This 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.

This 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.

This 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.

Accurately 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.

This study proposes a novel preprocessing approach for improving oil spill detection from Synthetic Aperture Radar (SAR) satellite imagery using deep learning models. A transfer learning approach with the LinkNet segmentation architecture pre-trained on ImageNet is employed. The model is trained on Sentinel-1 SAR data from 2018–2023 using a designed preprocessing pipeline that converts the single-channel SAR input to a 3-channel RGB image. The proposed preprocessing involves transforming the original SAR intensity values to a normal distribution, extracting nonlinear features, and encoding them into the RGB channels. Quantitative results on a test set show the preprocessed model achieves an improvement of 0.038 in F1-score and 0.054 in Intersection over Union compared to the original dB-scale preprocessing approach. Qualitative evaluation on independent SAR scenes from the Mediterranean Sea also demonstrates the model's ability to generalize to new geographic areas after training on data from other regions. The proposed preprocessing technique shows promising performance gains for automatic oil spill segmentation from SAR imagery and potential for integration with other preprocessing methods and task-specific neural network architectures.

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).

1stJuly 2021 the law on the creation of land market start effect in Ukraine. As a result, land appraisal became cornerstone task in Ukrainian agriculture sector. The official methodology on land appraisal includes use of soil fertility characteristics combined with coefficients related to the distance to the infrastructure objects or settlements and placing of field in specific functional areas, like recreational, or areas with high level of radiation pollution. In this study we collected open source infostructure geospatial information and characteristics of fields obtained from remote sensing data - crop types and Normalized Difference Vegetation Index to build land price predictive model trained on the official land market information. This work designed to investigate potential of geo-informational technologies and remote sensing in the land appraisal use. We separated all available ground truth land price data into three groups by fields size - very small, small, medium and big. We found different relationships between field characteristics and prices. For very small fields the most important features are area, altitude, slope, bonitet and distances to elevators, villages and roads. For small fields the most important are bonitet, altitude, area and distances to cities and roads. For medium and big field's area, slope, distance to cities, roads and historical NDVI.

This paper analyzes the problem of fire danger assessment and identifies the necessary sources and characteristics of satellite, ground and statistical data for the new approach of fire danger assessment. Modern information systems for fire danger assessment and fire monitoring using satellite and weather data are considered. The fire danger assessment method has been adapted for all types of land cover in Ukraine, which previously was successfully used in the Canadian methodology for determining the Fire Weather Index (FWI).

In the past few years, medium and high-resolution data became freely available for downloading. It provides great opportunity for researchers not to select between solving the task with high-resolution data on small territory or on global scale, but with low-resolution satellite images. Due to high spectral and spatial resolution of the data, Sentinel-1 and Sentinel-2 are very popular sources of information. Nevertheless, in practice if we would like to receive final product in 10 m resolution we should use bands with 10 m resolution. Sentinel-2 has four such bands, but also has other bands, especially red-edge 20 m resolution bands that are useful for vegetation analysis and often are omitted due to lower resolution. Thus, in this study we propose methodology for enhancing resolution (super-resolution) of the existing low-resolution images to higher resolution images. The main idea is to use advanced methods of deep learning - Generative Adversarial Networks (GAN) and train it to increase the resolution for the satellite images. Experimental results for the Sentinel-2 data showed that this approach is efficient and could be used for creating high resolution products.

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.

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.

In the past few years, deep learning methods developed and progressed in the many applied fields of science due to the appearance of large amounts of freely available data and improvement of computing resources. The task of crop mapping based on satellite data is not an exclusion. However, the appearance of clouds and shadows on the optical images cause difficulties on applying typical deep learning methods that recommend themselves in other issues. The easiest solution was to consider only images with a small percentage of clouds, but such way decreased the available data informativeness. In this study, we propose the new deep learning method based on recurrent neural network for efficient and precise crop mapping based on Sentinel-1 and Sentinel-2 imagery. The main idea of the study is to utilize all available information from the satellites and to provide an opportunity for neural network to extract the necessary features without any expert knowledge. Taking into account that regular feedforward neural networks could not deal effectively with such issue, authors suggest recurrent neural network with long short-term memory cells. Experimental results for the part of Kyiv region showed that this approach is rather efficient and outperformed traditional machine learning approaches and deep U-net architecture in terms of overall accuracy.

