Перегляд за Автор "Kussul, Nataliia"

Зараз показуємо 1 - 20 з 32
  • Ескіз
    ДокументВідкритий доступ
    3D Scene Reconstruction with Neural Radiance Fields (NeRF) Considering Dynamic Illumination Conditions
    (Anhalt University of Applied Sciences, 2023) Kolodiazhna, Olena; Savin, Volodymyr; Uss, Mykhailo; Kussul, Nataliia
    This paper addresses the problem of novel view synthesis using Neural Radiance Fields (NeRF) for scenes with dynamic illumination. NeRF training utilizes photometric consistency loss that is pixel-wise consistency between a set of scene images and intensity values rendered by NeRF. For reflective surfaces, image intensity depends on viewing angle and this effect is taken into account by using ray direction as NeRF input. For scenes with dynamic illumination, image intensity depends not only on position and viewing direction but also on time. We show that this factor affects NeRF training with standard photometric loss function effectively decreasing quality of both image and depth rendering. To cope with this problem, we propose to add time as additional NeRF input. Experiments on ScanNet dataset demonstrate that NeRF with modified input outperforms original model version and renders more consistent 3D structures. Results of this study could be used to improve quality of training data augmentation for depth prediction models (e.g. depth-from-stereo models) for scenes with non-static illumination.
  • Ескіз
    ДокументВідкритий доступ
    Advanced Method of Land Cover Classification Based on High Spatial Resolution Data and Convolutional Neural Network
    (Anhalt University of Applied Sciences, 2022) Shelestov, Andrii; Yailymov, Bohdan; Yailymova, Hanna; Shumilo, Leonid; Lavreniuk, Mykola; Lavreniuk, Alla; Sylantyev, Sergiy; Kussul, Nataliia
  • Ескіз
    ДокументВідкритий доступ
    Agriculture land appraisal with use of remote sensing and infrastructure data
    (IEEE, 2022) Kussul, Nataliia; Shelestov, Andrii; Yailymova, Hanna; Shumilo, Leonid; Drozd, Sophia
  • Ескіз
    ДокументВідкритий доступ
    Air Quality as Proxy for Assesment of Economic Activity
    (2023) Yailymova, Hanna; Kolotii, Andrii; Kussul, Nataliia; Shelestov, Andrii
    In Ukraine most of citizens and economic activity are concentrated over urban city centers and city functional areas. Thus, Air Quality and, in particular, levels of fine particulate matter (e.g., PM2.5 and PM 10 ) over cities can be a proxy for assessment of economic activity and density of city populations. Since the russia invasion to Ukraine started on 24 of February 2022 according to UNHCR (the UN Refugee Agency) 8 million refugees from Ukraine have now been registered across the Europe. Almost 7 million more are displaced within the country. On the other hand, there is no official statistics from national statistical service showing current influence of invasion on city economic activity or inhabitants amount. Thus, such a proxy can be used to see current situation by analyzing of particulate matter time series. In this work we compare averaged annual cumulated PM2.5 for 2018–2021 years with values for 2022 and estimate the correlation them with publicly available statistics on migration to see some relations. Global Sustainable Development Goal (SDG) indicator 11.6.2, “Annual mean levels of fine particulate in cities (population weighted)” is being extended for 2022 and compared with previous years.
