Теоретико-правові основи забезпечення невтручання у приватне життя особи у кримінальному провадженні

Блищик, О. В. Теоретико-правові основи забезпечення невтручання у приватне життя особи у кримінальному провадженні / Олег Васильович Блищик // Актуальні проблеми сучасної науки в дослідженнях молодих учених : тези доп. учасників наук.-практ. конф. з нагоди святкування Дня науки (м. Харків, 14 трав. 2021 р.) / МВС України, Харків. нац. ун-т внутр. справ. – Харків : ХНУВС, 2021. – С. 23-27.Виявлено коло проблем, які не дозволяють у повній мірі забезпечити захист прав, свобод та законних інтересів особи у кримінальному провадженні. Наголошено на необхідність переосмислення законодавчих гарантій невтручання у приватне життя особи у кримінальному провадженні, і, насамперед, з точки зору їх кореляції з міжнародними гарантіями забезпечення цього права.A number of problems have been identified that do not allow to fully protect the rights, freedoms and legitimate interests of a person in criminal proceedings. The need to rethink the legislative guarantees of non-interference in a person's private life in criminal proceedings is emphasized, and, first of all, from the point of view of their correlation with international guarantees of ensuring this right.Выявлен круг проблем, не позволяющих в полной мере обеспечить защиту прав, свобод и законных интересов лица в уголовном производстве. Отмечено необходимость переосмысления законодательных гарантий невмешательства в частную жизнь лица в уголовном производстве, и, прежде всего, с точки зрения их корреляции с международными гарантиями обеспечения этого права

Забезпечення невтручання у приватне життя під час проведення слідчих (розшукових) дій

Блищик, О. В. Забезпечення невтручання у приватне життя під час проведення слідчих (розшукових) дій / Олег Васильович Блищик // Сучасні тенденції розвитку криміналістики та кримінального процесу в умовах воєнного стану: тези доп. Міжнар. наук.-практ. конф. (м. Харків, 25 листоп. 2022 р.) / МВС України, Харків. нац. ун-т внутр. справ, Ф-т № 1. – Харків : ХНУВС, 2022. – С. 70-73.Зазначено, що приватне (особисте) життя особи є невід’ємним правом кожного й перебуває під особливим правовим захистом. Втручання в приватне спілкування особи шляхом проведення негласних слідчих (розшукових) дій вимагає чіткого дотримання нормативних положень, встановлених кримінальним процесуальним законодавством.It is noted that a person's private (personal) life is an inalienable right of everyone and is under special legal protection. Interference in a person's private communication by conducting covert investigative (search) actions requires strict compliance with the regulatory provisions established by the criminal procedural legislation.Отмечено, что частная (личная) жизнь личности является неотъемлемым правом каждого и находится под особой правовой защитой. Вмешательство в личное общение лица путем проведения негласных следственных (розыскных) действий требует четкого соблюдения нормативных положений, установленных уголовным процессуальным законодательством

Improved Estimates of Biomass Expansion Factors for Russian Forests

Biomass structure is an important feature of terrestrial vegetation. The parameters of forest biomass structure are important for forest monitoring, biomass modelling and the optimal utilization and management of forests. In this paper, we used the most comprehensive database of sample plots available to build a set of multi-dimensional regression models that describe the proportion of different live biomass fractions (i.e., the stem, branches, foliage, roots) of forest stands as a function of average stand age, density (relative stocking) and site quality for forests of the major tree species of northern Eurasia. Bootstrapping was used to determine the accuracy of the estimates and also provides the associated uncertainties in these estimates. The species-specific mean percentage errors were then calculated between the sample plot data and the model estimates, resulting in overall relative errors in the regression model of −0.6%, −1.0% and 11.6% for biomass conversion and expansion factor (BCEF), biomass expansion factor (BEF), and root-to-shoot ratio respectively. The equations were then applied to data obtained from the Russian State Forest Register (SFR) and a map of forest cover to produce spatially distributed estimators of biomass conversion and expansion factors and root-to-shoot ratios for Russian forests. The equations and the resulting maps can be used to convert growing stock volume to the components of both above-ground and below-ground live biomass. The new live biomass conversion factors can be used in different applications, in particular to substitute those that are currently used by Russia in national reporting to the UNFCCC (United Nations Framework Convention on Climate Change) and the FAO FRA (Food and Agriculture Organization’s Forest Resource Assessment), among others

Правова природа електронних доказів (кримінальний процесуальний аспект)

Абламська, В. В. Правова природа електронних доказів (кримінальний процесуальний аспект) / Вікторія Вікторівна Абламська, Олег Васильович Блищик // Актуальні проблеми сучасної науки в дослідженнях молодих учених, курсантів та студентів в умовах воєнного стану : тези доп. Всеук. наук.-практ. конф. (м. Харків, 22 черв. 2022 р.) / МВС України, Харків. нац. ун-т внут. справ, Наук. парк «Наука та безпека». – Харків : ХНУВС, 2022. – С. 22-25.З точки зору кримінального процесуального права, розкрито правову природу електронних доказів.From the point of view of criminal procedural law, the legal nature of electronic evidence is disclosed.С точки зрения уголовного процессуального права, раскрыта правовая природа электронных доказательств

Methodology for generating a global forest management layer

The first ever global map of forest management was generated based on remote sensing data. To collect training data, we launched a series of Geo-Wiki (https://www.geo-wiki.org/) campaigns involving forest experts from different world regions, to explore which information related to forest management could be collected by visual interpretation of very high-resolution images from Google Maps and Microsoft Bing, Sentinel time series and normalized difference vegetation index (NDVI) profiles derived from Google Earth Engine. A machine learning technique was then used with the visually interpreted sample (280K locations) as a training dataset to classify PROBA-V satellite imagery. Finally, we obtained a global wall-to-wall map of forest management at a 100m resolution for the year 2015. The map includes classes such as intact forests; forests with signs of management, including logging; planted forests; woody plantations with a rotation period up to 15 years; oil palm plantations; and agroforestry. The map can be used to deliver further information about forest ecosystems, protected and observed forest status changes, biodiversity assessments, and other ecosystem-related aspects

