M
Mykola Lavreniuk
Researcher at National Technical University
Publications - 65
Citations - 2592
Mykola Lavreniuk is an academic researcher from National Technical University. The author has contributed to research in topics: Land cover & Deep learning. The author has an hindex of 16, co-authored 62 publications receiving 1742 citations. Previous affiliations of Mykola Lavreniuk include Taras Shevchenko National University of Kyiv & National Academy of Sciences of Ukraine.
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Journal ArticleDOI
Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data
TL;DR: A multilevel DL architecture that targets land cover and crop type classification from multitemporal multisource satellite imagery outperforms the one with MLPs allowing us to better discriminate certain summer crop types.
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Exploring Google Earth Engine platform for big data processing: classification of multi-temporal satellite imagery for crop mapping
Andrii Shelestov,Mykola Lavreniuk,Nataliia Kussul,Alexei Novikov,Sergii Skakun,Sergii Skakun +5 more
TL;DR: Efficiency of using the Google Earth Engine (GEE) platform when classifying multi-temporal satellite imagery with potential to apply the platform for a larger scale and in terms of classification accuracy, the neural network based approach outperformed support vector machine, decision tree and random forest classifiers available in GEE.
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Parcel-Based Crop Classification in Ukraine Using Landsat-8 Data and Sentinel-1A Data
Nataliia Kussul,Guido Lemoine,Francisco Javier Gallego,Sergii Skakun,Mykola Lavreniuk,Andrii Shelestov +5 more
TL;DR: Comparing pixel-based and parcel-based approaches to crop classification from multitemporal optical (Landsat-8) and synthetic-aperture radar (SAR) Sentinel-1 imagery finds that pixel- based overall classification accuracy can be increased from 85.32% to 89.40% when using parcel boundaries.
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Efficiency Assessment of Multitemporal C-Band Radarsat-2 Intensity and Landsat-8 Surface Reflectance Satellite Imagery for Crop Classification in Ukraine
TL;DR: It is found that using backscatter coefficients from SAR images alone provides the same performance for winter crops (wheat and rapeseed) as surface reflectance from optical images.
Journal ArticleDOI
Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity
François Waldner,Diego de Abelleyra,Santiago R. Verón,Miao Zhang,Bingfang Wu,Dmitry Plotnikov,Sergey Bartalev,Mykola Lavreniuk,Sergii Skakun,Nataliia Kussul,Guerric Le Maire,Stéphane Dupuy,Ian Jarvis,Pierre Defourny +13 more
TL;DR: In this paper, the authors compared five existing cropland mapping methodologies over five contrasting Joint Experiment for Crop Assessment and Monitoring JECAM sites of medium to large average field size using the time series of 7-day 250m MODIS mean composites red and near-infrared channels.