Spatio-temporal Method Research Articles

Functional blindsight and its diagnosis.

Even when brain scans fail to detect a striate lesion, functional evidence for blindsight can be adduced. In the aftermath of an automobile accident, JK became blind. Results of ophthalmic exams indicated that the blindness must be cortical. Nevertheless, multiple MRI scans failed to detect structural damage to the striate cortex. Prior to the accident JK had been an athlete; after the accident he retained some athletic abilities, arousing suspicions that he might be engaged in fraud. His residual athletic abilities-e.g., hitting a handball or baseball, or catching a Frisbee-coupled with his experienced blindness, suggested blindsight. But due to the apparent absence of striate lesions, we designed a series of tasks for temporal and spatial dimensions in an attempt to detect functional evidence of his disability. Indeed, test results revealed compelling neural evidence that comport with his subjective reports. This spatiotemporal task-related method that includes contrasts with healthy controls, and detailed understanding of the patient's conscious experience, can be generalized for clinical, scientific and forensic investigations of blindsight.

Extended-Range Forecast of Winter Rainfall in the Yangtze River Delta Based on Intra-Seasonal Oscillation of Atmospheric Circulations

The Yangtze River Delta (YRD) is an important economic region in China. Heavy winter rainfall may pose serious threats to city operations. To ensure the safe operation of the city, meteorological departments need to provide forecast results for the Spring Festival travel rush weather service. Therefore, the extended-range forecast of winter rainfall is of considerable importance. To solve this problem, based on the analysis of low-frequency rainfall and the intra-seasonal oscillation of atmospheric circulation, an extended-range forecast model for winter rainfall is developed using spatiotemporal projection methods and is applied to a case study from 2020. The results show that: (1) The precipitation in the YRD during the winter has a significant intra-seasonal oscillation (ISO) with a periodicity of 10–30 d. (2) The atmospheric circulations associated with winter rainfall in the YRD have a significant characteristic of low-frequency oscillation. From a 30-day to a 0-day lead, large modifications appear in the low-frequency atmospheric circulations at low, mid, and high latitudes. At low latitudes, strong wet convective activity characterized by a negative OLR combined with a positive RH700 correlation coefficient moves northwestward and covers the entire YRD. Meanwhile, the Western Pacific subtropical high (WPSH) characterized by a positive Z500 anomaly enhances and lifts northward. At mid and high latitudes, the signal of negatively correlated Z500 northwest of Lake Balkhash propagates southeastward, indicating the cold is air moving southward. Multiple circulation factors combine together and lead to the precipitation process in the YRD. (3) Taking the intra-seasonal dynamical evolution process of the atmospheric circulation as the prediction factor, the spatiotemporal method is used to build the model for winter mean extended-range precipitation anomaly tendency in the YRD. The hindcast for the recent 10 years shows that the ensemble model has a higher skill that can reach up to 20 days. In particular, the skill of the eastern part of the YRD can reach 25 days. (4) The rainfall in the 2019/2020 winter has a significant ISO. The ensemble model could forecast the most extreme precipitation for 20 days ahead.

Social Media Image and Computer Vision Method Application in Landscape Studies: A Systematic Literature Review

This study systematically reviews 55 landscape studies that use computer vision methods to interpret social media images and summarizes their spatiotemporal distribution, research themes, method trends, platform and data selection, and limitations. The results reveal that in the past six years, social media–based landscape studies, which were in an exploratory period, entered a refined and diversified phase of automatic visual analysis of images due to the rapid development of machine learning. The efficient processing of large samples of crowdsourced images while accurately interpreting image content with the help of text content and metadata will be the main topic in the next stage of research. Finally, this study proposes a development framework based on existing gaps in four aspects, namely image data, social media platforms, computer vision methods, and ethics, to provide a reference for future research.

