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 A multimodal dataset for forest damage detection and machine learning(Assembled by Conference Management Services, Inc., 2024) Yailymova, Hanna; Yailymov, Bohdan; Salii, Yevhenii; Kuzin, Volodymyr; Shelestov, Andrii; Kussul, NataliiaAccurately recognizing areas of forest damage is crucial for planning, monitoring recovery processes, and evaluating environmental impact following catastrophic events. The widespread accessibility of satellite data, coupled with the ongoing advancement of machine and deep learning techniques, as well as computer vision methods, renders the implementation of these approaches in the automatic detection of damaged forest areas highly difficult. Nevertheless, a significant challenge in this regard is the scarcity of labeled data. The purpose of this article is to provide a useful and reliable dataset for territory of Ukraine for scientists, conservationists, foresters and other stakeholders involved in monitoring forest damage and its consequences for forest ecosystems and their services. The created dataset contains 18 locations with a time series of satellite images with a resolution of up to 10 m per pixel across Ukraine, as well as weather information. The data was collected from the Copernicus Sentinel-1,2 satellite missions as well as based on ERA-5 weather information.Документ Відкритий доступ A Novel Approach for Rapid Detection of Forest Degradation and Diseases Through Anomaly Analysis of Sentinel-2 Spectral Data(Anhalt University of Applied Sciences, 2025-04) Drozd, Sofiia; Kussul, Nataliia; Yailymova, HannaForest degradation is an ongoing global issue, with significant environmental impacts that necessitate efficient monitoring and management. This paper presents a simple yet effective method for detecting forest degradation using freely available Sentinel-2 satellite data and an anomaly detection approach. The aim of this study was to develop an accessible and reliable technique that could match the performance of more complex algorithms while using minimal computational resources. The research focused on spectral bands with 10-20 m resolution and vegetation indices (NDVI, NDMI, GCI, PSSRa) to analyze forest damage in the Harz region. The method involved identifying anomalies in the spectral data relative to randomly selected reference points from healthy forest areas, which were verified with high-resolution imagery from Google Earth Pro. The results demonstrated that specific Sentinel-2 bands, particularly B3 and B5, were the most informative for detecting damaged forests, while vegetation indices were less effective. By analyzing anomalies in these bands, we successfully tracked forest degradation from 2020 to 2024, revealing a significant increase in damage between 2020 and 2021, with a total of 68.1 thousand hectares of forest lost by 2024. The theoretical relevance of this study lies in the development of a cost-effective and straightforward method for forest monitoring, while the practical relevance is evident in its potential for large-scale forest management and conservation. This method provides an efficient tool for monitoring forest health with minimal data requirements and computational effort, offering a promising solution for forest managers and conservationists worldwide.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 Ukraine`s solar power potential: a comprehensive analysis using satellite data and fuzzy logic(Assembled by Conference Management Services, Inc., 2024) Drozd, Sofiia; Kussul, NataliiaThis study evaluates the land suitability for the placement of solar power stations in Ukraine, utilizing satellite data on climate factors (Global Horizontal Irradiance, temperature, precipitation, wind speed), topography (elevation, slope), and land use. Fuzzy logic, pairwise comparisons, and weighted linear combination methods were utilized to develop a high-resolution (100 m) land suitability map for the installation of solar power plants. The results show that more than half (54.5%) of Ukraine’s territory has a high suitability score (exceeding 0.65) for solar power stations, particularly in the southern and eastern regions, such as Odessa, Kherson, Mykolaiv, Zaporizhia, Donetsk, and Crimea. Only 10.68% of the land has a suitability score less than 0.6, and 18.18% is deemed absolutely unsuitable (with a score of 0, due to land cover), primarily located in the western and northern parts of the country. This indicates that Ukraine has significant potential for green energy production. The study provides an effective and useful tool for decision-making on the optimal location of solar power facilities in Ukraine.