Кафедра математичного моделювання та аналізу даних (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 3D-реконструкція приміщень за сферичними панорамами(КПІ ім. Ігоря Сікорського, 2023) Крохальов, Іван Данилович; Орєхов, Олександр АрсенiйовичМагістерська дисертація: 58 с., 35 рис., 2 табл., 15 джерел. Об`єкт дослідження – панорамні знімки приміщень, 3D-моделі приміщень, отримані внаслідок реконструкції; усі матеріали, що є результатом розв’язання проміжних задач реконструкції; Предмет дослідження – методи 3D-реконструкції, підхід Structure-from-Motion, його поведінка при різних прикладах вхідних даних, можливі кроки для покращення якості реконструкції; Мета роботи — побудувати алгоритм 3D-реконструкції на основі підходу Structure-from-Motion, для використання на різних прикладах вхідних даних. В роботі наведено програмну реалізацію математичної моделі, що дозволяє проводити дослідження у відповідності до мети роботи. Проведений порівняльний аналіз різних конфігурацій 3D-реконструкції. В роботі також наведено порівняння запропонованого метода із альтернативними підходами до 3D-реконструкції.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 Air Quality Estimation in Ukraine Using SDG 11.6.2 Indicator Assessment(Remote Sensing, 2021-11) Shelestov, Andrii; Yailymova, Hanna; Yailymov, Bohdan; Kussul, NataliiaДокумент 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 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, KlausThe 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.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 Biophysical Impact of Sunflower Crop Rotation on Agricultural Fields(Sustainability, 2022) Kussul, Nataliia; Deininger, Klaus; Shumilo, Leonid; Lavreniuk, Mykola; Ayalew Ali, Daniel; Nivievskyi, OlegДокумент Відкритий доступ Cloud Platforms and Technologies for Big Satellite Data Processing(Springer, 2023) Kussul, Nataliia; Shelestov, Andrii; Yailymov, BohdanThis 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.Документ Відкритий доступ Comparative analysis of classification techniques for topic-based biomedical literature categorisation(Frontiers Media S.A., 2023) Stepanov, Ihor; Ivasiuk, Arsentii; Yavorskyi, Oleksandr; Frolova, AlinaIntroduction: Scientific articles serve as vital sources of biomedical information, but with the yearly growth in publication volume, processing such vast amounts of information has become increasingly challenging. This difficulty is particularly pronounced when it requires the expertise of highly qualified professionals. Our research focused on the domain-specific articles classification to determine whether they contain information about drug-induced liver injury (DILI). DILI is a clinically significant condition and one of the reasons for drug registration failures. The rapid and accurate identification of drugs that may cause such conditions can prevent side effects in millions of patients. Methods: Developing a text classification method can help regulators, such as the FDA, much faster at a massive scale identify facts of potential DILI of concrete drugs. In our study, we compared several text classification methodologies, including transformers, LSTMs, information theory, and statistics-based methods. We devised a simple and interpretable text classification method that is as fast as Naïve Bayes while delivering superior performance for topic-oriented text categorisation. Moreover, we revisited techniques and methodologies to handle the imbalance of the data. Results: Transformers achieve the best results in cases if the distribution of classes and semantics of test data matches the training set. But in cases of imbalanced data, simple statistical-information theory-based models can surpass complex transformers, bringing more interpretable results that are so important for the biomedical domain. As our results show, neural networks can achieve better results if they are pre-trained on domain-specific data, and the loss function was designed to reflect the class distribution. Discussion: Overall, transformers are powerful architecture, however, in certain cases, such as topic classification, its usage can be redundant and simple statistical approaches can achieve compatible results while being much faster and explainable. However, we see potential in combining results from both worlds. Development of new neural network architectures, loss functions and training procedures that bring stability to unbalanced data is a promising topic of development.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 Crop Yield Forecasting for Major Crops in Ukraine(IEEE, 2021) Shelestov, Andrii; Shumilo, Leonid; Yailymova, Hanna; Drozd, SophiaДокумент Відкритий доступ Current Advances on Cloud-Based Distributed Computing for Forest Monitoring(Springer, 2023) Shelestov, Andrii; Salii, Yevhenii; Hordiiko, Nataliia; Yailymova, HannaOne of the most important tasks related to environmental protection is forests monitoring. Meanwhile, specialists deal with the problem of big data and the need to utilize powerful computing resources that are not always available. Cloud solutions (CREODIAS, Google Earth Engine, etc.) provide instant satellite data access and the ability to quickly and conveniently process geospatial data in the cloud and use it to search for information products. Forest monitoring is supported by the European Commission (EU project SWIFTT), the World Wildlife Fund and others. This work analyzes Sentinel-2 satellite spectral channels, which distribution of pixel values was constructed for diseased and healthy forests, and the possibility of separating these two classes was analyzed based on the Bhattacharya distance. The informativeness of time series application of the normalized difference vegetation index (NDVI) was analyzed. The assumption that the average value of NDVI decreases and the standard deviation increases when the forest changes is confirmed. Getting results for large areas will lead to a big data problem. Therefore, the structure of the pilot information system is proposed as the basis for a further cloud solution with the development of a machine (deep) learning model for forest monitoring in any territory (including Ukraine). This system allows monitoring forests dynamics based on time series of satellite data at the country level and worldwide. This will be an important step for Ukraine as a potential member of the EU in the field of providing information services and monitoring the most sensitive natural resources.Документ Відкритий 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, NataliiaДокумент 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.M