Матеріали конференцій, семінарів і т.п. (ММАД)
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Документ Відкритий 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, NataliiaThis 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.Документ Відкритий 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, NataliiaДокумент Відкритий доступ Agriculture land appraisal with use of remote sensing and infrastructure data(IEEE, 2022) Kussul, Nataliia; Shelestov, Andrii; Yailymova, Hanna; Shumilo, Leonid; Drozd, SophiaДокумент Відкритий доступ Air Quality as Proxy for Assesment of Economic Activity(2023) Yailymova, Hanna; Kolotii, Andrii; Kussul, Nataliia; Shelestov, AndriiIn 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.Документ Відкритий доступ Artificial Intelligence models in solving Ill-posed Inverse problems of Remote Sensing GHG emission(Leaving Planet Symposium, 2022) Sylantyev, Sergiy; Yailymova, Hanna; Shelestov, AndriiДокумент Відкритий доступ Assessing Ukrainian Territory Suitability for Solar Power Station Placement Using Satellite Data on Climate and Topography(IEEE, 2023) Kussul, Nataliia; Drozd, SofiiaThis 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.Документ Відкритий доступ Automatic Deforestation Detection based on the Deep Learning in Ukraine(IEEE, 2021) Shumilo, Leonid; Lavreniuk, Mykola; Kussul, Nataliia; Shevchuk, BellaДокумент Відкритий доступ Autoregressive models for air quality investigation(2023-08) Zalieska, Olena; Yailymova, HannaThe aim of the work is to build a forecast of air quality in Kyiv for some period of time. For this purpose we preprocessed and analized data, selected and fitted a model.Документ Відкритий 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, TetianaДокумент Відкритий доступ Earth observation data science programs in National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"(2022) Kussul, Nataliia; Shelestov, AndriiДокумент Відкритий доступ Extension of Copernicus Urban Atlas to Non-European Countries(IEEE, 2021) Shelestov, A.; Yailymova, H.; Yailymov, B.; Shumilo, L.; Lavreniuk, A.MДокумент Відкритий доступ Fire Danger Assessment Based on the Improved Fire Weather Index(IEEE, 2022) Kussul, Nataliia; Yailymov, Bohdan; Shelestov, Andrii; Yailymova, HannaДокумент Відкритий 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, HannaUkraine 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.Документ Відкритий доступ Fusion of classification algorithms for landfill detection in Ukraine(2022) Shelestov, Andrii; Yailymov, Bohdan; Yailymova, Hanna; Mikava, PolinaДокумент Відкритий доступ Fusion of very high and moderate spatial resolution satellite data for detection and mapping of damages in agricultural fields(IEEE, 2023) Kussul, Natallia; Drozd, Sofiia; Skakun, Sergii; Duncan, Erik; Becker-Reshef, InbalThe war in Ukraine has resulted in significant losses in the agricultural sector due to damages to farmlands posing a threat to global food security. To restore the prosperity of the agricultural sector it is essential to detect and assess damages in agricultural fields and monitor their evolution. Commercial satellite data at very high spatial resolution $(\lt3 \mathrm{m})$ such as sub-meter imagery acquired by Maxar’s WorldView and Planet Labs’ SkySat platforms allow detection and mapping of artillery craters at fine scale. However, the frequency of acquisition and geographical coverage of this type of data is limited and may be quite low, e.g., 1-2 scenes per agriculture season. With the aim to continuously monitor the state of the fields over large areas in Ukraine we must compliment the analysis with satellite data at lower spatial resolution, e.g., daily PlanetScope at $\sim 3-\mathrm{m}$ and 10-m Sentinel-2/MSI. Here, we propose a data fusion approach to monitor artillery craters in agricultural fields using combination of satellite images acquired at different spatial and temporal resolution. Specifically, we use a single-date SkySat image at 0.5-m resolution with crater detection using previously developed deep learning approach along with multi-temporal data acquired by PlanetScope and Sentinel-2 images. For the latter, we detect anomalies of refelecant signal in the blue and green spectral bands and the Normalized Difference Water Index (NDWI). This approach is applied to a test area of 8,800 ha in Donetsk oblast. We found that with PlanetScope images at 3-m we were able to identify 202 ha of craters, or 63% of those in SkySat imagery; with Sentinel-2 at 10-m we detected 165 ha (or 51%) of craters. Craters with an area smaller than $100 \mathrm{m}{2}$ were poorly detected. By analyzing anomalies in multi-temporal PlanetScope and Sentinel-2 images, we were able to identify craters that were not detected in SkySat data highlighting the importance of temporal component in the data. Furthermore, with daily PlanetScope data combined with Sentinel-2 data (3-5 days), we were able to estimate the dates of crater appearances and analyze the dynamics of craters and their evolution.Документ Відкритий доступ Generative Adversarial Networks for the Satellite Data Super Resolution Based on the Transformers with Attention(2023) Lavreniuk, Mykola; Shumilo, Leonid; Lavreniuk, AllaIn recent years, free access to high and medium resolution data has become available, providing researchers with the opportunity to work with low resolution satellite images on a global scale. Sentinel-1 and Sentinel-2 are popular sources of information due to their high spectral and spatial resolution. To obtain a final product with a resolution of 10 meters, we have to use bands with a resolution of 10 meters. Other satellite data with lower resolution, such as Landsat-8 and Landsat-9, can improve the results of land monitoring, but their harmonization requires a process known as super-resolution. In this study, we propose a method for improving the resolution of low-resolution images using advanced deep learning techniques called Generative Adversarial Networks (GANs). The state-of-the-art neural networks, namely transformers, with the combination of channel attention and self-attention blocks were employed at the base of the GANs. Our experiments showed that this approach can effectively increase the resolution of Landsat satellite images and could be used for creating high resolution products.Документ Відкритий доступ Geospatial Analysis of Leased Lands in Ukraine(IEEE, 2021) Shelestov, Andrii; Yailymov, Bohdan; Parkhomchuk, OleksandrДокумент Відкритий доступ Geospatial Analysis of Life Quality in Ukrainian Rural Areas(IEEE, 2023) Yailymova, Hanna; Yailymov, Bohdan; Kussul, Nataliia; Shelestov, AndriiIn 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.Документ Відкритий доступ Geospatial monitoring of sustainable and degraded agricultural land(2023-07) Yailymova, Hanna; Yailymov, Bohdan; Kussul, Nataliia; Shelestov, Andrii; Shumilo, LeonidIn 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).Документ Відкритий доступ Ground Based Validation of Copernicus Atmosphere Monitoring Service Data for Kyiv(IEEE, 2021) Shelestov, Andrii; Yailymova, Hanna; Yailymov, Bohdan; Samoilenko, Oleg; Shumilo, Leonid