Human-guided motion planner with perception awareness for assistive aerial teleoperation

Assistive aerial teleoperation in unknown environments can be challenging due to the limited sensing range of cameras and the insufficient human–computer interaction. Obstacles occluding the field of view (FOV ) of camera reduce the visibility of unknown environments, and it coupled with insufficient pilot instructions directly affect human intention recognition. To address this issue, the planned trajectory generated by assistive teleoperation should align with human intention while considering perception awareness (PA) of the FOV. However, most of the existing works considering PA solely increase the FOV visibility while neglect the human intention constraints. As a result, the PA constraints can cause the heading of the quadrotor to deviate from human expectation, requiring more human–computer interactions to correct it. In this paper, we propose a human-guided motion planner with perception awareness to solve these issues. Specifically, in the path-finding stage, we introduce a spatio-temporal path similarity metric method. This method, combined with motion primitive propagation, finds an initial path that is collision-free and roughly aligned with human intention. In the subsequent trajectory optimization stage, we jointly consider PA with respect to occlusion and human intention constraints, effectively optimizing FOV visibility without deviating the heading of quadrotor from human intention. Extensive experiments and benchmark comparisons demonstrate the effectiveness of our proposed method in solving assistive aerial teleoperation for even novice operators.

A spatiotemporal distribution identification method of vehicle weights on bridges by integrating traffic video and toll station data

Real-time monitoring of the spatiotemporal distribution of vehicle weights on bridge decks is an important component of bridge structural health monitoring systems. However, it is still a challenge to identify the spatiotemporal distribution of vehicle weights on the whole bridge deck because the existing identification techniques are based on the theory of line of influence or need to install a weight-in-motion (WIM) system on the bridge. This paper proposes an information fusion-based identification method for the spatiotemporal distribution of vehicle weights without WIM installation, in which, (1) the traffic videos acquired by multiple cameras arranged along both sides of the bridge are used to detect the spatiotemporal distribution and license plate of the vehicles, and the weights obtained from the toll station are linked to the vehicles by matching the license plates. In addition, (2) a digital image correlation (DIC)-based vehicle tracking method is proposed to solve the problems of frame drop and missing detection and (3) a polynomial fitting-based coordinate transformation method is proposed to avoid the derivation of complicated coordinate conversion formula related to pinhole camera. The efficiency and accuracy of the proposed identification approach are verified by the field data collected from a cable-stayed bridge and nearby toll stations. The results indicate that our proposed method is a feasible and reliable solution for identifying spatiotemporal distribution of vehicle weights on bridges.

Spatiotemporal Analysis of Urban Heat Islands and Vegetation Cover Using Emerging Hotspot Analysis in a Humid Subtropical Climate

Research on the temporal and spatial changes of the urban heat island effect can help us better understand how urbanization, climate change, and the environment are interconnected. This study uses a spatiotemporal analysis method that couples the Emerging Hot Spot Analysis (EHSA) technique with the Mann–Kendall technique. The method is applied to determine the intensity of the heat island effect in humid subtropical climates over time and space. The data used in this research include thermal bands, red band (RED) and near-infrared band (NIR), and Landsat 7 and 8 satellites, which were selected from 2000 to 2022 for the city of Sari, an Iranian city on the Caspian Sea. Pre-processed spectral bands from the ‘Google Earth Engine’ database were used to estimate the land surface temperature. The land surface temperature difference between the urban environment and the outer buffer (1500 m) was modeled and simulated. The results of this paper show the accuracy and novelty of using Emerging Hotspot Analysis to evaluate the effect of vegetation cover on the urban heat island intensity. Based on the Normalized Difference Vegetation Index (NDVI), the city’s land surface temperature increased by approximately 0.30 °C between 2011 and 2022 compared to 2001 to 2010. However, the intensity of the urban heat island decreased during the study period, with r = −0.42, so an average −0.031 °C/decade decrease has been experienced. The methodology can be transferred to other cities to evaluate the role of urban green spaces in reducing heat stress and to estimate the heat budget based on historical observations.