Ukrainian agricultural industry is one of the main sectors of economic growth. Nevertheless, Ukraine is way behind in the development. This is mostly due to the low level of modern technologies utilization by businesses and state entities. To ensure transparency, equity and reliability of Ukrainian land market, objective information on land use and crop state is required. The World Bank program “Supporting Transparent Land Governance in Ukraine” addresses these issues. Within the project, we performed satellite monitoring of land use in Ukraine, analyzed the feasibility of Google's cloud-based technology for processing large amount of data and developed a new platform to analyze the crop state using open and free Sentinel-1/2 satellite data. It is a 5-year project, which is extended for the whole country this year. We plan to make the technology of satellite monitoring operational and deployed in governmental institutions in 2023.

Built-up area mapping is essential task for Sustainable Development Goals (SDGs) indicators related to sustainable cities and communities. The calculation of indicator 11.3.1: “Ratio of land consumption rate to population growth rate” can be used by governments and decision makers for efficient smart city growth monitoring and planning. In this work, the built-up area map based on local data were built by using land cover classification approach for smart cities, developed in Horizon-2020 ERA-Planet SMURBS project. These maps were validated and compared with use of Global Human Settlement Layer, ground truth data and Maryland Forest product. The results shows that global products, such as Global Human Settlement Layer are very useful and have good accuracy especially in the case for global indicator 11.3.1 assessment. It can show the full picture of global urbanization changes. But, it is better to use local data for city scale, to provide accurate tracking of urban area development. Local data in this case could be more informative for the decision makers in purpose of the city growth management and proper use of environmental resources.

For accurate crop classification, it is necessary to use time-series of high-resolution satellite data to better discriminate among certain crop types. This task brings the following challenges: a large amount of satellite data for download, Big data processing and computational resources for utilization of state-of-the-art classification approaches. For solving these problems, we have developed an automated crop classification workflow, which is based on machine-learning techniques. By deployment of the workflow on the cloud platform, we can overcome challenges of Big data downloading and processing. In this paper, we present the system architecture and describe the experiments on structural and parametric identification of machine learning models utilized in the system.

For evaluating how far we are from achieving the Sustainable Development Goals and how big the progress is a global indicator framework was developed by the Inter-Agency and Expert Group on Sustainable Development Goals Indicators (IAEG-SDGs). In this paper, we propose an improved methodology for calculating indicator 2.4.1 "Proportion of agricultural area under productive and sustainable agriculture" within the ERA-PLANET Horizon 2020 project "The European Network for Observing our Changing Planet". The main improvements are in using accurate 10 m resolution crop classification map and Leaf Area Index derived from satellite data and WOFOST biophysical model. Based on proposed methodology indicator 2.4.1 has been calculated for the territory of Ukraine.

This study focuses on winter wheat yield assessment from NASA's Harmonized Landsat Sentinel-2 (HLS) product and meteorological observations through phenological fitting. Vegetation indices (VIs), namely difference vegetation index (DVI), normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI2), extracted from satellite optical data, are fitted per pixel against accumulated growing degree days (AGDD) using a quadratic function. Accumulated VIs are correlated against winter wheat yields. Results show a better performance from DVI compared to NDVI and EVI2.

Crop classification maps based on high resolution remote sensing data are essential for supporting sustainable land management. The most challenging problems for their producing are collecting of ground based training and validation datasets, non-regular satellite data acquisition and cloudiness. To increase the efficiency of ground data utilization it is important to develop classifiers able to be trained on the data collected in the previous year. In this study, we propose new deep learning method for providing crop classification maps using in-situ data that has been collected in the previous year. Main idea of the study is to utilize deep learning approach based on sparse autoencoder. At the first stage it is trained on satellite data only and then neural network fine-tuning is conducted based on in-situ data form the previous year. Taking into account that collecting ground truth data is very time consuming and challenging task, the proposed approach allows us to avoid necessity for annual collecting in-situ data for the same territory. Experimental results for the territory of Ukraine show that this technique is rather efficient and provides reliable crop classification maps with overall accuracy higher than 85.9%.

