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Representing Earth remote sensing data as time series
P. 135–145.
To date, all remote sensing data are represented and stored as temporal sequences of separate “snapshots” – rasters or grids. This makes impossible to quickly obtain a time series of a variable values for the full available period for a region of a coordinate grid. Trend research – one of the most important topics in Earth science – becomes extremely complex and time consuming. This paper proposes an alternative data representation and corresponding storage technique. The data are represented as a collection of individual time series, one per each grid cell or raster pixel. New storage layout enables any time series to be always readily accessible. This approach considerably facilitates the application of existing time series techniques to remote sensing, climate reanalysis and similar data as well as provides new research and development opportunities not available before.
Language:
English
In book
Issue 1(2)–2(3). , Donetsk: Донецкий национальный технический университет, 2012.
Tsydypova M., Современные проблемы дистанционного зондирования Земли из космоса 2026 Т. 23 № 3 С. 263–275
The article presents a methodology for mapping coniferous forest decline, based on the integration of automated remote sensing data interpretation techniques, vegetation indices, remote sensing data on above-ground biomass (Biomass CCI (Climate Change Initiative)), and materials from ground-based forest pathology surveys using forests of the Khamar-Daban ridge (Republic of Buryatia) as a case study. The ...
Added: August 9, 2026
Karpukhin I., Savchenko A., , in: Proceedings of the AAAI Conference on Artificial Intelligence. AAAI-26: AAAI Technical Track on Planning, Routing, and Scheduling; AAAI Technical Track on Reasoning under Uncertainty; AAAI Technical Track on Search and Optimization. Main Track, volume 40 no. 43.: American Association for Artificial Intelligence (AAAI) Press, 2026. P. 22536–22544.
Long-horizon events forecasting is a crucial task across various domains, including retail, finance, healthcare, and social networks. Traditional models for event sequences often extend to forecasting on a horizon using an autoregressive (recursive) multi-step strategy, which has limited effectiveness due to typical convergence to constant or repetitive outputs. To address this limitation, we introduce DEF, a novel approach for simultaneous forecasting of ...
Added: June 17, 2026
Karacharovskiy V., Ларина У. С., Резмерица А. А., Социологические исследования 2026 № 7 С. 60–75
Based on the neural network approach, the decomposition of the index of social mood in Russia for the period 2004-2024 was carried out and their dynamics for this period was modeled. The following issues are discussed: (a) the nature of the social mood' shocks in Russia, based on their connection with the concepts of politics ...
Added: May 14, 2026
Ankudinov I., Социология: методология, методы, математическое моделирование 2025 № 61 С. 165–203
The changing political mood of Russians is a constant subject of interest for sociological agencies. With the development of the Internet, conventional questionnaire research began to be supplemented by online surveys and, despite some skepticism, by social media mining. This article attempts to adjust an accidental web-sample so as to bring its estimates closer to ...
Added: April 22, 2026
Hushchyn M., Arzymatov K., Derkach D., Machine Learning 2026 Vol. 115 Article 56
Moments when a time series changes its behavior are called change points. Occurrence of change point implies that the state of the system is altered and its timely detection might help to prevent unwanted consequences. In this paper, we present two change-point detection approaches based on neural networks and online learning. These algorithms demonstrate linear ...
Added: March 6, 2026
Ivanov S., Borisov V., Ali S. et al., , in: 2025 IEEE XVII International Scientific and Technical Conference on Actual Problems of Electronic Instrument Engineering (APEIE).: IEEE, 2025. Ch. 127 P. 1–7.
This paper investigates the problem of detecting slow refrigerant leaks in a data center cooling system using a graph neural network. The study addresses the challenge of early fault identification, proposing a method for constructing a topological graph based on the engineering diagram, the physical layout, and the cause-and-effect relationships in the cooling system. This ...
Added: December 19, 2025
Алескеров Ф. Т., Lola I. S., Asoskov D. et al., Вопросы экономики 2025 № 11 С. 143–157
The LC-curve method, a new approach to time series analysis, was applied to the composite Business Uncertainty Index (BUI), based on the results of regular Rosstat business surveys, which made it possible to analyze uncertainty trajectories across Russia's enlarged industries and sub-sectors using two index specifications: ex-ante (forecast) and ex-post (actual). The results of the ...
Added: October 13, 2025
O. I. Vendina, A. V. Sheludkov, Gritsenko A. A., Regional Research of Russia 2024 Vol. 14 No. Suppl 1 P. S10–S30
The article is based on the materials of the field research conducted in Krasnodar krai in the summer–autumn of 2023, as well as on the analysis of articles and projects devoted to the substantiation of spatial forms and borders of Russian urban agglomerations. The Krasnodar urban agglomeration was chosen for several reasons: (a) the intensive ...
Added: October 10, 2025
Moskovchenko D., Roman Fedorov, Fakhretdinov A., Urban Ecosystems 2025 No. 28 Article 155
The northern part of western Siberia, which comprises the Khanty-Mansiysk and Yamalo-Nenets Autonomous Districts of Tyumen Oblast, Russia, is one of the most urbanized areas exposed to the subarctic climate. Here, we study the specifc socio-natural interactions afected by the development of urban green infrastructure in such climatic zones as middle and northern taiga, as ...