Документ 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 Detection of War-Caused Agricultural Field Damages Using Sentinel-2 Satellite Data with Machine Learning and Anomaly Detection(ACM Special Interested Group on Applied Computing (SIGAPP), 2024) Drozd, SofiiaThis research aims to detect war-caused damages on agricultural fields in Ukraine using Sentinel-2 satellite data. To achieve this, a Random Forest-based classification and an anomaly detection method deployed in the GEE cloud environment are applied. Two spectral bands - blue (B2) and green (B3) and two vegetation indices - NDVI and GCI - were used as input parameters. According to the results, the f1-score of classification reach 0.9. Using the developed methodology, more than 1.5 millions ha of fields were identified as damaged during the period of 2022--2023.Документ 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 Features’ Selection for Forest State Classification Using Machine Learning on Satellite Data(Assembled by Conference Management Services, Inc., 2024) Salii, Yevhenii; Kuzin, Volodymyr; Lavreniuk, Alla; Kussul, Nataliia; Shelestov, AndriiThis paper discusses the use of advanced computer vision and artificial intelligence techniques for analysing remote sensing data, specifically focusing on the semantic segmentation of forest areas. The goal is to identify forest damage caused by insect pests using multispectral images from Sentinel-2 satellites. The proposed approach involves using genetic algorithms to automatically select informative features based on vegetation indices. A new fitness function is introduced to assess the quality of the selected feature sets. The neural network is then trained and tested using real data. The results of the study show the effectiveness of proposed approach and highlight its advantages over traditional methods. The developed technique allowed to obtain highly informative set of features with minimized redundancy within huge feature space with moderate amount of computation.Документ Відкритий доступ Fire Danger Assessment Based on the Improved Fire Weather Index(IEEE, 2022) Kussul, Nataliia; Yailymov, Bohdan; Shelestov, Andrii; Yailymova, HannaДокумент Відкритий доступ Flooded areas monitoring below the Kakhovka Dam based on machine learning and satellite data(Assembled by Conference Management Services, Inc., 2024) Yailymov, Bohdan; Yailymova, Hanna; Kussul, Nataliia; Shelestov, AndriiThis study analyzed the flooding under the Kakhovka Dam in Ukraine using satellite remote sensing data after the dam was destroyed on June 6, 2023. Maps of the water bodies were created before and after the flooding disaster using Sentinel-1, Sentinel-2, and Landsat-9 imagery. A random forest classifier was used to map the flooded areas. As of June 9, 2023, the total flooded area below the Kakhovka Dam was 47,330 hectares, impacting agricultural lands, forests, grasslands and human settlements. The flooding also affected areas along the Ingulets River, leading to inundation of croplands located close to the river banks which could impact water quality. The disappearance of water canals that were used for irrigation of croplands is also analyzed, showing the far-reaching agricultural impacts of this flooding event. This study demonstrates the utility of satellite remote sensing for rapid monitoring and quantification of the impacts from dam failure flooding disasters.Документ 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 Foundation Model Integration in a Multi-Instance Digital Twin System for Land Use Change(IEEE, 2025-09) Kussul, Nataliia; Shelestov, Andrii; Giuliani, Gregory; Drozd, Sofiia; Kolotii, Andrii; Salii, Yevhenii; Cherniatevych, Anton; Yavorskyi, Oleksandr; Malyniak, Volodymyr; Poussin, CharlotteThis paper presents a novel approach to monitoring land use change by integrating foundation models within a dual-timescale Digital Twin (DT) framework. While existing Earth system DTs primarily focus on atmospheric variables, our architecture explicitly addresses the dynamics of land use transformation. The proposed system utilizes a hierarchical structure of Digital Twin Instances and Aggregators that operate on two temporal scales: a rapid change component for near real-time vegetation monitoring and a gradual change component for long-term land use classification. O ur i mplementation r elies o n cloud-based data pipelines and foundation models for analyzing satellite imagery. It features a cognitive user interface that transforms complex geospatial data into contextually relevant insights. By incorporating pre-trained foundation models and physics-informed neural networks, our framework is designed to improve change detection accuracy while reducing computational requirements. Implementation experiences in Ukrainian and Swiss landscapes demonstrate the framework’s effectiveness across diverse geographic contexts.
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