The extraction of spatiotemporal features from video sequences allows for the recognition of actions and the analysis of behaviors in video, making it a key challenge in automated video processing. The literature shows widespread use of deep learning...
Real-time performance is important in rule-based continuous spatiotemporal query processing for risk analysis and decision making of target objects collected by sensors of combat vessels. The existing Rete algorithm, which creates a compiled node lin...
The availability of geodata with high spatial and temporal resolution is increasing steadily. Often, these data are continuously generated by distributed sensor networks and provided as geodata streams. Geostatistical analysis methods, such as spatio...
Scalable real-time processing of large amounts of data has become a research topic of particular importance due to the continuously rising amount of data that is generated by devices equipped with sensing components. While existing approaches allow f...
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...
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...
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...
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...
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...
Industrial process monitoring via soft sensors remains challenging due to time delays, strong disturbances, and time-varying characteristics. Therefore, a local sparse spatiotemporal window-driven stacked denoising autoencoder (LSSW-SDAE) soft sensor...
Spatiotemporal information processing within the human brain is done by a joint task of neurons and synapses with direct optical inputs. Therefore, to mimic this neurofunction using photonic devices could be an essential step to design future artific...
Multistep-ahead prediction of critical states in the iron ore sintering process is essential for maintaining production stability, enhancing energy efficiency, and reducing industrial emissions. However, large time delays, strong coupling, and condit...
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 solar photovoltaic (PV) capacity estimation requires high-resolution, site-specific solar irradiance data to account for localized variability. However, global datasets, such as the National Solar Radiation Database (NSRDB), provide regional...
Rice viral diseases adversely affect crop yield and quality. Most rice viruses are transmitted through insect vectors. However, the traditional whole-plant inoculation method cannot control the initial inoculation site in rice plants because the inse...
To investigate the role of potassium in the regulation of potato growth, dynamic changes in starch–sugar metabolism, and processing quality. In this study, “Gannong Potato No. 9” was used as the test material, and five potassium con...
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...
The availability of spatial and spatiotemporal big data is increasing rapidly. Spatially and temporally high resolved data are especially gathered via the Internet of Things. This data can often be accessed as data streams that push new data tuples c...
This paper introduces a new spike activation function (SPKAF) or spike membership function for fuzzy adaptive neurons (FAN), developed for decoding spatiotemporal information with spikes, optimizing digital signal processing. A solution with the adap...
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...
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...
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...
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...
In the study of spatiotemporal geographical phenomena, the space–time interpolation method is widely applied, and the demands for computing speed and accuracy are increasing. For nonprofessional modelers, utilizing the space–time interpol...
The construction of large-scale civil infrastructures requires massive spatiotemporal data to support the management and control of scheduling, quality control, and safety monitoring. Existing artificial-intelligence-based data processing algorithms...
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...
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...
Land use changes under natural and anthropogenic driving factors have spatiotemporal ecological consequences, and these need to be identified to protect biodiversity and the robustness of ecosystems. While driving factor research has mainly focused o...
High-fidelity characterization and effective monitoring of spatial and spatiotemporal processes are crucial for high-performance quality control of many manufacturing processes and systems in the era of smart manufacturing. Although the recent develo...
An event is an important medium for recording, expressing, and understanding the real world. Additionally, a data model can provide a digital and structured description method for the real world. Therefore, studying event data models is highly import...
The accurate prediction of sea surface temperature (SST) is essential for studying marine phenomena, understanding climate dynamics, and forecasting environmental changes. However, developing a general SST prediction model is challenging due to signi...
Many effective and advanced methods have been developed to explore oceanic dynamics using time series of raster-formatted datasets; however, they have generally been designed at a scale suitable for data observation and used independently of each oth...
In this paper, we study the effects of the shield tunnel construction on the deformation of an existing pipeline parallel to and above the new shield tunnel. We propose an analytical solution to predict the spatiotemporal deformation of the existing...
Agricultural sustainability is crucial for ensuring food security, promoting economic development, maintaining ecological balance, and achieving sustainable development goals. In this study, based on relevant theories of agricultural sustainability,...
Social media is increasingly being used to obtain timely flood information to assist flood disaster management and situational awareness. However, since data in social media are massive, redundant, and unstructured, it is tricky to intuitively and cl...
Existing research on quality gain-loss functions predominantly focuses on single variables or separable quality characteristics, overlooking the correlations among multiple quality attributes and the complexity of spatiotemporal factors. To address t...
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...
We present a novel statistical methodology for analyzing shifts in spatio-temporal fire occurrence patterns within the Brazilian Pantanal, utilizing remote sensing data. Our approach employs a Log-Gaussian Cox Process to model the spatiotemporal dyna...
Under the continuous disturbance of ecosystems driven by urbanization, landscape fragmentation and the disruption of ecological processes and functions are key challenges in optimizing ecological networks (EN). This study aims to examine the spatiote...
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...
The Qiandao Lake Basin (QLB), which occupies low hilly terrain in the monsoon region of southeastern China, is facing serious environmental challenges due to human activities and climate change. Here, we investigated source attribution, transport pro...
Forest fires are a natural phenomenon which might have severe implications on natural and anthropogenic ecosystems. Future projections predict that, under a climate change environment, the fire season would be lengthier with higher levels of droughts...
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...
Infrared imaging technology has been widely adopted for industrial gas leak detection due to its capability for large field-of-view, long-range, and dynamic monitoring. However, in practical applications, natural object interference within the scene,...
High-density polyethylene (HDPE) pipes are widely used for urban natural gas transportation. Pipes are usually welded using the technique of thermal butt fusion, which is prone to manufacturing defects that are detrimental to safe operation. This pap...
The residential sector has become the second largest energy consumer in China. Urban residential energy consumption (URE) in China is growing rapidly in the process of urbanization. This paper aims to reveal the spatiotemporal dynamic evolution and i...
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