A Utility-Based Reputation Model for Grid Resource Management System

In this paper we propose extensions to the existing utility-based reputation model for virtual organizations (VOs) in grids, and present a novel approach for integrating reputation into grid resource management system. The proposed extensions include: incorporation of statistical model of user behaviour (SMUB) to assess user reputation; a new approach for assigning initial reputation to a new entity in a VO; capturing alliance between consumer and resource; time decay and score functions. The addition of the SMUB model provides robustness and dynamics to the user reputation model comparing to the policy-based user reputation model in terms of adapting to user actions. We consider a problem of integrating reputation into grid scheduler as a multi-criteria optimization problem. A non-linear trade-off scheme is applied to form a composition of partial criteria to provide a single objective function. The advantage of using such a scheme is that it provides a Pareto-optimal solution partially satisfying criteria with corresponding weights. Experiments were run to evaluate performance of the model in terms of resource management using data collected within the EGEE Grid-Observatory project. Results of simulations showed that on average a 45 % gain in performance can be achieved when using a reputation-based resource scheduling algorithm

Intelligent Computations for Flood Monitoring

Floods represent the most devastating natural hazards in the world, affecting more people and causing more property damage than any other natural phenomena. One of the important problems associated with flood monitoring is flood extent extraction from satellite imagery, since it is impractical to acquire the flood area through field observations. This paper presents a method to flood extent extraction from synthetic-aperture radar (SAR) images that is based on intelligent computations. In particular, we apply artificial neural networks, self-organizing Kohonen’s maps (SOMs), for SAR image segmentation and classification. We tested our approach to process data from three different satellite sensors: ERS-2/SAR (during flooding on Tisza river, Ukraine and Hungary, 2001), ENVISAT/ASAR WSM (Wide Swath Mode) and RADARSAT-1 (during flooding on Huaihe river, China, 2007). Obtained results showed the efficiency of our approach

Grid Approach to Satellite Monitoring Systems Integration

This paper highlights the challenges of satellite monitoring systems integration, in particular based on Grid platform, and reviews possible solutions for these problems. We describe integration issues on different levels: data integration level and task management level (job submission in terms of Grid). We show example of described technologies for integration of monitoring systems of Ukraine (National Space Agency of Ukraine, NASU) and Russia (Space Research Institute RAS, IKI RAN). Another example refers to the development of InterGrid infrastructure that integrates several regional and national Grid systems: Ukrainian Academician Grid (with Satellite data processing Grid segment) and RSGS Grid (Chinese Academy of Sciences)

Earth observation data science programs in National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"

Nowadays, satellite monitoring and geospatial intelligence are the drivers of digital transformation and economic development all over the world. At the same time, in Ukraine there is no higher education programs dealing with Earth observation data science or machine and deep learning on remote sensing data. In 2019, Space Research Institute in cooperation with the Department of Mathematical Modeling and Data Analysis (MMDA department) of National Technical University of Ukraine “Kyiv Polytechnic Institute” (NTUU “KPI”) joined the Copernicus Academy network for deeper involvement into educational activities related to the Copernicus program. As Copernicus Academy laboratory, we contribute into international scientific and innovative international programs, provide trainings and master classes for students, regional administrations and teachers. Most of our projects deal with machine learning on satellite and auxiliary data and satellite monitoring applications and require deep knowledge of mathematics, machine learning and data analysis. To facilitate involvement of students into our projects, in 2021 MMDA department established a certificate program “Models and methods of intellectual analysis of heterogeneous data” for master students of Applied Mathematics specialty (https://mmda.ipt.kpi.ua/en/certificate-program-models-and-methods-of-intellectual-analysis-of-heterogeneous-data/). It includes big geospatial data analysis, geospatial information technologies and deep learning for satellite and heterogeneous data. It allows students to dive into Earth observation domain and bridge the gap between applied mathematics and satellite monitoring. Students do their master’s research within international projects, in particular, Horizon-2020 e-shape or NASA project “High-Impact Hot Spots of Land Cover Land Use Change: Ukraine and Neighboring Countries”. They develop machine learning models for different applications based on Copernicus data and implement them on different cloud platforms, such as GEE, CREODIAS and AWS. Some of them develop their startup projects based on this research. For further development of our program and better motivation of our students we are interested in collaboration with similar programs for academic mobility of students and professors and looking for innovative educational forms and resources

U-Net Model for Logging Detection Based on the Sentinel-1 and Sentinel-2 Data

Illegal logging in Ukraine is a big problem that negatively affects both environmental and socio-economic indicators of the country. The main reason for this problem is the lack of independent control over the forest industry. Lack of control, in turn, makes it possible to provide inaccurate information about the permitted logging and to hide the fact of logging. The solution to this problem is the use of modern approaches of Remote Sensing and deep learning to implement mechanisms for forestry monitoring and logging detection based on the satellite data. Most researches on satellite-based logging detection technology are based on the optical satellite missions. However, for countries with temperate and cold climates, the use of such approaches is problematic in winter and autumn due to the lack of vegetative biomass and the high percentage of clouds and snow in satellite images. In this study, we assessed a methodology for detecting logging based on optical and radar images of Copernicus satellite missions, namely Sentinel-l and 2. The obtained results show that when using this approach, it is possible to monitor and detect logging with high accuracy both in summer and in winter with the frequency of data updates once a week. The basis of this methodology is a convolutional neural network with U -Net architecture, which input is a stack of optical and radar images in summer and spring, and works on radar images only in winter and autumn

