Although significant progress has been achieved in the development of spatio-temporal models that capture real-world phenomena, more interaction between phenomenon-based and business-based modeling approaches is still required. This represents a much...
The unprecedented development of Internet of Things (IoT) technology produces humongous amounts of spatio-temporal sensing data with various geometry types. However, processing such datasets is often challenging due to high-dimensional sensor data ge...
Many kinds of spatio-temporal data in our daily lives, such as the trajectory data of moving objects, stream natively. Streaming systems exhibit significant advantages in processing streaming data due to their distributed architecture, high throughpu...
With the advancement of intelligent manufacturing strategies, Cyber–Physical Production Systems (CPPSs) generate massive amounts of multidimensional, dynamic, and non-stationary data, posing significant challenges to real-time Process Monitorin...
A novel spatio-temporal 2-dimensional (2-D) processing method that can jointly estimate the transmitting-receiving azimuth and Doppler frequency for bistatic multiple-input multiple-output (MIMO) radar in the presence of spatial colored noise and an...
Accurate prediction of invasive species diffusion is essential for effective management and ecological conservation. Existing spatio-temporal Cox process models face limitations due to the separability assumption, which fails to capture spatio-tempor...
In this paper, we analyze the tornado occurrences in the Unites States. To perform inference procedures for the spatio-temporal point process we adopt a dynamic representation of Log-Gaussian Cox Process. This representation is based on the decomposi...
Governments are continuously developing strategies for policy implementation toward water resource protection. However, little is known about the practical application of such plans to test their effectiveness in policy practice. This study focused o...
The integrated recognition of spatio-temporal characteristics (e.g., speed, interaction with surrounding areas, and driving forces) of urbanization facilitates regional comprehensive development. In this study, a large-scale data-driven approach was...
Typhoon is one of the most destructive natural disasters in the world. Real-time information on the process of typhoon events serves as important reference for disaster emergency. In the era of big data, microblog text has been gradual applied to the...
Realistic modeling of pedestrian flow in dense public spaces is important for urban crowd geosimulation, mobility analysis, and indoor public-space geo-information modeling. Although prior research has emphasized microscopic agent interactions, highe...
Emergency calls are defined by an ever-expanding utilisation of information and sensing technology, leading to extensive volumes of spatio-temporal high-resolution data. The spatial and temporal character of the emergency calls is leveraged by author...
This paper follows up on a reference paper that inspired MDPI’s Topic “Stochastic Geomechanics: From Experimentation to Forward Modeling”, where global and local deformation effects on sand specimens are fully described from high re...
In this paper, a two-phase spatio-temporal point process (STPP) defined on a countable metric space and characterized by a conditional intensity function is introduced. In the first phase, the process is memoryless, generating completely random point...
Renewable energy generation data usually exhibit strong spatial correlations and temporal continuity, but measurement curves from wind and photovoltaic stations often contain sporadic missing entries and continuous missing segments due to communicati...
Immediate risk assessment and analysis are crucial in managing urban hazard events (UHEs). However, it is a challenge to develop an immediate risk assessment process (RAP) that can integrate distributed sensors and data to determine the uncertain mod...
Considering the massive influx of new energy into the power system, accurate wind speed prediction is of great importance to its stability. Due to the influence of sensor accuracy and harsh natural environments, there is inevitable noise interference...
Multivariate space–time datasets are often collected at discrete, regularly monitored time intervals and are typically treated as components of time series in environmental science and other applied fields. To effectively characterize such data...
Product quality control in chemical processes faces challenges from dynamic non-stationary data, underutilized variable spatial correlations, and overreliance on prior knowledge. This paper addresses these issues by proposing an enhanced Spatio-Tempo...
Aiming at the strong spatio-temporal coupling relationship between data in the actual industrial production process, which leads to the problem of insufficient reliability and poor timeliness of traditional process anomaly monitoring methods, a time...
The widespread availability of tools to collect and share spatial data enables us to produce a large amount of geographic information on a daily basis. This enormous production of spatial data requires scalable data management systems. Geospatial arc...
The theory of the optimal formation of coherent and incoherent images is developed using the foundations of the statistical theory of optimization of radio engineering information-measuring systems. The main operations necessary for synthesizing opti...
Robotic manipulators have to constantly deal with the complex task of detecting whether a grasp is stable or, in contrast, whether the grasped object is slipping. Recognising the type of slippage—translational, rotational—and its directio...
Operating power systems with large amounts of renewables requires predicting future photovoltaic (PV) production with fine temporal and spatial resolution. State-of-the-art techniques combine numerical weather predictions with statistical post-proces...
This paper, taking a typical agricultural village in China as an example, explored the evolution process and characteristics of rural population flows, capital flows and information flows since the reform and opening up in 1978, using a social survey...