  • Ескіз
    ДокументВідкритий доступ
    Air Quality Estimation in Ukraine Using SDG 11.6.2 Indicator Assessment
    (Remote Sensing, 2021-11) Shelestov, Andrii; Yailymova, Hanna; Yailymov, Bohdan; Kussul, Nataliia
  • Ескіз
    ДокументВідкритий доступ
    Assessing damage to agricultural fields from military actions in Ukraine: An integrated approach using statistical indicators and machine learning
    (2023) Kussul, Nataliia; Drozd, Sofiia; Yailymova, Hanna; Shelestov, Andrii; Lemoine, Guido; Deininger, Klaus
    The ongoing full-scale Russian invasion of Ukraine has led to widespread damage of agricultural lands, jeopardizing global food security. Timely detection of impacted fields enables quantification of production losses, guiding recovery policies and monitoring military actions. This study presents a robust methodology to automatically identify agricultural areas damaged by wartime ground activities using free Sentinel-2 satellite data. The 10 m resolution spectral bands and vegetation indices are leveraged, alongside their statistical metrics over time, as inputs to a Random Forest classifier. The algorithm efficiently pinpoints damaged fields, with accuracy metrics around 0.85. Subsequent anomaly detection delineates damages within the fields by combining spectral bands and indices. Applying the methodology over 22 biweekly periods in 2022, approximately 500 thousand ha of cropland across 10 regions of Ukraine were classified as damaged, with the most significant impacts occurring from March to September. The algorithm provides updated damage information despite cloud cover and vegetation shifts. The approach demonstrates the efficacy of automated satellite monitoring to assess agricultural impacts of military actions, supporting recovery analysis and documentation of war crimes.
  • Ескіз
    ДокументВідкритий доступ
    Assessing Ukrainian Territory Suitability for Solar Power Station Placement Using Satellite Data on Climate and Topography
    (IEEE, 2023) Kussul, Nataliia; Drozd, Sofiia
    This research aims to assess the suitability of Ukrainian territories for the placement of solar power stations using satellite data on climate and topographic characteristics. The suitability of the territories was determined using a weighted sum method, incorporating input parameters from climate maps sourced from ERA5- Land dataset, which included data on annual global horizontal solar irradiation (GHI), accumulated annual temperature above 25°C, average annual wind speed, and maps of accumulated annual precipitation. Additionally, topographic maps from the SRTM dataset were utilized, providing information on elevations, slopes, and terrain shading. Furthermore, data from Wikimapia on the locations of existing major solar power stations in Ukraine were used to verify the placement optimization. The results of the study revealed that the largest portion of the country (over 48%) exhibits moderate suitability scores (0.3-0.4). Favorable territories (suitability score above 0.3) outweigh unsuitable ones for solar power stations. The southern regions and the Crimean Peninsula offer the most favorable conditions for the placement of solar farms. Overall, all analyzed major solar power stations in Ukraine were located in optimal territories. Furthermore, it was found that certain regions such as Odessa, Poltava, Kharkiv, Zaporizhia, Dnipropetrovsk, Donetsk, and Luhansk demonstrate good suitability scores (0.3-0.4), yet they are not fully exploited. These regions hold significant potential for the future construction of powerful and productive solar power stations.
  • Ескіз
    ДокументВідкритий доступ
    Automatic Deforestation Detection based on the Deep Learning in Ukraine
    (IEEE, 2021) Shumilo, Leonid; Lavreniuk, Mykola; Kussul, Nataliia; Shevchuk, Bella
  • Ескіз
    ДокументВідкритий доступ
    Biophysical Impact of Sunflower Crop Rotation on Agricultural Fields
    (Sustainability, 2022) Kussul, Nataliia; Deininger, Klaus; Shumilo, Leonid; Lavreniuk, Mykola; Ayalew Ali, Daniel; Nivievskyi, Oleg
  • Ескіз
    ДокументВідкритий доступ
    Cloud Platforms and Technologies for Big Satellite Data Processing
    (Springer, 2023) Kussul, Nataliia; Shelestov, Andrii; Yailymov, Bohdan
    This paper addresses the problem of processing large volumes of satellite data and compares different cloud platforms for potential solutions. Existing cloud platforms like Google Earth Engine, Amazon Web Services (AWS), and CREODIAS have been used to tackle this challenge. However, this study proposes an optimal pipeline for satellite data processing, taking into account the advantages and limitations of each platform. The specific focus is on solving machine learning problems using satellite data. In the experiment conducted, the effectiveness of each cloud platform was analyzed. It was found that cloud platforms offer benefits such as flexibility, access to computing resources, and parallel processing architectures, leading to increased productivity and cost reduction. CREODIAS, in particular, stands out due to its specialization in satellite data and easy access to various data types, along with tools for data searching and visualization. The experiment demonstrated that tasks, from data loading to classification, were executed fastest on CREODIAS resources. However, AWS performed data classification faster. The availability of its own internal data bucket was a significant advantage of CREODIAS, especially when considering ARD data. These findings contribute to the advancement of AI methodologies and have practical implications for solving satellite monitoring applications.
