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딥러닝 모델 기반 스케일업 LULUCF 매트릭스 구축에 관한 연구 = Construction of Scale-up LULUCF matrix based on Deep-learning model

박정묵 2019년

논문상세정보
인용/피인용
' 딥러닝 모델 기반 스케일업 LULUCF 매트릭스 구축에 관한 연구 = Construction of Scale-up LULUCF matrix based on Deep-learning model' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • 임학, 입업
  • gis
  • lulucf
  • 국가산림자원조사
  • 국가온실가스인벤토리
  • 기후변화
  • 딥 러닝
  • 매트릭스
  • 빅데이터
  • 원격탐사
  • 토지이용 변화
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
6,858 0

0.0%

' 딥러닝 모델 기반 스케일업 LULUCF 매트릭스 구축에 관한 연구 = Construction of Scale-up LULUCF matrix based on Deep-learning model' 의 참고문헌

  • 텐서플로로 배우는 딥러닝
    솔라리스 영진닷컴. p. 145-148 [2018]
  • 지적통계연보
    국토교통부 [2018]
  • 제6차 국가산림자원조사 및 산림의 건강 활력도 현지조사 지침 서 ver 1.3
    국립산림과학원. p. 1-10 [2016]
  • 제6차 국가산림자원조사 및 산림의 건강 활력도 조사 현지조사 지침서
    국립산림과학원 [2011]
  • 온실가스종합정보센터
    http://www.gir.go.kr/home/index.do?menuId=23 [2018]
  • 알기쉬운 부동산상식
    노원구청 노원구청 [2009]
  • 시 도별 토지 수급 전망과 토지이용계획 평가
    한국농촌경 제연구원 농어촌연구원 [2009]
  • 서해안 간척사업 현황분석 및 활용방안
    경기개발연구원 p. 4-11 [2007]
  • 산지전용허가제도의 개선에 관한 연구
    김명엽 한국토지공법학회지. 52: 71-92 [2011]
  • 산림부문의 국가온실가스 배출 흡수계수 개발 필요 우선순위 및 정량평가 방법론
    국립산림과학원 [2013]
  • 산림공간정보서비스
    산림청. . http://www.forest.go.kr/newkfsweb/html/HtmlPage.do?pg=/fgis/UI_KFS_5003_010300 .html&mn=KFS_02_04_03_04_08&orgId=fgis [2014]
  • 딥러닝 프레임워크의 비교: 티아노, 텐서플로, CNTK를 중심으로. 한국지능정보시스템학회
    안성만 양지헌 이재준 정여진 지능정보연구 23: 1-17 [2017]
  • 농업면적조사 통계정보 보고서
    통계청 [2015]
  • 국토연구원. 토지의 공급확대 및 효율적 이용을 위한 정책방향. 국토연구원
    세 종 [1998]
  • 국내외 임상도 현황분석 및 발전방안
    국립산림과학원 [2015]
  • 국가산림자원조사 고정표본점 자료를 이용한 토지이용변화 평가
    김래현 손영모 이선정 임종수 기후변화학회지 6: 33-40 [2015]
  • 국가기록원
    2018. http://theme.archives.go.kr/next/forest/viewMain.do [2018]
  • 국가 온실가스 인벤토리 보고서
    온실가스종합정보센터. p. 262 [2017]
  • 국가 온실가스 인벤토리 LULUCF 부문 통계 구축방안 에 관한 연구
    안종욱 옥진아 유선철 한국공간정보학회지 23: 67-77 [2015]
  • 국가 온실가스 감축목표에의 국내외 산림탄소 활용방안
    산림청. p. 222 [2016]
  • 교토의정서의 토지이용 및 산림(LULUCF)부문 온실가스 인벤 토리 작성을 위한 IPCC 2013 지침
    국립산림과학원 [2017]
  • 고해상도 위성영상의 토지피복분류와 정확도 비교 연구
    김형석 박소영 오치영 이양원 최철웅 한국지리정보학회지 13: 89-100 [2010]
  • 고해상도 위성영상을 이용한 정밀 주제 정보 추출
    유영걸 유지호 이현직 한 국지형공간정보학회지 18: 73-81 [2010]
  • 林野庁. 2016. 新規植林・再植林・森林減少の画像判読の指針. p. 15-26.
  • 「2006 IPCC 가이드라인」도입에 따른 개선방안 연구 – AFOLU 중 LULUCF 부문을 중심으로-
    국립산림과학원 [2013]
  • Watson RT, NobleI IR, Bolin B.2000. Land use, land-use change, and forestry, Special Report of the IPCC, Cambridge University Press, 377, Cambridge, UK.
  • Traore BB, Kamsu-Foguema B, Tangara F.2018. Deep convolution neural network for image recognition. Ecological Informatics 48: 257-268.
  • Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z.2016. Rethinking the Inception Architecture for Computer Vision. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. p. 2818-2826.
  • Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A.2014. Going Deeper with Convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  • Sharma N, Jain V, Mishra A. 2018. An Analysis Of Convolutional Neural Networks For Image Classification. Procedia Computer Science 132: 377-384.
  • Sebastian Aleksandrowicz, Konrad Turlej, Stanisław Lewiński, Zbigniew Bochenek. 2014. Change Detection Algorithm for the Production of Land Cover Change Maps over the European Union Countries. Remote Sensing 6: 5976-5994.
  • Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85-117.
  • Schmidhuber J.2014. Deep Learning in Neural Networks: An Overview. http://arxiv.org/abs/1404.7828
  • Sainath T et al.2013. Convolutional neural networks for LVCSR, ICASSP, 2013.
  • SEPA(Sweden Environmental Protection Agency).2017. National Inventory report Sweden 2017. Naturv rdsverket. p. 338-375.
  • Rouhi R, Jafari M, Kasaei S, Keshavarzian P. 2015. Benign and malignant breast tumors classification based on region growing and CNN segmentation. Expert Systems with Applications 42: 990-1002.