The study aimed to generate informative data on solar radiation in order to establish sustainable solar energy that will support domestic needs and agricultural production and processing industries in Jubek State, South Sudan. Solar radiation intensi...
The performance of hydrological models in western China has been restricted due to the scarcity of meteorological observation stations in the region. In addition to improving the quality of atmospheric input data, the use hydrological models to analy...
Rock art paintings present high sensitivity to light, and an exhaustive evaluation of the potential color degradation effects is essential for further conservation and preservation actions on these rock art systems. Microfading spectrometry (MFS) is...
Service robots operating in indoor environments should recognize dynamic changes from sensors, such as RGB-depth (RGB-D) cameras, and recall the past context. Therefore, we propose a context query-processing framework, comprising spatio-temporal robo...
Accurate air quality forecasting requires the effective integration of heterogeneous data sources that vary in spatial coverage, temporal resolution, and sensing reliability. This paper presents a unified spatio-temporal data processing framework des...
16 November 2018
Understanding spatio-temporal differences in nitrogen (N) transformation, transport and reduction rates in water bodies is critical to achieve effective mitigation of river eutrophication. We performed culture experiments in six rivers in the Taihu B...
From the middle of the Northern Song Dynasty (960–1127), the Linji School became the main branch of the Southern Chan Buddhism. Understanding the historical significance of the Linji School is crucial for comprehending the origins and developme...
Knowing the GIS expression of geological phenomena is an important basis for the combination of geology and GIS. Regional geological structures include folds, faults, strata, rocks, and other typical geological phenomena and are the focus of geologic...
Stormwater quality modeling remains one of the most challenging issues in urban hydrology today. The processes involved in contaminant generation and transport are very complex, with many associated uncertainties, including uncertainty arising from p...
In this work, three new convolutional neural network models—spatio-temporal convolutional neural network versions 1 and 2 (ST_CNN_v1 and ST_CNN_v2), and the spatio-temporal dilated convolutional neural network (ST_Dilated_CNN)—are propose...
The processes of land use and cover change (LUCC) are highly diverse and complex, being heavily influenced by natural factors, economic factors, and other related factors. These changes have a significant impact on ecological environments and landsca...
This research work employs theoretical and empirical expert knowledge in constructing an agglomerative parallel processing algorithm that performs spatio-temporal clustering upon seismic data. This is made possible by exploiting the spatial and tempo...
Understanding cultivated land use transition (CLUT) requires analytical frameworks capable of capturing the interconnected changes in production inputs, land use structure, and multifunctional outcomes. However, existing CLUT studies often rely on fr...
Accurate wind speed forecasting is essential for the efficient integration of wind energy into power systems, as it directly influences generation scheduling, grid stability, and energy market operations. Forecast errors can lead to significant econo...
Analyzing and mining spatiotemporal processes refers to the extraction of geographic phenomena from spatiotemporal data and the analysis of available geographic knowledge and patterns. It finds applications in various fields such as natural disaster...
In the geographic environment, mesoscale ocean eddies and similar phenomena exhibit continuous and gradual changes. However, due to limitations in remote sensing observation technology, the obtained observational data are discrete, which contradicts...
Urban waterlogging is one of the major “diseases” faced by cities, posing a great challenge to the healthy and sustainable development of cities. The traditional geographic knowledge graph struggles to capture dynamic changes in urban wat...
13 November 2022
Green production is an inevitable choice for China’s high-quality economic development. With the rise of the digital technology revolution, China’s digital transformation may play an integral and important role in increasing green total f...
Statistical analysis and research on insect grooming behavior can find more effective methods for pest control. Traditional manual insect grooming behavior statistical methods are time-consuming, labor-intensive, and error-prone. Based on computer vi...
At present, small dim moving target detection in hyperspectral imagery sequences is mainly based on anomaly detection (AD). However, most conventional detection algorithms only utilize the spatial spectral information and rarely employ the temporal s...
Over the last decade, innovative computer technologies and the multiplication of geospatial data acquisition solutions have transformed the geographic information systems (GIS) landscape and opened up new opportunities to close the gap between GIS an...
The rapid spread of infectious diseases is a major public health problem. Recent developments in fighting these diseases have heightened the need for a contact tracing process. Contact tracing can be considered an ideal method for controlling the tra...
Irregular headways could reduce the public transit service level heavily. Finding out the exact causes of irregular headways will greatly help to develop efficient strategies aiming to improve transit service quality. This paper utilizes bus GPS data...
The work is devoted to solving current scientific and applied problems of the development of radar imaging methods. These developments are based on statistical theory of optimal signal processing. These developments allow researchers to create cohere...
Indoor fire is a sudden and frequent disaster that severely threatens the safety of indoor people worldwide. Indoor fire emergency evacuation is crucial to reducing losses involving various objects and complex relations. However, traditional studies...
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