Understanding the decision-making behavior of pedestrians is essential for urban designers and developers in enhancing the commercial and aesthetic value of streets and other urban spaces. However, limited research has been conducted on the activity...
For multi-robot cooperative coverage, an effective spatial division strategy is essential to ensure balanced and spatially continuous task regions for each robot. Traditional grid-based partitioning approaches like DARP (Divide Areas based on Robots&...
In order to improve the efficiency of transportation networks, it is critical to forecast traffic congestion. Large-scale traffic congestion data have become available and accessible, yet they need to be properly represented in order to avoid overfit...
Trajectory planning for autonomous vehicles is essential for ensuring driving safety, passenger comfort, and operational efficiency. Collision avoidance constraints introduce significant computational complexity due to their inherent non-convex and n...
In the context of robotics and game AI, grid-based Distance Maps (DMs) are often used to fulfill collision checks by providing each traversable cell maximal clearance to its closest obstacle. A key challenge for DMs’ application is how to impro...
Forecasting monthly precipitation in mountainous terrain poses challenges that push conventional deep learning approaches to their limits: convective processes operate locally while orographic effects span entire drainage basins. We compare three arc...
Many grid-based spatial hydrological models suffer from the complexity of setting up a coherent spatial structure to calibrate such a complex, highly parameterized system. There are essential aspects of model-building to be taken into account: spatia...
Rapid urbanization in the Global South has intensified the formation of mega-urban regions, where conventional urban–rural classifications often fail to capture the complexity of peri-urban systems. In the Jakarta–Bandung Mega-Urban Regio...
Change detection is a crucial field in remote sensing image analysis for tracking environmental dynamics. Although convolutional neural networks (CNNs) have made impressive strides in this field, their grid-based processing structures struggle to cap...
We present a visual–inertial odometry (VIO) system that integrates a deep feature extraction and filtering strategy with optical flow to improve tracking robustness. While many traditional VIO methods rely on hand-crafted features, they often s...
Grid-based maritime search and rescue (SAR) planning requires sequential decisions from incomplete spatial observations. Increasing map size also expands the search space and can intensify reward sparsity. This paper proposes CCAL-DQN, an LSTM deep Q...
Transit systems play a vital role in urban mobility, yet predicting individual travel behavior within these systems remains a complex challenge. Traditional machine learning approaches struggle with transit trip data because each trip may consist of...
Accurate and spatially resolved yield estimation is a critical requirement for precision agriculture and orchard management. This paper presents a geometrically consistent, orchard-scale apple yield estimation framework that integrates GNSS–vis...
Pseudo-multiview learning (PML) improves classification by generating complementary internal representations and fusing their evidence using subjective logic. However, conventional PML incurs substantial computational costs as the number of pseudo-vi...
Soundscape monitoring has become an increasingly important tool for studying ecological processes and supporting habitat conservation. While many recent advances focus on identifying species through supervised learning, there is growing interest in u...
Significant computation challenges are emerging as agent-based modeling becomes more complicated and dynamically data-driven. In this context, parallel simulation is an attractive solution when dealing with massive data and computation requirements....
This article addresses the problem of finding the shortest route for Automated Guided Vehicles (AGVs) in a production environment with constrained battery state-of-charge (SoC) and time-dependent operating conditions. The route map is divided into a...
Reliable UAV navigation is challenging when direct geometric perception is limited and only sparse environmental observations are available. This paper presents CoSafe-Nav, an integrated navigation framework that uses ultra-wideband (UWB) line-of-sig...
Reliable ecosystem service (ES) assessments are essential for sustainable land management and ecological restoration; yet, their implications for decision-making may vary with the spatial units used for analysis. This study examined how spatial repre...
Land subsidence prediction remains challenging. Conventional grid-based or sequence-only neural networks struggle to represent these spatial dependencies and often lack structured mechanisms for incorporating region-level deformation priors and local...
Traffic forecasting in cellular networks is a challenging spatiotemporal prediction problem due to strong temporal dependencies, spatial heterogeneity across cells, and the need for scalability to large network deployments. Traditional cell-specific...
Self-location plays a crucial role in a framework of autonomous navigation, especially in a GNSS/radio-denied environment. At the current time, self-location for artificial agents still has to resort to the visual and laser technologies in the framew...
With the continuous advancement of technology for obtaining geographic spatial data, the accumulated volume of such data has been increasing, thus imposing higher demands on the storage, organization, and management of such data. As a new form of dat...
Grid cells in the brain’s entorhinal cortex are essential for spatial navigation and have inspired advancements in robotic navigation systems. This paper first provides an overview of recent research on grid cell-based navigation in robotics, f...
With the continuous development of general aviation, the contradiction between the air demand of general aviation low-altitude airspace and civil aviation routes is sharp. The difficulty of airspace planning is complex and changeable, and the existin...
