한국지리정보학회지 (Journal of the Korean Association of Geographic Information Studies)

  • 제22권1호
  • /
  • Pages.62-77
  • /
  • 2019
  • /
  • 1226-9719(pISSN)
  • /
  • 2287-6952(eISSN)
한국지리정보학회 (The Korean Association of Geographic Information Studies)
권호 표지
DOI QR코드

DOI QR Code

다목적실용위성 자료의 오픈 데이터 큐브 적용을 위한 기본 고려사항

Consideration Points for application of KOMPSAT Data to Open Data Cube

  • 이기원 (한성대학교 전자정보공학과) ;
  • 김광섭 (한성대학교 전자정보공학과) ;
  • 이선구 (한국항공우주연구원 위성활용실) ;
  • 김용승 (한국항공우주연구원 위성활용실)
  • 투고 : 2019.02.13
  • 심사 : 2019.03.20
  • 발행 : 2019.03.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

지구관측위성 위원회(Committee on Earth Observation Satellites: CEOS)에서 주관하는 오픈 데이터 큐브(Open Data Cube: ODC)는 지구관측그룹(Group on Earth Observations: GEO)에서 구축하는 전 지구 관측시스템(Global Earth Observation System of Systems: GEOSS)의 기반 플랫폼으로 적용되고 발전하고 있다. ODC는 클라우드 컴퓨팅 환경을 기반으로 무상으로 공개되는 대용량의 위성영상정보를 이용하여 국가 규모, 지역 단위에서 사용자가 원하는 다양한 수준의 과학적 정보처리와 분석을 목적으로 하는 응용 서비스 구축에 적용할 수 있는 오픈소스 플랫폼이다. 이 연구에서는 ODC의 주요 특징에 대하여 유사한 목적을 갖는 구글 어스 엔진과 비교하여 설명하였다. 그리고 ODC에 대하여 소개하고 우리나라의 다목적실용위성(KOMPSAT) 영상정보를 이 플랫폼에 적용하는 데 필요한 기본 개념과 고려 사항을 제시하고자 한다. 또한, KOMPSAT 위성영상을 이 플랫폼에서 사용하기 위한 단계를 구분하여 설명하였고 실제 데이터를 이용하여 데이터의 입력과 등록에 적용되는 중간 과정을 예시하였다. 한편 오픈 데이터 사용권 관점에서 KOMPSAT 위성영상을 ODC 응용 서비스에서 적용할 수 있는 실제 방안을 제시하였다. KOMPSAT 위성영상정보의 ODC 적용을 위한 정책과 기술 사항들은 향후 GEO의 GEOSS에 다른 유상 위성정보를 사용하는 데 중요한 근거가 될 것으로 기대한다.

Open Data Cube(ODC) has been emerging and developing as the open source platform in the Committee on Earth Observation Satellites(CEOS) for the Global Earth Observation System of Systems(GEOSS) deployed by the Group on Earth Observations (GEO), ODC can be applied to the deployment of scalable and large amounts of free and open satellite images in a cloud computing environment, and ODC-based country or regional application services have been provided for public users on the high performance. This study first summarizes the status of ODC, and then presents concepts and some considering points for linking this platform with Korea Multi-Purpose Satellite (KOMPSAT) images. For the reference, the main contents of ODC with the Google Earth Engine(GEE) were compared. Application procedures of KOMPSAT satellite image to implement ODC service were explained, and an intermediate process related to data ingestion using actual data was demonstrated. As well, it suggested some practical schemes to utilize KOMPSAT satellite images for the ODC application service from the perspective of open data licensing. Policy and technical products for KOMPSAT images to ODC are expected to provide important references for GEOSS in GEO to apply new satellite images of other countries and organizations in the future.

