| Deep learning classification of land cover and crop types using remote sensing data N Kussul, M Lavreniuk, S Skakun, A Shelestov IEEE Geoscience and Remote Sensing Letters 14 (5), 778-782, 2017 | 1949 | 2017 |
| Exploring Google Earth Engine platform for big data processing: Classification of multi-temporal satellite imagery for crop mapping A Shelestov, M Lavreniuk, N Kussul, A Novikov, S Skakun frontiers in Earth Science 5, 17, 2017 | 562 | 2017 |
| Parcel-based crop classification in Ukraine using Landsat-8 data and Sentinel-1A data N Kussul, G Lemoine, FJ Gallego, SV Skakun, M Lavreniuk, AY Shelestov IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2016 | 238 | 2016 |
| Efficiency assessment of multitemporal C-band Radarsat-2 intensity and Landsat-8 surface reflectance satellite imagery for crop classification in Ukraine S Skakun, N Kussul, AY Shelestov, M Lavreniuk, O Kussul IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2015 | 236 | 2015 |
| Regional scale crop mapping using multi-temporal satellite imagery N Kussul, S Skakun, A Shelestov, M Lavreniuk, B Yailymov, O Kussul The International Archives of the Photogrammetry, Remote Sensing and Spatial …, 2015 | 137 | 2015 |
| Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity F Waldner, D De Abelleyra, SR Verón, M Zhang, B Wu, D Plotnikov, ... International Journal of Remote Sensing 37 (14), 3196-3231, 2016 | 134 | 2016 |
| Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine A Kolotii, N Kussul, A Shelestov, S Skakun, B Yailymov, R Basarab, ... The International Archives of the Photogrammetry, Remote Sensing and Spatial …, 2015 | 118 | 2015 |
| Cloud approach to automated crop classification using Sentinel-1 imagery A Shelestov, M Lavreniuk, V Vasiliev, L Shumilo, A Kolotii, B Yailymov, ... IEEE Transactions on Big Data 6 (3), 572-582, 2019 | 111 | 2019 |
| A workflow for Sustainable Development Goals indicators assessment based on high-resolution satellite data N Kussul, M Lavreniuk, A Kolotii, S Skakun, O Rakoid, L Shumilo International Journal of Digital Earth, 2020 | 108 | 2020 |
| A rule-based approach for crop identification using multi-temporal and multi-sensor phenological metrics G Ghazaryan, O Dubovyk, F Löw, M Lavreniuk, A Kolotii, J Schellberg, ... European Journal of Remote Sensing 51 (1), 511-524, 2018 | 104 | 2018 |
| Regional retrospective high resolution land cover for Ukraine: Methodology and results M Lavreniuk, N Kussul, S Skakun, A Shelestov, B Yailymov 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS …, 2015 | 80 | 2015 |
| Deep learning approach for large scale land cover mapping based on remote sensing data fusion N Kussul, A Shelestov, M Lavreniuk, I Butko, S Skakun 2016 IEEE international geoscience and remote sensing symposium (IGARSS …, 2016 | 78 | 2016 |
| Large scale crop classification using Google earth engine platform A Shelestov, M Lavreniuk, N Kussul, A Novikov, S Skakun 2017 IEEE international geoscience and remote sensing symposium (IGARSS …, 2017 | 73 | 2017 |
| Large-scale classification of land cover using retrospective satellite data MS Lavreniuk, SV Skakun, AJ Shelestov, BY Yalimov, SL Yanchevskii, ... Cybernetics and Systems Analysis 52, 127-138, 2016 | 67 | 2016 |
| Geospatial Intelligence and Data Fusion Techniques for Sustainable Development Problems. N Kussul, A Shelestov, R Basarab, S Skakun, O Kussul, M Lavrenyuk ICTERI 1356, 196-203, 2015 | 58 | 2015 |
| Parcel based classification for agricultural mapping and monitoring using multi-temporal satellite image sequences N Kussul, G Lemoine, J Gallego, S Skakun, M Lavreniuk 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS …, 2015 | 57 | 2015 |
| Satellite data reveal cropland losses in South-Eastern Ukraine under military conflict S Skakun, CO Justice, N Kussul, A Shelestov, M Lavreniuk Frontiers in Earth Science 7, 305, 2019 | 55 | 2019 |
| Roadside collection of training data for cropland mapping is viable when environmental and management gradients are surveyed F Waldner, N Bellemans, Z Hochman, T Newby, D de Abelleyra, ... International Journal of Applied Earth Observation and Geoinformation 80, 82-93, 2019 | 47 | 2019 |
| Conflation of expert and crowd reference data to validate global binary thematic maps F Waldner, A Schucknecht, M Lesiv, J Gallego, L See, A Pérez-Hoyos, ... Remote sensing of environment 221, 235-246, 2019 | 40 | 2019 |
| Deep learning crop classification approach based on sparse coding of time series of satellite data M Lavreniuk, N Kussul, A Novikov IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium …, 2018 | 37 | 2018 |
Nataliia Kussul (Наталія Куссуль)University of MarylandPatvirtintas el. paštas umd.edu
