Publication de 12 numéros par an
ISSN Imprimer: 1064-2315
ISSN En ligne: 2163-9337
Indexed in
Land Cover Changes Analysis Based on Deep Machine Learning Technique
RÉSUMÉ
The methodology for solving the problem of processing of large amount of remote sensing data is proposed. The hierarchical structure of the model of deep learning method is based on neural network approach and geospatial analysis methods. This methodology was applied for high resolution land cover change mapping for Ukraine territory from 1990 to 2010. The efficiency of this approach was shown for non-arable agricultural area and changes analysis, particularly, in the eastern regions during the occupation period.
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Kussul Nataliia, Kolotii Andrii, Shelestov Andrii, Yailymov Bohdan, Lavreniuk Mykola, Land degradation estimation from global and national satellite based datasets within UN program, 2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), 2017. Crossref
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Kussul N., Kolotii A., Adamenko T., Yailymov B., Shelestov A., Lavreniuk M., Ukrainian cropland through decades: 1990–2016, 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON), 2017. Crossref
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Kussul Nataliia, Lavreniuk Mykola, Skakun Sergii, Shelestov Andrii, Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data, IEEE Geoscience and Remote Sensing Letters, 14, 5, 2017. Crossref
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Lavreniuk M., Kussul N., Meretsky M., Lukin V., Abramov S., Rubel O., Impact of SAR data filtering on crop classification accuracy, 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON), 2017. Crossref
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Lesiv Myroslava, Schepaschenko Dmitry, Moltchanova Elena, Bun Rostyslav, Dürauer Martina, Prishchepov Alexander V., Schierhorn Florian, Estel Stephan, Kuemmerle Tobias, Alcántara Camilo, Kussul Natalia, Shchepashchenko Maria, Kutovaya Olga, Martynenko Olga, Karminov Viktor, Shvidenko Anatoly, Havlik Petr, Kraxner Florian, See Linda, Fritz Steffen, Spatial distribution of arable and abandoned land across former Soviet Union countries, Scientific Data, 5, 1, 2018. Crossref
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Jadhav Jagannath K., Singh R. P., Automatic semantic segmentation and classification of remote sensing data for agriculture, Mathematical Models in Engineering, 4, 2, 2018. Crossref
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Kussul Nataliia, Kolotii Andrii, Shelestov Andrii, Lavreniuk Mykola, Bellemans Nicolas, Bontemps Sophie, Defourny Pierre, Koetz Benjamin, Sentinel-2 for agriculture national demonstration in ukraine: Results and further steps, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017. Crossref
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Nijhawan Rahul, Das Josodhir, Raman Balasubramanian, A hybrid of deep learning and hand-crafted features based approach for snow cover mapping, International Journal of Remote Sensing, 40, 2, 2019. Crossref
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Kussul Nataliia, Lavreniuk Mykola, Skakun Sergii, Shelestov Andrii, Cropland productivity assessment for Ukraine based on time series of optical satellite images, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017. Crossref
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Zhou Zhuang, Li Shengyang, Peanut planting area change monitoring from remote sensing images based on deep learning, 2017 4th International Conference on Systems and Informatics (ICSAI), 2017. Crossref
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YAILYMOV B.Ya., LAVRENIUK M.S., SHELESTOV A.Yu., KOLOTII A.V., YAILYMOVA H.O., FEDOROV O.P., Methods of essential variables determination for the Earth’s surface state assessing, Kosmìčna nauka ì tehnologìâ, 24, 4, 2018. Crossref
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Nijhawan Rahul, Joshi Deepankar, Narang Naman, Mittal Aditya, Mittal Ankush, A Futuristic Deep Learning Framework Approach for Land Use-Land Cover Classification Using Remote Sensing Imagery, in Advanced Computing and Communication Technologies, 702, 2019. Crossref
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Shumilo Leonid, Kussul Nataliia, Shelestov Andrii, Korsunska Yuliia, Yailymov Bohdan, Sentinel-3 Urban Heat Island Monitoring and analysis for Kyiv Based on Vector Data, 2019 10th International Conference on Dependable Systems, Services and Technologies (DESSERT), 2019. Crossref
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Shelestov Andrii, Yailymov Bohdan, The state of actual land use monitoring in the leading countries with use of satellite data, Ukrainian journal of remote sensing, 12, 2017. Crossref
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Huang Yun, Tang Linbo, Jing Donglin, Li Zhen, Tian Yibing, Zhou Shichao, Research on Crop Planting Area Classification From Remote Sensing Image Based on Deep Learning, 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP), 2019. Crossref
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Sharifi Alireza, Felegari Shilan, Tariq Aqil, Siddiqui Saima, Forest Cover Change Detection Across Recent Three Decades in Persian Oak Forests Using Convolutional Neural Network, in Climate Impacts on Sustainable Natural Resource Management, 2021. Crossref
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Moradi Elahe, Sharifi Alireza, Assessment of forest cover changes using multi-temporal Landsat observation, Environment, Development and Sustainability, 2022. Crossref
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Lee Seong-Hyeok, Lee Moung-Jin, Comparisons of Multi Resolution Based AI Training Data and Algorithms Using Remote Sensing Focus on Landcover, Frontiers in Remote Sensing, 3, 2022. Crossref
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Yang Xuan, Bai Yongqing, Chen Pan, Li Cong, Lu Kaixuan, Chen Zhengchao, A Prior Semantic Network for Large-Scale Landcover Change of Landsat Imagery, Sustainability, 14, 20, 2022. Crossref
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Shelestov Andrii, Yailymov Bohdan, Yailymova Hanna, Nosok Svitlana, Piven Oleh, Cloud-Based Technologies for Data Processing in Ukraine: International Context, in Progress in Advanced Information and Communication Technology and Systems, 548, 2023. Crossref