Added: August 4, 2025
Ivanov P., Shtark M., Kozhevnikov A. et al., IEEE Access 2025 Vol. 13 P. 25186–25197
Fault detection and diagnosis (FDD) is a critical challenge in industrial processes aimed at minimizing risks such as safety hazards, costly downtime, and suboptimal production. Traditional supervised FDD methods offer great performance while heavily relying on large volumes of labeled data, whereas unsupervised methods do not depend on labeled data, though are inferior in performance ...
Added: April 29, 2025
Makeeva N., Прикладная эконометрика 2025 Т. 79 С. 27–49
The paper presents the results of an accuracy analysis of nowcasting models for Russia’s GDP and its components based on usage data for the period from the first quarter of 2014 to the third quarter of 2023. The novelty of the study lies in comparing the accuracy of various models — MIDAS, MFBVAR, DFM models, ...
Added: April 19, 2025
Shvedov A. S., Sviyazov V., В кн.: Системное моделирование социально-экономических процессов: труды 46-ой международной научной школы-семинара, г. Уфа, 9 - 15 октября 2023 г.: Воронеж: Истоки, 2024. С. 526–531.
The generalized autoregressive conditional heteroscedasticity model is widely applied to financial time series. There are further generalizations of this model. One of such generalizations is a combination of Takagi–Sugeno type fuzzy systems and autoregressive conditional heteroscedasticity models. The Takagi–Sugeno fuzzy systems advantage is that there is a standalone generalized autoregressive conditional heteroscedasticity model constructed for ...
Added: June 26, 2024
Yu. A. Dubnov, A. Yu. Popkov, Polishchuk V. Y. et al., Automation and Remote Control 2023 Vol. 84 No. 1 P. 64–81
Randomized machine learning focuses on problems with considerable uncertainty in data and models. Machine learning algorithms are formulated in terms of a functional entropylinear programming problem. We adapt these algorithms to forecasting problems on an example of the evolution of thermokarst lakes area in permafrost zones. Thermokarst lakes generate methane, a greenhouse gas affecting climate ...
Added: February 5, 2024
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The monograph presents research materials based on the results of assessment and analysis of the dynamic characteristics of time series of stock quotes. Optimal estimates were selected and justified, the applying of which makes possible to improve the quality and efficiency of the forecasting stock quotes, building and rebalancing of investment portfolios, risk management, etc. ...
Added: December 22, 2023
Sviyazov V., Control Sciences 2022 No. 6 P. 21–28
Volatility modeling and forecasting is a topical problem both in scientific circles and in the practice. This paper develops an approach combining the GARCH model and fuzzy logic. The Takagi–Sugeno fuzzy inference scheme is adopted to fuzzify an original autoregression model (the conditional heteroskedasticity model). As a result, several different local GARCH models can be ...
Added: December 6, 2023
Sviyazov V., Экономический журнал Высшей школы экономики 2023 Т. 27 № 3 С. 412–434
The problem of volatility forecasting with and without consideration of weekly seasonality effect (the weekend effect) is examined in this research. The question of the seasonality existence is understood in the following sense: do models, which incorporate seasonality, feature better forecasts? The fuzzy GARCH model, which accounts for a weekly seasonality effect is presented in ...
Added: October 28, 2023
Zelenin E., Gurinov A., Garipova S. et al., Journal of Maps 2023 Vol. 19 No. 1 Article 2252006
The Kamchatka Peninsula lies on the eastern active margin of Eurasia, adjacent to the Kuril-Kamchatka subduction zone. In this study, we provide a geomorphological map of the Central Kamchatka Depression – the largest sedimentary basin in Kamchatka and also in all the island arcs of the North Pacific. The depression extends along Kamchatka at latitudes ...
Added: October 27, 2023
Bessonov V. A., Вопросы статистики 2023 Т. 30 № 4 С. 84–95
The paper discusses the author's proposals for addressing the problem of achieving the comparability of Russian socio-economic indicators related to the change in the borders of the state as a result of the special military operation. The experience of ensuring indicators comparability when changing state borders, namely during the unification of Germany in 1990 and ...
Added: August 25, 2023
Shabanov N., Sergey A. Bartalev, Kobayashi H. et al., IEEE Transactions on Geoscience and Remote Sensing 2023 Vol. 61 Article 4405717
Forest is a multi-layered canopy, where overstory and understory implement different biogeochemical cycles, phenology and functional role. Remote sensing products typically estimate forest total Leaf Area Index (LAI), while few quantify its components. The theoretical understanding of foliage distribution between layers is still quite limited. In this study we’ve developed a semi-empirical model for decomposition ...
Added: July 5, 2023
Grigoryev T., Verezemskaya P., Krinitskiy M. et al., Remote Sensing 2022 Vol. 14 No. 22 Article 5837
Global warming has made the Arctic increasingly available for marine operations and created a demand for reliable operational sea ice forecasts to increase safety. Because ocean-ice numerical models are highly computationally intensive, relatively lightweight ML-based methods may be more efficient for sea ice forecasting. Many studies have exploited different deep learning models alongside classical approaches ...
Added: June 19, 2023
IEEE, 2023.
Added: March 22, 2023