  • OSF(Official Statistics Finland).2018. Green- house Gas Emissions in Finland 1990 to 2016. OSF.
  • OSF(Official Statistics Finland).2017. Greenhouse Gas Emissions in Finland 1990 to 2015.
  • Myint SW, Gober P, Brazel A, Grossman-Clarke S, Weng Q.2011. Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sens. Environ. 115: 1145–1161.
  • Mitri GH, Karam J.2015. Mapping Greenhaouse Gas Emissions and Removals from the Land Use, Land Use Change, and Forestry Sector at the Local Level.
  • Mikolov T et al.2010. Recurrent neural network based language model, Interspeech, 2010.
  • MLIT(Japan Ministry of Land, Infrastructure, Transport and Tourism).2016. Land Use Status Survey.
  • MENZ(New Zealand Ministry for the Environment).2018. New Zealand’s Greenhouse Gas Inventory 1990-2016. MFE.
  • MENZ(New Zealand Ministry for the Environment).2017. New Zealand’s Greenhouse Gas Inventory 1990-2015.
  • MENZ(New Zealand Ministry for the Environment).2014. Accuracy assessment of LUCAS 2012 land use map.
  • MENZ(New Zealand Ministry for the Environment).2012. Land use and Carbon Analysis System: Satellite Imagery Interpretation Guide for Land Use Classes.
  • MENZ(New Zealand Ministry for the Environment).2006. New Zealand’s Initial Report under The Kyoto Protocol.
  • MEJ(Ministry of the Environment Japan).2017. National Greenhouse Gas Inventory Report of JAPAN. Center for Global Envi- ronmental Research.
  • MEJ(Japan Ministry of the Environment).2018. National greenhouse gas inventory report of Japan. MEJ.
  • MAFF(Japan Ministry of Agriculture, Forestry and Fisheries).2016. Statistics of Cultivated and Planted Area.
  • MAFF(Japan Ministry of Agriculture, Forestry and Fisheries).2000. World Census of agriculture and Forestry.
  • Lund HG.1982. Point sampling-the role in in-place resource inventories. Society of American Foresters. P. 79-84.
  • Li X, Shao G.2013. Object-based urban vegetation mapping with high-resolution aerial photography as a single data source. Int. J. Remote Sens. 34: 771–789.
  • LeCun Y, Bottou L, Bengio Y, Haffner P.1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11): 2278-2324.
  • LeCun Y, Boser B, Denker JS , Henderson D, Howard RE, Hubbard W, Jackel LD.1989. Backpropagation Applied to Handwritten Zip Code Recognition. Neural Computation 1: 541-551.
  • LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature. 521. p. 436-444.
  • LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521: 436–444.
  • Krug HAJ.2018. Accounting of GHG emissions and removals from forest management: a long road from Kyoto to Paris. Carbon Balance and Management 13: 1-11.
  • Krizhevsky A, Sutskever I, Hinton GE.2012. ImageNet classification with deep convolutional neural networks. In Proceedings of the Neural Information Processing Systems (NIPS) Conference, La Jolla, CA, USA, 3–8 December 2012.
  • Krizhevsky A, Sutskever I, Hinton GE.2012. ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems 25, Curran Associates, Inc (2012). pp. 1097-1105.
  • Ke Y, Quackenbush L J, Im J.2010. Synergistic use of QuickBird multispectral imagery and LIDAR data for object-based forest species classification. Remote Sens. Environ. 114: 1141–1154.
  • KOMPSAT-3급 위성영상을 이용한 농 업 토지이용 및 작물 생육정보 추출
    김성준 박종화 박진기 신형섭 이미선 대한원격탐사학회 25: 411-421 [2009]
  • IPCC(Intergovernmental Panel on Climate Change).2006. 2006 lPCC Guidelines for Nationa l Greenhouse Gas Inventories. Institute for Global Environmental Strategies (IGES).
  • IPCC(Intergovernmental Panel on Climate Change).2003. Good Practice Guidance for Land Use, Land-Use Change and Forestry. Institute for Global Environmental Strategies (IGES).
  • IPCC(Intergovernmental Panel on Climate Change).2000. IPCC Special Reprot: Land Use, Land-Use Change, and Forestry. IPCC.
