SATELLITE AGRICULTURAL MONITORING IN UKRAINE AT COUNTRY LEVEL: WORLD BANK PROJECT

被引：11
作者：
Kussul, N. [1 ,2 ,3 ]
Shelestov, A. [1 ,2 ,3 ]
Yailymova, H. [3 ]
Yailymov, B. [1 ,3 ]
Lavreniuk, M. [1 ,3 ]
Ilyashenko, M. [3 ]
机构：
[1] Space Res Inst NASU SSAU, Kiev, Ukraine
[2] Natl Tech Univ, Igor Sikorsky Kyiv Polytech Inst, Kiev, Ukraine
[3] EOS Data Analyt, Kiev, Ukraine
来源：
IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2020年
基金：
欧盟地平线“2020”;
关键词：
agricultural monitoring; cloud-based information technology; data time series; satellite monitoring;
D O I：
10.1109/IGARSS39084.2020.9324573
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Ukrainian agricultural industry is one of the main sectors of economic growth. Nevertheless, Ukraine is way behind in the development. This is mostly due to the low level of modern technologies utilization by businesses and state entities. To ensure transparency, equity and reliability of Ukrainian land market, objective information on land use and crop state is required. The World Bank program "Supporting Transparent Land Governance in Ukraine" addresses these issues. Within the project, we performed satellite monitoring of land use in Ukraine, analyzed the feasibility of Google's cloud-based technology for processing large amount of data and developed a new platform to analyze the crop state using open and free Sentinel-1/2 satellite data. It is a 5-year project, which is extended for the whole country this year. We plan to make the technology of satellite monitoring operational and deployed in governmental institutions in 2023.
引用
收藏
页码：1050 / 1053
页数：4
相关论文
共 10 条
  • [1] Water resource quality monitoring using heterogeneous data and high-performance computations
    Kravchenko A.N.
    Kussul N.N.
    Lupian E.A.
    Savorsky V.P.
    Hluchy L.
    Shelestov A.Yu.
    [J]. Cybern. Syst. Anal., 2008, 4 (616-624): : 616 - 624
  • [2] Kussul N, 2017, PROCEEDINGS OF THE 2017 9TH IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT DATA ACQUISITION AND ADVANCED COMPUTING SYSTEMS: TECHNOLOGY AND APPLICATIONS (IDAACS), VOL 1, P383, DOI 10.1109/IDAACS.2017.8095109
  • [3] ALONG THE SEASON CROP CLASSIFICATION IN UKRAINE BASED ON TIME SERIES OF OPTICAL AND SAR IMAGES USING ENSEMBLE OF NEURAL NETWORK CLASSIFIERS
    Kussul, Nataliia
    Lavreniuk, Mykola
    Shelestov, Andrii
    Yailymov, Bohdan
    [J]. 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2016, : 7145 - 7148
  • [4] The Wide Area Grid Testbed for Flood Monitoring Using Earth Observation Data
    Kussul, Nataliia N.
    Shelestov, Andrii Yu.
    Skakun, Sergii V.
    Li, Guoqing
    Kussul, Olga M.
    [J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2012, 5 (06) : 1746 - 1751
  • [5] Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery
    Phan Thanh Noi
    Kappas, Martin
    [J]. SENSORS, 2018, 18 (01)
  • [6] Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture
    Rodriguez-Galiano, V. F.
    Chica-Olmo, M.
    Abarca-Hernandez, F.
    Atkinson, P. M.
    Jeganathan, C.
    [J]. REMOTE SENSING OF ENVIRONMENT, 2012, 121 : 93 - 107
  • [7] Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders
    Russwurm, Marc
    Koerner, Marco
    [J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2018, 7 (04):
  • [8] USING THE FUZZY-ELLIPSOID METHOD FOR ROBUST ESTIMATION OF THE STATE OF A GRID SYSTEM NODE
    Shelestov, A. Yu.
    Kussul, N. N.
    [J]. CYBERNETICS AND SYSTEMS ANALYSIS, 2008, 44 (06) : 847 - 854
  • [9] Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping
    Shelestov, Andrii
    Lavreniuk, Mykola
    Kussul, Nataliia
    Novikov, Alexei
    Skakun, Sergii
    [J]. FRONTIERS IN EARTH SCIENCE, 2017, 5 : 1 - 10
  • [10] Shi XJ, 2015, ADV NEUR IN, V28
1