by setting difference values of the intensity of pixel of pre-/post-disaster. The accuracy of optimal image processing and the result of threshold values were verified using the error matrix. The results of the study enabled the early exaction of the extents of the damages using the aerial imagery with identical characteristics. It was also possible to apply to various damage items for imagery change detection in case of utilizing multi-band im agery. Furthermore, more quantitative estimation of the dam ages would be possible with the use of numerous GIS layers such as land cover and cadastral maps.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.329-346
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• 2022

Recently, as the market for short form videos (Instagram, TikTok, YouTube) on social media has gradually increased, research using them is actively being conducted in the artificial intelligence field. A representative research field is Video to Shop, which detects fashion products in videos and searches for product images. In such a video-based artificial intelligence model, product features are extracted using convolution operations. However, due to the limitation of computational resources, extracting features using all the frames in the video is practically impossible. For this reason, existing studies have improved the model's performance by sampling only a part of the entire frame or developing a sampling method using the subject's characteristics. In the existing Video to Shop study, when sampling frames, some frames are randomly sampled or sampled at even intervals. However, this sampling method degrades the performance of the fashion product search model while sampling noise frames where the product does not exist. Therefore, this paper proposes a sampling method MF (Missing Fashion items on frame) sampler that removes noise frames and improves the performance of the search model. MF sampler has improved the problem of resource limitations by developing a keyframe mechanism. In addition, the performance of the search model is improved through noise frame removal using the noise detection model. As a result of the experiment, it was confirmed that the proposed method improves the model's performance and helps the model training to be effective.

• Journal of Korean Society of Forest Science
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• v.99 no.6
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• pp.827-835
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• 2010

Reliable forest statistics provides important information to meet the UNFCCC. In this respect, the national forest inventory has played a crucial role to provide the reliable forest statistics for several decades. However, the previous forest statistics calculated by administrative district has not provided spatial information in a small scale. Thus, this study focused on developing models to estimate an explicit spatial distribution of forest growing stock. For this, first, stand volume model by stand types was developed using National Forest Inventory(NFI) data. Second, forest type map was integrated with this model. NFI data were used to calculate plot-level stand volume and basal area. The stand types of NFI plot including the species composition, age class, DBH class and crown density class are very crucial data to be connected with forest type map. Finally, polygonlevel stand volume map was developed with spatial uncertainty map. Average stand volume was estimated at 85.7 $m^3$/ha in the study area, and at 95% significance interval it was ranged from 79.7 $m^3$/ha to 91.8 $m^3$/ha.

• Informatization Policy
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• v.25 no.4
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• pp.37-64
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• 2018

This study aims to forecast the future of the Korean society via a big data analysis. Based upon two sets of database - a collection of 46,000,000 news on 127 media in Naver Portal operated by Naver Corporation and a collection of 70,000 academic papers of social sciences registered in KCI (Korea Citation Index of National Research Foundation) between 2005-2017, 40 most frequently occurring keywords were selected. Next, their temporal variations were traced and compared in terms of number and pattern of frequencies. In addition, core issues of the future were identified through keyword network analysis. In the case of the media news database, such issues as economy, polity or technology turned out to be the top ranked ones. As to the academic paper database, however, top ranking issues are those of feeling, working or living. Referring to the system and life-world conceptual framework suggested by $J{\ddot{u}}rgen$ Habermas, public interest of the future inclines to the matter of 'system' while professional interest of the future leans to that of 'life-world.' Given the disparity of future interest, a 'mismatch paradigm' is proposed as an alternative to social forecasting, which can substitute the existing paradigms based on the ideas of deficiency or deprivation.

• Proceedings of the KSRS Conference
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• 2009.03a
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• pp.105-110
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• 2009

현재 NASA에서 제공되는 MODIS 지표반사율자료(MOD09)는 MODIS영상을 이용한 각종 주제자료들의 중요한 입력 자료로 사용되고 있으며, MODIS 지표반사율 자료에 대한 객관적인 검증연구가 필요한 실정이다. 따라서 본 연구에서는 MOD09의 검증관련 초기 연구로서, 남한에 분포하는 불변성 타겟(invariant target)을 대상으로 2006년 일별 250m MODIS 지표반사율자료(MOD09GQK)자료의 객관적 검증을 시도하였다. 우선, MOD09 QA(Quality Assurance)자료를 이용하여 구름의 영향을 받은 화소를 제거한 후, 수치지도와 토지피복도를 이용하여 정의한 불변성 타겟에 해당되는 MOD09영상의 화소값을 추출하였다. 이와 같이 추출된 시계열 MOD09GHK영상의 화소값에 1차 회귀분석을 적용하여 이상 반사율 값을 탐지하고, 그 원인을 분석하였다. 검증 결과 나지지역에 대해서 0.0186의 RMSE값이 나타났으며, 인공물의 경우 0.2891의 RMSE값을 보였다. 발생된 이상 화소를 살펴보면, 구름, 그림자, 눈에 영향에 의해 발생한 것도 있으며, 원인을 알 수 없는 이상 화소들도 분포하였다. 향후 연구에서는 한반도 전역의 MODIS 시계열 반사율영상을 대상으로 MODIS 대기보정알고리즘과 입력인자의 적합성을 판단하기 위한 연구를 진행할 예정이다.