Separable ConvNet Spatiotemporal Mixer for Action Recognition

Video action recognition is vital in the research area of computer vision. In this paper, we develop a novel model, named Separable ConvNet Spatiotemporal Mixer (SCSM). Our goal is to develop an efficient and lightweight action recognition backbone that can be applied to multi-task models to increase the accuracy and processing speed. The SCSM model uses a new hierarchical spatial compression, employing the spatiotemporal fusion method, consisting of a spatial domain and a temporal domain. The SCSM model maintains the independence of each frame in the spatial domain for feature extraction and fuses the spatiotemporal features in the temporal domain. The architecture can be adapted to different frame rate requirements due to its high scalability. It is suitable to serve as a backbone for multi-task video feature extraction or industrial applications with its low prediction and training costs. According to the experimental results, SCSM has a low number of parameters and low computational complexity, making it highly scalable with strong transfer learning capabilities. The model achieves video action recognition accuracy comparable to state-of-the-art models with a smaller parameter size and fewer computational requirements.

Deep learning models for groundwater level prediction based on delay penalty

Abstract In irrigation agriculture, predicting groundwater level (GWL) using deep learning models can help decision-makers coordinate surface water and groundwater usage, thus aiding in the sustainable development and utilization of groundwater. However, when making a long sequence prediction, prediction sequences often have severe delays affecting the availability of prediction results. In this paper, a new loss function is proposed to minimize the lag and oversmoothing on the prediction of GWLs. GWL, meteorology, and pumping data are collected via an irrigation Internet of Things system in Hutubi County, Xinjiang. Through Pearson's correlation analysis, historical potential evapotranspiration (ET0), groundwater extraction, and GWL were chosen to predict GWLs. Datasets were constructed through the proposed spatiotemporal data fusion method; then, the best model from the six deep learning models was selected by comparing the prediction capability of the datasets. Finally, the mean-squared error (MSE) loss function is replaced by the proposed loss function. Compared to the mean absolute error, MSE, and predicted sequence graphs, the new loss function significantly depresses the time delay with similar prediction accuracy.

The spatiotemporal distribution prediction method for distributed photovoltaic installed capacity based on power supply measurement data

Abstract With the anticipated expansion of distributed power grid integration in the foreseeable future, the consideration of distributed power’s impact on power balance becomes paramount in distribution network planning. In this research, we presented a novel approach for predicting the spatial and temporal distribution of distribution network planning areas, with a specific focus on estimating the installed capacity of distributed photovoltaic (PV) systems. Our method leveraged the saturated capacity of distributed PV, requiring minimal data inputs. By establishing a quantitative model that elucidated the relationship between installed distributed PV capacity and land area, we generated PV installed capacity evolution curves for various types of land. Subsequently, we derived the development coefficient of distributed PV installed capacity. By combining this coefficient with the current status of installed distributed PV capacity in the target area’s land parcels, we forecasted the spatial and temporal distribution of future distributed PV capacity within the region. The proposed prediction model held significant implications for the planning of new distribution networks. Additionally, this study predicted the installed distributed PV capacity for distinct land use types, including residential, commercial, and industrial land, using a regional power supply unit as a representative example. We employed the installed PV capacity unit to forecast the electricity loss rate and energy saving rate within the planning area. By validating the model and method through exemplary test results, we demonstrated the model’s feasibility and accuracy. Furthermore, our model effectively predicted the impact of distributed PV integration on overall load forecasting, thereby offering the power grid more precise load forecasting capabilities.