  • Ескіз
    ДокументВідкритий доступ
    Complex method for land degradation estimation
    (IOP Conf. Series: Earth and Environmental Science, 2023-01) Kussul, Nataliia; Shumilo, Leonid; Yailymova, Hanna; Shelestov, Andrii; Krasilnikova, Tetiana
  • Ескіз
    ДокументВідкритий доступ
    Discrete Atomic Transform-Based Lossy Compression of Three-Channel Remote Sensing Images with Quality Control
    (Remote Sensing, 2021) Makarichev, Victor; Vasilyeva, Irina; Lukin, Vladimir; Vozel, Benoit; Shelestov, Andrii; Kussul, Nataliia
  • Ескіз
    ДокументВідкритий доступ
  • Ескіз
    ДокументВідкритий доступ
    Fire Danger Assessment Based on the Improved Fire Weather Index
    (IEEE, 2022) Kussul, Nataliia; Yailymov, Bohdan; Shelestov, Andrii; Yailymova, Hanna
  • Ескіз
    ДокументВідкритий доступ
    Fire Danger Assessment Using Moderate-Spatial Resolution Satellite Data
    (MDPI, 2023) Kussul, Nataliia; Fedorov, Oleh; Yailymov, Bohdan; Pidgorodetska, Liudmyla; Kolos, Liudmyla; Yailymova, Hanna; Shelestov, Andrii
    Fire is one of the most common disturbances in natural ecosystems. The analysis of various sources of information (official and unofficial) about the fires in Ukraine (2019–2020) showed a lack of timely and reliable information. Satellite observation is of crucial importance to provide accurate, reliable, and timely information. This paper aims to modify the index of fire danger of a forest’s FWI by increasing its precision, based on the use of higher spatial resolution satellite data. A modification of the FWI method involves the utilization of the soil moisture deficit, in addition to the six subindices of the FWI system. In order to calculate the subindices values, weather data from the Copernicus Atmosphere Monitoring Service were used. Soil moisture deficit is calculated using Sentinel-1 radar satellite data on the water saturation degree of the soil surface layer and geospatial parameters from the 3D Soil Hydraulic Database of Europe. The application of the proposed methodology using the specified satellite, weather, and geospatial data makes it possible to assess fire danger on a continental scale with a spatial resolution of 250 m, 1 km, and a daily temporal resolution. Validation of the proposed method for modifying the FWI system demonstrates an improvement in the precision and relevance of fire danger prediction.
  • Ескіз
    ДокументВідкритий доступ
    Forecast of Yield of Major Crops in Ukraine in War Conditions 2022 based on MODIS and Sentinel-2 Satellite Data
    (2023) Kussul, Nataliia; Drozd, Sophia; Yailymova, Hanna
    Ukraine was one of the main exporters of plant products. However, as a result of the aggression, the country's agriculture has suffered greatly, export volumes are decreasing, which may provoke a shortage of agricultural products on world markets. It is impossible to assess crop yield and forecast the harvest volume locally, as the collection of information has become difficult due to the active conduct of hostilities and the occupation of a large part of the territories. Therefore, it is necessary to use land remote sensing data to assess crop yield. In this research, we will build regression models based on a random forest for each region of Ukraine to estimate crop yield based on 16-day composites of the NDVI time series during the summer vegetation period from Sentinel-2 (10m) and MODIS (500m) satellites, involving in the calculation NDVI crop maps. The official yield of maize, sunflower, soybean, rapeseed, and wheat for the years 2016-2021 was used as training data. According to the results of the analysis, models based on NDVI from the MODIS satellite showed better accuracy (relative error within 8-18%), but models based on NDVI data from Sentinel-2 better described the variance of the predicted yield. During the research, we found a sharp drop in land productivity indicators compared to the productivity of 2021 for the territories of central, southern and eastern Ukraine. According to our estimates based on MODIS data, the average yield at the country level is expected to be 40.98 t/ha for wheat, 57.66 t/ha for maize, 23.57 t/ha for sunflower, 21.06 t/ha for soybeans, 21.15 t/ha for rapeseed. Estimates based on Sentinel-2 data: 43.22 t/ha for wheat, 71.93 t/ha for maize, 26.86 t/ha for sunflower, 22.94 t/ha for soybeans, 28.23 t/ha for rapeseed.