Global forest management data for 2015 at a 100 m resolution

Spatially explicit information on forest management at a global scale is critical for understanding the status of forests, for planning sustainable forest management and restoration, and conservation activities. Here, we produce the first reference data set and a prototype of a globally consistent forest management map with high spatial detail on the most prevalent forest management classes such as intact forests, managed forests with natural regeneration, planted forests, plantation forest (rotation up to 15 years), oil palm plantations, and agroforestry. We developed the reference dataset of 226 K unique locations through a series of expert and crowdsourcing campaigns using Geo-Wiki (https://www.geo-wiki.org/). We then combined the reference samples with time series from PROBA-V satellite imagery to create a global wall-to-wall map of forest management at a 100 m resolution for the year 2015, with forest management class accuracies ranging from 58% to 80%. The reference data set and the map present the status of forest ecosystems and can be used for investigating the value of forests for species, ecosystems and their services

Drivers of tropical forest loss between 2008 and 2019

During December 2020, a crowdsourcing campaign to understand what has been driving tropical forest loss during the past decade was undertaken. For 2 weeks, 58 participants from several countries reviewed almost 115 K unique locations in the tropics, identifying drivers of forest loss (derived from the Global Forest Watch map) between 2008 and 2019. Previous studies have produced global maps of drivers of forest loss, but the current campaign increased the resolution and the sample size across the tropics to provide a more accurate mapping of crucial factors leading to forest loss. The data were collected using the Geo-Wiki platform (www.geo-wiki.org) where the participants were asked to select the predominant and secondary forest loss drivers amongst a list of potential factors indicating evidence of visible human impact such as roads, trails, or buildings. The data described here are openly available and can be employed to produce updated maps of tropical drivers of forest loss, which in turn can be used to support policy makers in their decision-making and inform the public

A crowdsourced global data set for validating built-up surface layers

Several global high-resolution built-up surface products have emerged over the last five years, taking full advantage of open sources of satellite data such as Landsat and Sentinel. However, these data sets require validation that is independent of the producers of these products. To fill this gap, we designed a validation sample set of 50 K locations using a stratified sampling approach independent of any existing global built-up surface products. We launched a crowdsourcing campaign using Geo-Wiki (https://www.geo-wiki.org/) to visually interpret this sample set for built-up surfaces using very high-resolution satellite images as a source of reference data for labelling the samples, with a minimum of five validations per sample location. Data were collected for 10 m sub-pixels in an 80 × 80 m grid to allow for geo-registration errors as well as the application of different validation modes including exact pixel matching to majority or percentage agreement. The data set presented in this paper is suitable for the validation and inter-comparison of multiple products of built-up areas

Estimating the Global Distribution of Field Size using Crowdsourcing

There is increasing evidence that smallholder farms contribute substantially to food production globally yet spatially explicit data on agricultural field sizes are currently lacking. Automated field size delineation using remote sensing or the estimation of average farm size at subnational level using census data are two approaches that have been used. However, both have limitations, e.g. automatic field size delineation using remote sensing has not yet been implemented at a global scale while the spatial resolution is very coarse when using census data. This paper demonstrates a unique approach to quantifying and mapping agricultural field size globally using crowdsourcing. A campaign was run in June 2017 where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo-Wiki application. During the campaign, participants collected field size data for 130K unique locations around the globe. Using this sample, we have produced the most accurate global field size map to date and estimated the percentage of different field sizes, ranging from very small to very large, in agricultural areas at global, continental and national levels. The results show that smallholder farms occupy up to 40% of agricultural areas globally, which means that, potentially, there are many more smallholder farms in comparison with the two different current global estimates of 12% and 24%. The global field size map and the crowdsourced data set are openly available and can be used for integrated assessment modelling, comparative studies of agricultural dynamics across different contexts, for training and validation of remote sensing field size delineation, and potential contributions to the Sustainable Development Goal of Ending hunger, achieve food security and improved nutrition and promote sustainable agriculture

Estimating the Global Distribution of Field Size using Crowdsourcing

There is increasing evidence that smallholder farms contribute substantially to food production globally yet spatially explicit data on agricultural field sizes are currently lacking. Automated field size delineation using remote sensing or the estimation of average farm size at subnational level using census data are two approaches that have been used. However, both have limitations, e.g. automatic field size delineation using remote sensing has not yet been implemented at a global scale while the spatial resolution is very coarse when using census data. This paper demonstrates a unique approach to quantifying and mapping agricultural field size globally using crowdsourcing. A campaign was run in June 2017 where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo-Wiki application. During the campaign, participants collected field size data for 130K unique locations around the globe. Using this sample, we have produced the most accurate global field size map to date and estimated the percentage of different field sizes, ranging from very small to very large, in agricultural areas at global, continental and national levels. The results show that smallholder farms occupy up to 40% of agricultural areas globally, which means that, potentially, there are many more smallholder farms in comparison with the two different current global estimates of 12% and 24%. The global field size map and the crowdsourced data set are openly available and can be used for integrated assessment modelling, comparative studies of agricultural dynamics across different contexts, for training and validation of remote sensing field size delineation, and potential contributions to the Sustainable Development Goal of Ending hunger, achieve food security and improved nutrition and promote sustainable agriculture