Spatiotemporal-restricted A∗ algorithm as a support for lane-free traffic at intersections with mixed flows

Improving the capacity of intersections is the key to enhancing road traffic systems. Benefiting from the application of Connected Automated Vehicles (CAVs) in the foreseeing future, it is promising to fully utilize spatiotemporal resources at intersections through cooperative and intelligent trajectory planning for CAVs. Lane-free traffic is currently a highly anticipated solution that can achieve more flexible trajectories without being limited by lane boundaries. However, it is challenging to apply efficient lane-free traffic to be compatible with the traditional intersection control mode for mixed flow composed of CAVs and Human-driving Vehicles (HVs). To address the research gap, this paper proposes a spatiotemporal-restricted A∗ algorithm to obtain efficient and flexible lane-free trajectories for CAVs. First, we restrict the feasible area of the heuristic search algorithm by considering the feasible area and orientation of vehicles to maintain the trajectory directionality of different turning behaviors. Second, we propose a spatiotemporal sparse sampling method by defining the four-dimensional spatiotemporal grid to accelerate the execution of the heuristic search algorithm. Third, we consider the motions of HVs as dynamic obstacles with rational trajectory fluctuation during the process of trajectory planning for CAVs. The proposed method can retain the advantage of efficiently exploring feasible trajectories through the hybrid A∗ algorithm, while also utilizing multiple spatiotemporal constraints to accelerate solution efficiency. The experimental results of the simulated and real scenarios with mixed flows show that the proposed model can continuously enhance traffic efficiency and fuel economy as the penetration of CAVs gradually increases.

A multi-factor spatio-temporal correlation analysis method for PV development potential estimation

The impact of wind speed on PV power generation is influenced by the monsoon and the complex topography and environmental factors found in mountainous areas. This complexity can reduce the correlation between PV power and irradiance. To better understand the impact of wind speed, we conducted an analysis in various topographical regions of the Hengduan Mountain in China. Our findings show that high wind speeds can enhance PV utilization, particularly in plateau and hill regions. This is due to the blocking and lifting effect of the north-south oriented mountains on the east-west wind, as well as the channeling effect on the north-south wind. To improve the efficiency of utilizing solar energy, it is important to consider the spatial distribution of wind speed when developing PV. Correlation analysis of multi-factors on a spatial and temporal scale can also offer insight. Furthermore, planning the layout of PV systems with meteorological resources in mind can enhance PV utilization in complex mountainous terrain.

SCGTracker: Spatio-temporal correlation and graph neural networks for multiple object tracking

Multiple Object Tracking is an important vitally important fundamental task in computer vision. Visual tracking becomes challenging when objects move in groups and are obscured from each other. There are two mainstream solution strategies for these group models. One is to transform the data association problem into a graph matching problem for solving, while the other is to apply the social power model as an advanced constraint for group tracking. In the former case, the solving difficulty geometric growth as the number of tracked objects increases, and the computing efficiency for real-time tracking demand cannot be met. The latter strategy tends to set up fixed-size groups or offline training rules, resulting in a lack of flexibility that limits their scenario generalization. According to the shortcomings of existing methods, this paper proposes a novel multiple object tracking method with spatio-temporal correlation and graph neural networks. Firstly, the relational features of the historical trajectories are extracted through the spatio-temporal relationship learning module, which models the spatio-temporal correlations of the objects and dynamically constructs the group structure online. Then, the graph neural network is combined with appearance and motion information, and the similarity between each detection and tracklet is used as a weight in node feature aggregation to make powerful distinctions between node features. Meanwhile, the spatio-temporal correlation method is also used to solve target loss issues caused by occlusion. Even collocated with linearly assigned data association method, good tracking results are still achieved, with a low computational complexity. Experiments on three challenging public datasets, namely MOT16, MOT17, and MOT20, validated the accuracy and efficiency of the proposed tracking method.