키워드

GRJBBB_2019_v22n1_62_f0001.png 이미지

FIGURE 1. Interrelations among GEO, GEOSS, CEOS, and Open Data Cube

GRJBBB_2019_v22n1_62_f0002.png 이미지

FIGURE 2. Data Cube task in AOGEOSS (edited and excerpted from https://aogeoss.com/en/staticpages/index.php/task)

GRJBBB_2019_v22n1_62_f0003.png 이미지

FIGURE 3. The status of the country-based data cube, as of December 2018 (edited country development at https://www.opendatacube.org/ceos)

GRJBBB_2019_v22n1_62_f0004.png 이미지

FIGURE 4. Simple view of ODC components for application services

GRJBBB_2019_v22n1_62_f0005.png 이미지

FIGURE 5. KOMPSAT data application procedure in Open Data Cube

GRJBBB_2019_v22n1_62_f0006.png 이미지

FIGURE 7. (a) Measurement listing result of KOMPSAT ingestion, (b) RGB compositing of KOMPSAT images in ODC

GRJBBB_2019_v22n1_62_f0007.png 이미지

FIGURE 6. (a) YAML result of defining product, (b) Python module for indexing configuration of KOMPSAT optical images and (c) YAML result of ingesting data

GRJBBB_2019_v22n1_62_f0008.png 이미지

FIGURE 8. Example of ODC application case with data search modes and analytical functions (excerpted from http://ec2-52-201-154-0.compute-1.amazonaws.com/)