  • IPCC(Intergovernmental Panel on Climate Change).1997. Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories. Institute for Global Environmental Strategies (IGES).
  • Hussain E, Shan J.2016. Object-based urban land cover classification using rule inheritance over very high-resolution multisensor and multitemporal data. GISci. Remote Sens 53: 164–182.
  • Huang MH, Rust RT.2018. Artificial Intelligence in Service. Journal of Service Research 21: 155-172.
  • Houghton RA, Hackler JL. 2003. Sources and sinks of carbon from land-use change in China, Global Biogeochemical Cycles, 17, 1034.
  • Hinton G, Osindero S, Welling M, Teh YW.2006. Unsupervised Discovery of Nonlinear Structure Using Contrastive Backpropagation. Science 30: 725–732.
  • Hao M, Shi W, Deng K, Zhang H, He P.2016. An Object-Based Change Detection Approach Using Uncertainty Analysis for VHR Images. Journal of Sensors 2016: 1-17.
  • Glorot X, Bordes A, Bengio Y.2011. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics. PMLR 15:315-323.
  • Glorot X, Bordes A, Bengio Y.2011. Deep Sparse Rectifier Neural Networks. Proceedings of Machine Learning Research(PMLR) 15: 315-323.
  • GSI(Japan Geographical Survey Institute).2016. Statistical Reports on the Land Area by Prefectures and Municipalities in Japan.
  • Fu G, Zhao H, Li C, Shi L.2013. Segmentation for High-Resolution Optical Remote Sensing Imagery Using Improved Quadtree and Region Adjacency Graph Technique. Remote Sens 5: 3259–3279.
  • FEA(Germany Federal Environment Agency).2018. National Inventory Report for the German Greenhouse Gas Inventory 1990–2016. FEA.
  • FEA(Federal Environment Agency of Germany).2016. National Inventory Report of Germany.
  • FAO(Food and Agriculture Organization of the United Nations).2005. Global Forest Resource Assessment 2005.
  • Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. 2017. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542: 115-118.
  • Costanza R, Groot RD, Braat L, Kubiszewski I, Fioramonti L, Sutton P, Farber S, Grasso M.2017. Twenty years of ecosystem services: how far have we come and how far do we still need to go?. Ecosystem Services 28: 1-16.
  • Cordts M, Omran M, Ramos S, Rehfeld T, Enzweiler M, Benenson R, Franke W, Roth S, Schiele B.2016. The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern Recognition (CVPR), Las Vegas, NV, USA, 27–30 June 2016; p. 3213– 3223.
  • Convolutional Neural Network와 Tensorflow를 활용한 차량 모델 판별
    김병만 신동 2016년 한국컴퓨터종합학술대회 논문집 [2016]
  • Cochran WG.1977. Sampling techniques. 3rded. John Wiley & Sons.
  • Chung CF, Fabbri AG.2003. Validation of Spatial Prediction Models for Landslide Hazard Mapping. Natural Hazards 30: 451-472.
  • Bengio Y, Courville A, Vincent P.2013. Representation Learning: A Review and New Perspectives. IEEE Trans. PAMI, special issue Learning Deep Architectures.
  • Bengio Y, Courville A, Vincent P.2013. "Representation Learning: A Review and New Perspectives," IEEE Trans. PAMI, special issue Learning Deep Architectures, 2013.
  • Baatz M, Sch pe A.2000. Multiresolution Segmentation: An Optimization Approach for High Quality Multi-scale Image Segmentation. In Angewandte Geographische Information Sverarbeitung XII; Herbert Wichmann Verlag: Heidelberg, Germany, 2000; p. 12–23.
  • Al-Hamdan MZ, Oduor P, Flores AI, Kotikot SM, Mugo R, Ababu J, Farah H.2017. Evaluating land cover changes in Eastern and Southern Africa from 2000 to 2010 using validated Landsat and MODIS data. International Journal of Applied Earth Observation and Geoinformation 62: 8-26.
  • Achard F, Grassi G, Herold M, Teobaldelli M, Mollicone D. 2008. Use ofsatellite remote sensing in LULUCF sector. GOFC-GOLD Report 33.
  • 2030년 국가 온실가스 감축목표 달성을 위한 기본 로드멥 수정 안
    관계부처 합동 [2018]
  • .Calculation of GHGs Emission from LULUCF-Cropland Sector in South Korea
    Kim MS Kim YH Ko BG Lee CH Park SJ Yun SG 한국토양비료학회지 49: 826-831 [2016]