Efficient Bike-sharing Repositioning with Cooperative Multi-Agent Deep Reinforcement Learning

As an emerging mobility-on-demand service, bike-sharing system (BSS) has spread all over the world by providing a flexible, cost-efficient, and environment-friendly transportation mode for citizens. Demand-supply unbalance is one of the main challenges in BSS because of the inefficiency of the existing bike repositioning strategy, which reallocates bikes according to a pre-defined periodic schedule without considering the highly dynamic user demands. While reinforcement learning has been used in some repositioning problems for mitigating demand-supply unbalance, there are significant barriers when extending it to BSS due to the dimension curse of action space resulting from the dynamic number of workers and bikes in the city. In this paper, we study these barriers and address them by proposing a novel bike repositioning system, namely BikeBrain, which consists of a demand prediction model and a spatio-temporal bike repositioning algorithm. Specifically, to obtain accurate and real-time usage demand for efficient bike repositioning, we first present a prediction model ST-NetPre, which directly predicts user demand considering the highly dynamic spatio-temporal characteristics. Furthermore, we propose a spatio-temporal cooperative multi-agent reinforcement learning method (ST-CBR) for learning the worker-based bike repositioning strategy in which each worker in BSS is considered an agent. Especially, ST-CBR adopts the centralized learning and decentralized execution way to achieve effective cooperation among large-scale dynamic agents based on Mean Field Reinforcement Learning (MFRL), while avoiding the huge dimension of action space. For dynamic action space, ST-CBR utilizes a SoftMax selector to select the specific action. Meanwhile, for the benefits and costs of agents’ operation, an efficient reward function is designed to seek an optimal control policy considering both immediate and future rewards. Extensive experiments are conducted based on large-scale real-world datasets, and the results have shown significant improvements of our proposed method over several state-of-the-art baselines on the demand-supply gap and operation cost measures.

Development and Evaluation of a Cloud-Gap-Filled MODIS Normalized Difference Snow Index Product over High Mountain Asia

Accurate snow cover data are critical for understanding the Earth’s climate system, and exploring hydrological processes and regional water resource management over High Mountain Asia (HMA). However, satellite-based remote sensing observations of snow cover have inevitable data gaps originating from cloud cover, sensor, orbital limitations and other factors. Here an effective cloud-gap-filled (CGF) method was developed to fully fill the data gaps in Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference snow index (NDSI) product. The CGF method combines the respective strengths of the cubic spline interpolation method and the spatio-temporal weighted method for generating the CGF Terra-Aqua MODIS NDSI product over HMA from 2000 to 2021. Based on the validation results of in situ snow-depth observations, the CGF NDSI product achieves a high range overall accuracy (OA) of 93.54–98.08%, a low range underestimation error (MU) of 0.15–3.49% and an acceptable range overestimation error (MO) of 0.84–5.77%. Based on the validation results of high-resolution Landsat images, this product achieves the OA of 88.52–92.40%, the omission error (OE) of 1.42–10.28% and the commission error (CE) of 5.97–17.58%. The CGF MODIS NDSI product can provide scientific support for eco-environment sustainable management in the high mountain region.

Solving time fractional partial differential equations with variable coefficients using a spatio-temporal meshless method

<p>This paper presents a novel spatio-temporal meshless method (STMM) for solving the time fractional partial differential equations (TFPDEs) with variable coefficients based on the space-time metric. The main idea of the STMM is to directly approximate the solutions of fractional PDEs by using a multiquadric function with the space-time distance within a space-time scale framework. Compared with the existing methods, the present meshless STMM entirely avoids the difference approximation of fractional temporal derivatives and can be easily applied to complicated irregular geometries. Furthermore, both regular and irregular nodal distribution can be used without loss of accuracy. For these reasons, this new space-time meshless method could be regarded as a competitive alternative to the conventional numerical algorithms based on difference decomposition for solving the TFPDEs with variable coefficients. Numerical experiments confirm the ability and accuracy of the proposed methodology.</p>

Research on the Development Path of E-commerce for Rural Tourism in the Context of Information Technology