  • Ескіз
    ДокументВідкритий доступ
    Generative adversarial network augmentation for solving the training data imbalance problem in crop classification
    (2023) Shumilo, Leonid; Okhrimenko, Anton; Kussul, Nataliia; Drozd, Sofiia; Shkalikov, Oleh
    Deep learning models offer great potential for advancing land monitoring using satellite data. However, they face challenges due to imbalanced real-world data distributions of land cover and crop types, hindering scalability and transferability. This letter presents a novel data augmentation method employing Generative Adversarial Neural Networks (GANs) with pixel-to-pixel transformation (pix2pix). This approach generates realistic synthetic satellite images with artificial ground truth masks, even for rare crop class distributions. It enables the creation of additional minority class samples, enhancing control over training data balance and outperforming traditional augmentation methods. Implementing this method improved the overall map accuracy (OA) and intersection over union (IoU) by 1.5% and 2.1%, while average crop type classes’ user accuracy (UA) and producer accuracies (PA), as well as IoU, were improved by 11.2%, 6.4% and 10.2%.
  • Ескіз
    ДокументВідкритий доступ
    Geospatial Analysis of Life Quality in Ukrainian Rural Areas
    (IEEE, 2023) Yailymova, Hanna; Yailymov, Bohdan; Kussul, Nataliia; Shelestov, Andrii
    In this work, the authors developed an initial algorithm for assessing the quality of life in rural areas of Ukraine using the aggregation of heterogeneous geospatial information. The approach consists in a comprehensive assessment of the remoteness of the village from vital infrastructure facilities (hospitals, educational institutions, banks, libraries, shops, roads, power lines, etc.), to natural ecosystems (water bodies, forests or parks), as well as to occupied territories. The obtained results show that the largest number of villages with a depressed quality of life are located in the eastern and southern territories of the country, and with a positive quality - mainly in the west and central Ukraine. This, of course, is partly related to active hostilities, but considering that the proposed algorithm works based on the analysis of distances to various objects, it can be concluded that the war only worsened the condition of life in the villages.
  • Ескіз
    ДокументВідкритий доступ
    Geospatial monitoring of sustainable and degraded agricultural land
    (2023-07) Yailymova, Hanna; Yailymov, Bohdan; Kussul, Nataliia; Shelestov, Andrii; Shumilo, Leonid
    In this study, the assessment of sustainable development goal (SDG) indicator 2.4.1 for Ukraine and Germany is conducted using geospatial and satellite data. The traditional methodology for the SDG indicator 2.4.1 calculation cannot be directly applied to the Ukrainian territory due to the lack of systematic data collection of the essential indicators. Therefore, the authors have developed an integrated approach to estimate land degradation, that uses different schemes for various land cover and crop types at the national scale, utilizing satellite data and employing the WOFOST model for crop growing simulation. The research describes the information sources used for creation crop type classification maps and the necessary data for modeling leaf area index (LAI) based on the WOFOST model. The calculated indicators are determined for each Ukrainian region from 2018 to 2022. Observations in 2022 show a decline in the indicator 2.4.1 across nearly all regions of Ukraine, directly attributed to the military conflicts within the Ukraine. To assess the possibility of applying the developed technology to a large area, the indicator was calculated for a European country (Germany).
  • Ескіз
    ДокументВідкритий доступ
    Information Technology for Land Degradation Assessment Based on Remote Sensing
    (Anhalt University of Applied Sciences, 2022) Kussul, Nataliia; Shelestov, Andrii; Shumilo, Leonid; Titkov, Dmytro; Yailymova, Hanna