This paper presents a novel bio-inspired predictive model of visual navigation inspired by mammalian navigation. This model takes inspiration from specific types of neurons observed in the brain, namely place cells, grid cells and head direction cell...
A new analytical formula for H0, one of the three parameters (H0, g, and r) on which the NeQuick model is based to describe the altitude profile of the electron density above the F2-layer peak height hmF2, has recently been proposed. This new analyti...
This study explored the influence of the spatial resolution of a gridded weather dataset when inputted in the soil and water assessment tool (SWAT) over the Garonne River watershed. Several datasets are compared: ground-based weather stations, the 8-...
Image super-resolution (SR) is a fundamental task in computer vision that aims to recover high-fidelity details from low-resolution images. To address the limitations of existing Transformer-based SR methods, this paper introduces GSDAT-Net, a grid-s...
This study extends hierarchical spatiotemporal identifiers beyond cell identification by using the Binary Spatiotemporal Identifier (BST-ID) as a computational representation for multi-cell regions. BST-ID supports independent resolution along x, y,...
The perception of 3D space by mobile robots is rapidly moving from flat metric grid representations to hybrid metric-semantic graphs built from human-interpretable concepts. While most approaches first build metric maps and then add semantic layers,...
This study proposes an end-to-end mathematical framework to automatically transform warehouse layout images into optimization-ready route matrices. The objective is to convert visual spatial information into a discrete, graph-based representation sui...
Accurate daily gridded precipitation estimates are needed to characterize spatial rainfall variability and support retrospective hydrological analysis in Southeast Asia, where complex rainfall regimes and sparse rain gauge coverage remain major chall...
Local short-duration heavy-rainfall warning requires early assessment of hazardous rainfall within a specified neighborhood. Geostationary meteorological satellites provide frequent, spatially continuous observations of cloud systems and storm evolut...
Groundwater-storage anomalies (GWSA) derived from the Gravity Recovery and Climate Experiment (GRACE) mission provide valuable information for regional groundwater monitoring. Improving the spatial representation and temporal continuity of GRACE-deri...
Accurate prediction of airport delays under convective weather conditions is essential for effective traffic coordination and improving overall airport efficiency. Traditional methods mainly rely on numerical weather and traffic indicators, but they...
In dynamic environments with continuous variability, such as those affected by typhoons, ship path planning must account for both navigational safety and the maneuvering characteristics of the vessel. However, current methods often struggle to accura...
In recent years, graph-based learning methods have gained significant traction in point-of-interest (POI) recommendation systems due to their strong generalization capabilities. These approaches commonly transform user check-in records into graph-str...
This paper presents an Implicit Neural Representation method for Event-Based Imaging Velocimetry (INR-VG) to reconstruct dense velocity fields from sparse event streams. The core idea is to learn a mapping (multilayer perceptron) from spatial coordin...
The representation of electronic wavefunctions in real space grids, which are directly related to molecular orbitals and electronic densities either in molecular or crystalline systems, is a fundamental part of many studies at ab initio levels, since...
Research on surface visibility and road-network-level visibility is crucial for transportation. However, complex terrain, frequent dust activities, and sparse observations increase the difficulty of continuous visibility monitoring along roads. This...
Mobile crowdsensing (MCS) leverages the sensing capabilities of mobile devices carried by a vast number of participants to accomplish large-scale urban sensing tasks. In MCS systems, participants continuously generate trajectory streams while uploadi...
Near-field multistatic radar imaging requires evaluating a nonlinear matched-filter operator over a three-dimensional search region, imposing a prohibitive computational burden on systems utilizing sparse, large-aperture receiver layouts. In this pap...
Regional development disparities remain a persistent global challenge, yet existing assessment approaches often face a trade-off between spatial detail and temporal coverage. Conventional socioeconomic statistics provide relatively reliable informati...
This study develops a reproducible regional screening workflow to assess geothermal potential in the Cambrian reservoir system of Western Lithuania under conditions of sparse and heterogeneous legacy subsurface data. The approach integrates data comp...
Conventional independently trained models for distributed photovoltaic (DPV) power forecasting at the feeder, main-transformer, and regional-grid levels do not explicitly reuse spatiotemporal representations across the distribution-network hierarchy....
Optical Fiber Composite Overhead Ground Wire (OPGW) cables serve dual functions in power systems, lightning protection and critical communication infrastructure for real-time grid monitoring. Accurate OPGW identification during UAV inspections is ess...
India is the world’s most populous country, yet also one of the least urban. It has long been known that India’s official estimates of urban percentages conflict with estimates derived from alternative conceptions of urbanization. To date...
Hand gesture recognition plays an important role in computer vision with broad applications in human–computer interaction, intelligent perception, and contactless interaction systems. However, existing detection-based methods mainly rely on han...
Unstructured POI name texts are widely used in fine-grained urban analysis, yet missing labels and semantic ambiguity often limit their value for spatial inference. This study proposes a large language model-based semantic–spatial inference fra...
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