Abstract In the digital age, e-commerce has emerged as a transformative force in the domain of rural tourism, infusing traditional practices with renewed dynamism. This study employs the spatiotemporal extreme entropy value method to assess the evolution of rural tourism in China. The investigation delves into the influence of e-commerce on the qualitative enhancement of rural tourism, utilizing a dynamic panel data model to analyze its effects and conducting heterogeneity tests based on geographic and economic criteria. According to the weights assigned by the spatiotemporal extreme entropy method, the quality of rural tourism development in China generally increased between 2013 and 2023, with a notable dip to 0.3005 in 2020. The heterogeneity analysis, implemented through a fixed-effects model, reveals significant positive impacts in both the eastern and central regions at the 5% significance level. Moreover, regions with a medium level of economic development exhibit a robust positive effect, with a regression coefficient of 0.429. The developmental impact of e-commerce on high-quality rural tourism is statistically significant at the 1% level, and all control variables demonstrate significant positive correlations. These findings underscore the substantial role that e-commerce development plays in advancing high-quality rural tourism.

Locality Preserving Projections-Based Spatiotemporal Modeling of the Temperature Distribution of Lithium-Ion Batteries

The temperature distribution is one of the key factors affecting the safety, capacity fade, and optimal use of lithium-ion batteries (LIBs). However, it is a challenging task to develop an accurate approximation model for estimating this distribution, owing to the inherent nature of thermal processes, including time/space coupling, infinite dimensions, and strong nonlinearity. Therefore, this paper proposes a novel locality preserving projections (LPP)-based spatiotemporal modeling method to address this problem. First, an orthogonal enhanced LPP (OELPP) method is proposed for time/space decoupling. With this novel reduction dimension technique, the spatiotemporal temperature variables are separated into spatial basis functions (SBFs) and low-dimensional time variables. Then, to handle the temporal nonlinearity, a broad learning system with kernel-based manifold features (BLS-KMF) is designed to construct a dynamic temporal model. To improve the modeling performance, the kernel trick and the intrinsic manifold information are introduced to the proposed BLS-KMF to learn the mapped features. Finally, with the time/space synthesis, the battery temperature distribution can be reconstructed. Experimental results on a 32 Ah ternary lithium-ion battery illustrate the superior performance and effectiveness of the proposed method.

A Rapid and Convenient Spatiotemporal Calibration Method of Roadside Sensors Using Floating Connected and Automated Vehicle Data

A Rapid and Convenient Spatiotemporal Calibration Method of Roadside Sensors Using Floating Connected and Automated Vehicle Data

Spatio-temporal management of renewable energy consumption, carbon emissions, and cost in data centers

Under the background of "carbon neutrality ", data center enterprises are confronted with the challenges of high energy costs and the need to manage carbon emissions. Compared with traditional energy sources, renewable energy possesses the advantages of being low-carbon and cost-effective, making it an essential avenue for data centers to enhance their utilization of renewable energy. By employing a spatio-temporal scheduling method for computing power load, data center enterprises can maximize the benefits of renewable energy, achieve low-carbon and cost-effective operation, and enhance the consumption of renewable energy. This study developed a spatio-temporal scheduling model for computing load in data centers, with a specific focus on optimizing the utilization of renewable energy while considering the goals of low-carbon emissions and cost-effectiveness. A two-stage spatio-temporal scheduling algorithm (ESTS) was designed and implemented, and three sets of experiments were conducted to assess the effectiveness and applicability of offline load scheduling using offline load data from Alibaba's cluster-trace-v2018. The results demonstrate that the proposed scheduling method can achieve a significant reduction of carbon emissions by 70% and operating costs by 40% across various scenarios. Moreover, during the summer season when renewable energy is abundant, the application of this scheduling method in a single data center can effectively achieve the objectives of managing low-carbon emissions and minimizing costs.

Low Spatio-temporal Frequency Wavefront Aberration Correction Method Based on a Movable Secondary Mirror

Low Spatio-temporal Frequency Wavefront Aberration Correction Method Based on a Movable Secondary Mirror