In this paper, we propose a novel method for an object-based post-classification filtering, specifically tailored to improve agricultural land use maps. That has significant impact on the solving other applied tasks like detection of land cover changes and crop rotation violation, area estimation and crop yield forecasting. The main idea of this method is to divide classification map into separate objects (group of pixels with the same class value) and investigate the properties of them, taking into account the specificity of each class, independently. The most challenging task in post-classification filtering is preserving edges and boundaries between different fields. Often these boundaries are narrow and some traditional filters tend to treat this like noise and remove them. To deal with this, our method identifies boundaries of objects like crop fields, based on a modified version of the Sobel algorithm. The accuracy and effectiveness of our method has been tested and compared with other methods, based on accuracy assessments and visual comparison.

Data provided by synthetic aperture radar (SAR) of Sentinel satellite can be useful for many applications. However, as for any SAR image, speckle noise is present in acquired images. Speckle properties are important for different operations of SAR image processing as filtering, edge detection, segmentation, classification. Thus, we first carry out preliminary analysis of speckle statistics and show that speckle PDF is quite close to Gaussian whilst noise is of practically multiplicative nature. Second, spatial correlation properties of speckle are analyzed. The study is performed in local DCT domain. This is done since then the obtained 2D spectrum is employed in image despeckling based on DCT. Peculiarities of several possible approaches to despeckling are discussed. Several examples for one component and dual polarization data are presented.

Agriculture is one of the key areas where Remote Sensing (RS) techniques can be efficiently implemented for solving wide range of tasks (crop mapping, crop monitoring, crop yield forecasting etc.) on regular basis. Sentinel mission represents really new opportunities in agricultural domain - free of charge for non-commercial use satellite images with 10-20 m spatial resolution, 5-day revisit frequency with global coverage and compatibility to the Landsat missions. In this paper we present the results of Sentinel-2 national demonstration project in Ukraine executed during vegetation period of 2016 and coordinated by Universite catholique de Louvain (UCL). Within this demonstration Ukraine was selected as one of three sites for national demonstration due to high variability of agroclimatic conditions, relatively big fields and wide range of major crops over the territory of the country.

Ukraine is a large agricultural country situated in Eastern Europe (603,500 km2). Nowadays in Ukraine, there is no any land market due to the moratorium on land sales. Nevertheless, in all areas preparation for land market is undergoing. Cropland productivity assessment based on satellite data is a challenging task for Ukraine because of a large territory and big diversity of agricultural crops. Cropland productivity is one of the major factors for forming the land price. In this paper, we aim to provide land productivity maps based on analysis of MODIS and Landsat-8 data due to availability long term time-series of Normalized Difference Vegetation index (NDVI) from sensors aboard those remote sensing satellites. Taking into account the huge amount of satellite products to be analyzed, in the study we propose to exploit the Google Earth Engine (GEE) cloud platform. It was found that land productivity maps provided from MODIS data for different time periods are strongly correlated. The experiment shows that land productivity maps should have high resolution. That is why, Landsat-8 data is more appropriate for land market purpose, despite of some bias in values comparing to results based on MODIS data. Comparing crop mask from ESA Sen2Agri project and obtained results it was found the dependence of land productivity value and crop/non-crop cover. It was found that irrigated fields from the south part of the study area are the most productive lands in Ukraine.

For many applied problems in agricultural monitoring and food security it is important to provide reliable crop classification maps in national or global scale. Large amount of satellite data for large scale crop mapping generate a “Big Data” problem. The main idea of this paper was comparison of pixel-based approaches to crop mapping in Ukraine and exploring efficiency of the Google Earth Engine (GEE) cloud platform for solving “Big Data” problem and providing high resolution crop classification map for large territory. The study is carried out for the Joint Experiment of Crop Assessment and Monitoring (JECAM) test site in Ukraine covering the Kyiv region (North of Ukraine) in 2013. We found that Google Earth Engine (GEE) provided very good performance in enabling access to remote sensing products through the cloud platform, but our own approach based on ensemble of neural networks outperformed SVM, decision tree and random forest classifiers that are available in GEE.