TABLE 1. Comparison of ODC and GEE

GRJBBB_2019_v22n1_62_t0001.png 이미지

참고문헌

  1. Ali, I., V. Naeimi, S. Cao, S. Elefante, T. S. Le, B. Bauer-Marschallinger and W. Wagner. 2017. Sentinel-1 Data Cube Exploitation: Tools, Products, Services and Quality Control. in the Proc. of the 2017 conference on Big Data from Space in Toulouse, France, 28 Nov.
  2. Brooke, B., L. Lymburner and A. Lewis. 2017. Coastal dynamics of Northern Australia - Insights from the Landsat Data Cube. Remote Sensing Applications: Society and Environment 8:94-98. https://doi.org/10.1016/j.rsase.2017.08.003
  3. Dwyer, J.L., D.P. Roy, B. Sauer, C.B. Jenkerson, H.K. Zhang and L. Lymburner. 2018. Analysis Ready Data: Enabling Analysis of the Landsat Archive. Remote Sensing 10:1363.
  4. GEO Data Sharing Working Group. 2014. White Paper: Mechanisms to Share Data as Part of GEOSS Data-CORE, https://www.earthobservations.org/documents/dsp/draft_white_paper_geoss_legal_interoperability_30_october_2011.pdf (Accessed on Jan. 27, 2019).
  5. Giuliania, G., B. Chatenoux, A. De Bono, D. Rodila, J.-P. Richard, K. Allenbach, H. Dao and P. Peduzzi. 2017. Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube(SDC) on generating Analysis Ready Data(ARD). Big Earth Data, 1(1-2):100-117. https://doi.org/10.1080/20964471.2017.1398903
  6. Gorelick, N., M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau and R. Moore. 2017. Google Earth Engine: Planetary -scale geospatial analysis for everyone. Remote Sensing of Environment 202:18-27. https://doi.org/10.1016/j.rse.2017.06.031
  7. Guo, H., L. Wang and D. Liang. 2016. Big Earth Data from space: a new engine for Earth science. Science Bulletin 61(7):505-513. https://doi.org/10.1007/s11434-016-1041-y
  8. Hu, F., M. Xu, J. Yang, Y. Liang, K. Cui, M.M. Little, C.S. Lynnes, D.Q. Duffy and C. Yang. 2018. Evaluating the Open Source Data Containers for Handling Big Geospatial Raster Data. ISPRS International Journal of Geo-Information 7:144. https://doi.org/10.3390/ijgi7040144
  9. Lewis, A., L. Lymburner, M.B.J. Purss, B. Brooke, B. Evans, A. Ip, A.G. Dekker, J.R. Irons, S. Minchin, N. Mueller, S. Oliver, D. Roberts, B. Ryan, M. Thankappan, R. Woodcock, and L. Wyborn. 2016. Rapid, high-resolution detection of environmental change over continental scales from satellite data - the Earth Observation Data Cube. International Journal of Digital Earth 9: 106-111. https://doi.org/10.1080/17538947.2015.1111952
  10. Lewis, A., S. Oliver, L. Lymburner, B. Evans, L. Wybornb, N. Mueller, G. Raevksi, J. Hooke, R. Woodcock, J. Sixsmith, W. Wu, P. Tan, F. Li, B. Killough, S. Minchin, D. Roberts, D. Ayers, B. Bala, J. Dwyer, A. Dekker, T. Dhu, A. Hicks, A. Ip, M. Purss, C. Richards, S. Sagar, C. Trenham, P. Wang, and L.-W. Wang. 2017. The Australian Geoscience Data Cube - Foundations and lessons learned. Remote Sensing of Environment 202:276-292. https://doi.org/10.1016/j.rse.2017.03.015
  11. Liu, Q., 2018. Asia-Oceania GEOSS Task 10/11 - Update on data sharing and data cubes. Presentation material in 11th GEOSS Asia-Pacific Symposium in Kyoto, Japan, 24 Oct.
  12. Killough, B., 2018a. Overview of the Open Data Cube Initiative, in Proc. of the IGARSS 2018 in Valencia, Spain, 27 July.
  13. Killough, B., 2018b. Open Data Cubes Cloud Services Experiences and Lessons Learned, Presentation material in Future Data Architectures Big Data Workshop in Sao Jose dos Campos Brazil, April 26.
  14. Klein, T., M. Nilsson, M.A. Persson, and B. Hakansson. 2017. From Open Data to Open Analyses-New Opportunities for Environmental Applications?. Environments 4:32:1-17. https://doi.org/10.20448/journal.505.2017.41.1.8
  15. Nativi, S., P. Mazzetti, M. Santoro, F. Papeschi, M. Craglia and O. Ochiai. 2015. Big Data challenges in building the Global Earth Observation System of Systems. Environmental Modelling & Software 68:1-26. https://doi.org/10.1016/j.envsoft.2015.01.017
  16. Nativi, S., P. Mazzetti and M. Craglia. 2017. A view-based model of data-cube to support big earth data systems interoperability. Big Earth Data, 1:75-99. https://doi.org/10.1080/20964471.2017.1404232
  17. Pachon, I., S. Ramirez, D. Fonseca, P. Lozano-Rivera, C. Ariza, M. P. Mancipe, M. Villamizar, H. Castro, E. Cabrera, and M.T. Becerra. 2018. Random Forest Data Cube based Algorithm for Land Cover Classification: A Colombian Case. in the Proc. of the IGARSS 2018, in Valencia, Spain, 27 July.
  18. Rizvi, S. R., B. Killough, A. Cherry and S. Gowda. 2018. Lessons Learned and cost Analysis of hosting a Full Stack Open Data Cube(ODC) Application on the Amazon Web service(AWS). in the Proc. of the IGARSS 2018, in Valencia, Spain, 27 July.
  19. Soille, P., A. Burger, D. De Marchi, P. Kempeneers, D. Rodriguez, V. Syrris and V. Vasilev. 2018. A versatile data-intensive computing platform for information retrieval from big geospatial data. Future Generation Computer Systems 81:30-40. https://doi.org/10.1016/j.future.2017.11.007
  20. STEPI. 2014. Plan for Service System Building and Platform Design for Utilization Promotion of Satellite Images 128pp.
  21. Strobl, P.P. Baumann, A. Lewis, Z. Szantoi, B. Killough, M. Purss, M. Craglia, S. Nativi, A. Held, and T. Dhu. 2017. The Six Faces of the Data Cube. in the Proc. of the 2017 conference on Big Data from Space in Toulouse, France, 28 Nov.
  22. Yang, C.Q. Huang, Z. Li, K. Liu, Kai and F. Hu. 2017. Big Data and cloud computing: innovation opportunities and challenges. International Journal of Digital Earth 10:13-53. https://doi.org/10.1080/17538947.2016.1239771

피인용 문헌

  1. RadCalNet 자료를 이용한 다목적실용위성 3A 영상 자료의 지표 반사도 성과 검증 vol.36, pp.2, 2020, https://doi.org/10.7780/kjrs.2020.36.2.1.6