Relationships Between Land Degradation and Climate Change Vulnerability of Agricultural Water Resources

According to the methodology for determining land degradation adopted by the UN for the calculation of the sustainable development goal's (SDG) indicator 15.3.1, land productivity on the basis of remote sensing data is one of the three sub-indicators. At the same time, the process of land degradation is very complex and it has not yet been studied how it is affected by climate changes. This task is complicated by the fact that climate change has consequences in the future. However, satellite data have a long history of observations and therefore we can see, how climate indicators affect the process of land degradation in historical terms. In this paper, we used MODIS satellite data to calculate land productivity and estimated the relationship between land productivity and climate change vulnerability of agricultural water resources (CCV) obtained by SWAT model for Ukraine. Correlation and regression analysis show that the climate change vulnerability of agricultural water resources is one of the indicators of land degradation

Air Quality Estimation in Ukraine Using SDG 11.6.2 Indicator Assessment

This research was funded by the National Research foundation of Ukraine within the project 2020.02/0284 «Geospatial models and information technologies of satellite monitoring of smart city problems», which won the competition “Leading and Young Scientists Research Support”.Ukraine is an associate member of the European Union, and in the coming years, it is expected that all the data and services already used by European Union countries will become available for Ukraine. An important program, which is the basis for building European monitoring services for smart cities, is the Copernicus program. The two most important services of this program are the Copernicus Land Monitoring Service (CLMS) and the Copernicus Atmosphere Monitoring Service (CAMS). CLMS provides important information on land use in Europe. In the context of smart cities, the most valuable tool is the Urban Atlas service, which is related to local CLMS services and provides a detailed digital city plan in vector form, which is segmented into small functional areas classified by Coordinate Information on the Environment (CORINE) nomenclature. The Urban Atlas is a geospatial layer with high resolution, built for all European cities with a population of more than 100,000. It combines high-resolution satellite data, city segmentation by blocks and functional urban areas (FUAs), important city infrastructure, etc. This product is used as a basis for city planning and obtaining analytics on the most important indicators of city development, including air quality monitoring. For Ukraine, such geospatial products are not provided under the Copernicus program. In this article, FUAs are developed for Ukrainian cities using European technology. It is important to start work on this program’s implementation as early as possible so that when the first city atlas appears, Ukraine will be ready to work with it together with the European community. This requires preparing the basis for national research and training national stakeholders and consumers to use this product. To make this happen, it is necessary to have a national geospatial product that can be used as an analogue of the city atlas. In this article, the authors analyzed the existing methods of air quality assessment and the Global Sustainable Development Goal (SDG) indicator 11.6.2, “Annual mean levels of fine particulate matter (e.g., PM2.5 and PM10) in cities (population weighted)”, achieved for European cities. Based on this, indicator 11.6.2 was then evaluated for the first time in Ukraine, considering the next 5 years. For the correct use of global products for Ukraine, CAMS global satellite data and population data (Global Human Settlement Layer and NASA population data) for Ukrainian cities were validated. These studies showed a statistically significant result and, therefore, demonstrated that global products can be used to monitor air quality both at the city level and for Ukraine as a whole. The obtained results were analyzed, and the values of indicator 11.6.2 for Ukraine were compared with those for other European countries

Is Soil Bonitet an Adequate Indicator for Agricultural Land Appraisal in Ukraine?

Agriculture land appraisal analysis is an important component of the land market. This task is especially essential for Ukraine, which plans to lift the moratorium on land transactions and legalize farmland sales in 2021. Most post-Soviet countries adopted the notion of a soil bonitet—a quantitative score representing natural soil fertility. This score is also proposed in Ukraine to perform agricultural land appraisals. However, this is a static parameter and does not account for the dynamics of actual crop production on the agricultural lands. Moreover, the bonitet score is not crop-specific. Therefore, in this study, we use maps of bonitet based on the soil map and natural-agricultural districts of Ukraine and crop yields at the village scale to explore the relationships between bonitet values and actual crop production in Ukraine. We found that land appraisal is not correlated with the actual soil bonitet

Validation of the Global Human Settlement Layer and NASA Population Data for Ukraine

The National Research Foundation of Ukraine project 2020.02/0284 “Geospatial models and information technologies of satellite monitoring of smart city problems”.In Ukraine, due to financial difficulties, the planned census is often postponed from year to year. The country is forced to rely on static data, which is sometimes completely untrue. In addition, it is not possible to count the number of inhabitants everywhere. In particular, Ukraine has not known for several years exactly how many people live in the occupied territories. As a result - the wrong distribution of the budget, which entails another financial crisis and a number of other troubles. At the same time there several satellite based products allowing to estimate the population. This study provide validation of satellite based population products delivered by JRC and NASA for the territory of Ukraine. To verify the correctness of the satellite based products, such as Global Human Settlement Layer (GHSL) and NASA population GPWv4 data collection have been compared to official statistics on the number of the largest cities of Ukraine