Deep learning (DL) is a powerful state-of-the-art technique for image processing including remote sensing (RS) images. This letter describes a multilevel DL architecture that targets land cover and crop type classification from multitemporal multisource satellite imagery. The pillars of the architecture are unsupervised neural network (NN) that is used for optical imagery segmentation and missing data restoration due to clouds and shadows, and an ensemble of supervised NNs. As basic supervised NN architecture, we use a traditional fully connected multilayer perceptron (MLP) and the most commonly used approach in RS community random forest, and compare them with convolutional NNs (CNNs). Experiments are carried out for the joint experiment of crop assessment and monitoring test site in Ukraine for classification of crops in a heterogeneous environment using nineteen multitemporal scenes acquired by Landsat-8 and Sentinel-1A RS satellites. The architecture with an ensemble of CNNs outperforms the one with MLPs allowing us to better discriminate certain summer crop types, in particular maize and soybeans, and yielding the target accuracies more than 85% for all major crops (wheat, maize, sunflower, soybeans, and sugar beet).

Along the season crop classification based on satellite data is challenging task for Ukraine because of a big diversity of different agricultural crops with different phenology (crop calendars). Taking into account the availability for free of high resolution (10 to 30 meter) optical and SAR data from different satellite, the most resource consuming task is ground data collecting. That is why the proper time of ground surveys and crop classification maps developing is very important. In the study we propose to build three crop classification maps for JECAM Ukraine test site in Kyiv region during the vegetation season. The first one is built in the middle of May to classify winter cereals and rapeseeds. The next crop classification map is developing in July to discriminate major summer crops (spring cereals, maize, soybeans, sunflowers). The final crop map is built in autumn to refine summer crops and sugar beet discrimination. Time series of multi-temporal satellite images with restored missing (clouded and shadowed) data are classified using neural network approach, in particular ensemble of multi-layer perceptrons (MLPs). It is shown, that addition of satellite data from the end of previous year to the spring imagery allows to significantly improve the accuracy of winter crops classification. In July it is possible to deliver the map with major summer crops with overall accuracy higher than 87%, and the overall accuracy of final map at the end of the season is 94%.

In the paper we propose the methodology for solving the large scale classification and area estimation problems in the remote sensing domain on the basis of deep learning paradigm. It is based on a hierarchical model that includes self-organizing maps (SOM) for data preprocessing and segmentation (clustering), ensemble of multi-layer perceptrons (MLP) for data classification and heterogeneous data fusion and geospatial analysis for post-processing. The proposed methodology is applied for generation of high resolution land cover and land use maps for the territory of Ukraine from 1990 to 2010 and 2015.

Many applied Earth observation problems are based on land cover and land use maps, derived from satellite data. That is why it is important to assess their accuracy. We have developed retrospective regional 30 meter resolution land cover maps for Ukraine based on Landsat data for 1990, 2000 and 2010. As there is no reference data for validating retrospective periods, validation of the maps could be done only with photo-interpretation. In this paper we investigate two different sampling schemes for reference samples selection: pseudo-random (purposeful) samples selection (first approach) and systematic on regular grid (second approach). With systematic samples selection we receive the lower accuracy of classification (overall, user and producer), then with pseudo-random Nevertheless we consider the validation results with the systematic sampling scheme (the second approach) to be more reliable comparing to the first one, because the second sampling scheme is less subjective. Moreover, samples proportion within the second approach better corresponds to the statistics.

In this paper, we propose a new approach to pixel and parcel-based classification of multi-temporal optical satellite imagery. We first restore missing data due to clouds and shadows based on vector and raster data fusion in different phases of classification methodology. Pixel-based classification maps are derived from an ensemble of neural networks, in particular multilayer perceptrons (MLPs). The proposed approach is applied for regional scale crop classification using multi-temporal Landsat-8 images for the JECAM site in the Kyivska oblast of Ukraine in 2013. The obtained results on crop area estimates are also compared to official statistics.

In this paper we propose a new methodology to automatically generate retrospective high resolution land cover maps on a regular basis for the whole territory of Ukraine. An ensemble of neural networks, in particular multilayer perceptrons (MLPs), is used for multi-temporal Landsat-4/5/7 satellites imagery classification with previously restored missing data due to clouds, shadows and non-regular coverage. This methodology was used to obtain land cover maps for the territory of Ukraine for three decades, namely 1990s, 2000s and 2010s, with overall accuracy more than 97%.

In this paper, a new method for automatic identification of ground control points (GCPs) on optical remote sensing images is presented. An elastic Radial Basis Function (RBF) neural network based model for nonlinear coordinate transformation and image rectification is proposed. The new method can be used to produce dense fields of about thousands of GCPs per image to train highly deformable transformation models. As a result, an accuracy improvement of order of 4 in comparison with the Automated Precise Orthorectification Package (AROP) can be obtained. The proposed method is applied for the Ukrainian remote sensing satellite Sich-2. The obtained average RMSE error by the new method for Sich-2 images is estimated at 17.8 m.

In this paper, we focus on assessing efficiency of using different satellite-derived parameters (biophysical variables), such as NDVI, FAPAPR, and VHI, for crop yield forecasting for Ukraine. The main objective of this study is to select the optimal parameters using a rigorous feature selection procedure based on random forest and cross validation. Empirical models incorporating different parameters are built, and used in the operational framework. The forecasts that are available 2-3 months prior harvest are compared to official statistics for the years 2011-2013.

In this paper, we focus on the application of satellite synthetic-aperture radar (SAR) images for discriminating summer crops in Ukraine within the JECAM project. Both optical (EO-1/ALI) and SAR (RADARSAT-2) images are used in order to assess impact adding SAR images for classification purposes. Three different classifiers, in particular neural networks, support vector machine and decision trees, are applied with neural networks giving the best overall accuracy. It is found that major impact of using SAR images is for sunflower and sugar beet classes while there was no gain for other crops (maize and soybeans).

In this paper, we focus on quantitative drought risk assessment using satellite data. Methods of the extreme value theory (EVT) are applied for a time-series of vegetation health index (VHI) derived from NOAA satellites in order to provide drought hazard mapping. For this, a Poisson-GP (Generalized Pareto) model is applied for modelling VHI extreme values. The model allows estimation and mapping of return periods of different categories of drought severity. An approach to economical risk assessment due to droughts is presented. The derived drought hazard map is integrated with high resolution crop map to provide final estimates of risk. The proposed approach is implemented for quantitative assessment of drought risk for the Kyiv region in Ukraine.

Wheat is one of the most important and grown crops in Ukraine. Enabling reliable and accurate winter wheat yield forecasts several months in advance of the harvest is an important problem. In this paper we assess relative efficiency of using MODIS data to winter wheat yield forecasting in Ukraine at oblast level. Relative efficiency is defined as the ratio between the variance of the sample (in our case the winter wheat yield official statistics) and the variance of the estimate that has been made with the aid of satellite data. Performance of the forecasting models in terms of relative efficiency was dependant on the agroclimatic zone being on average 1.2 for Plane-Polissya, 1.5 for Forest-Steppe, and 1.9 for Steppe.

In this paper we discuss advantages and benefits of Sensor Web approach to flood monitoring and risk assessment. A general framework of using Sensor Web based services is discussed that incorporates heterogeneous data sources to provide disaster hazard mapping. Probability density function of the disaster is estimated based on the analysis of heterogeneous geospatial data. We use risk functional minimization theory that is developed within the theoretical framework known as a statistical learning theory. A particular case-study, the Namibia SensorWeb Pilot Project, of exploiting this framework is described.

In this paper we discuss results of a pilot study conducted by Ukrainian Space Research Institute of NASU-NSAU, in collaboration with the MARS team of the JRC, to explore the feasibility, cost-efficiency and specific difficulties of crop area estimation assisted by satellite remote sensing in Ukraine. The study compares the cost efficiency of several image types (MODIS, Landsat TM, AWiFS, LISS-III and RapidEye) combined with a field survey on a stratified sample of square segments. Additionally, field data were collected “along the road” as training data for image classification algorithms. The study shows that TM images from Landsat 5 yielded the best results, in spite of the old age of this sensor. Among the sensors that were tested, only MODIS and Landsat TM reach cost-efficiency thresholds.

A system is proposed for user activity monitoring in computer networks. The system is based on the use of neural networks and is implemented using agent approach. The monitoring system allows to detect anomalies in user activity, and consists of two components-on-line and off-line. On-line monitoring is carried out in real time and is used to predict the processes started by an user on the basis of previous ones. Off-line monitoring is carried out at the end of the day and is based on the analysis of statistical parameters of user behavior (user signature). Both on-line and off-line monitoring use neural network approach to detect anomalies in user behavior. Proposed system was verified on real data obtained in Intranet of Space Research Institute of NASU-NSAU and Institute of Physics and Technologies of National Technical University of Ukraine "Kiev Polytechnic Institute".