Transportation infrastructure is vital for sustaining communities and fostering economic development. Urbanization and climate change have led to the rapid deterioration of road transport systems, posing significant challenges for future sustainabili...
Modern geo-information platforms commonly adopt multi-window map interfaces that integrate heterogeneous data, such as dynamic maps and live camera feeds. These interfaces impose high cognitive load and slow spatial event detection. Operators must ra...
High-Definition (HD) Maps have become a cornerstone of autonomous vehicle (AV) technology, enabling precise localization, perception, and decision-making. Despite their increasing prominence in the automotive and geospatial industries, HD Maps remain...
Duckweed/algal bloom-type black–odorous waters (DAWs) exhibit composite optical properties of vegetation and pollution, posing intractable remote sensing identification challenges in complex environments. Current methods suffer from three criti...
All-sky cameras provide continuous hemispherical observations essential for atmospheric monitoring and observatory operations; however, automated classification of sky conditions in tropical environments remains challenging due to strong illumination...
The unique geographical isolation and climate sensitivity of island ecosystems make them valuable for ecological research. The Normalized Difference Vegetation Index (NDVI) is an important indicator when monitoring and evaluating these systems, and i...
To develop targeted forest management strategies, management staff must understand the statistical relationships between forest aesthetic values and landscape metrics across specified distance ranges. However, as the existing studies based on distanc...
Ensuring equitable and inclusive access to children’s extracurricular activity facilities represents a profound manifestation of educational equity and is crucial for promoting children’s holistic development and societal sustainability....
Street layout has a significant effect on accessibility and intelligibility, which ultimately affects navigation and movement efficiency. While previous research has examined planned and unplanned street patterns, most studies focus on single-scale a...
To enhance the autonomous navigation capability of intelligent agents in complex environments, this paper presents a visual localization algorithm for dynamic scenes that leverages multi-source information fusion. The proposed approach is built upon...
Efficient level-of-detail (LOD) management is crucial for handling large-scale 3D meshes in BIM, GIS, and digital twin applications. In practice, both individual models and complex multi-mesh scenes require multi-resolution representations. Yet two p...
The growing availability of spatial data from remote sensing, laser scanning (LiDAR), and photogrammetric techniques stimulates the dynamic development of methods for the automatic detection and classification of topographic objects. In recent years,...
In the original publication [...]
Kazakhstan, located in Central Asia, is experiencing faster warming than the global trend, making it an important region regarding the study of how climate change is affecting climatic zones. This research aims to identify projected shifts in Kö...
Activity space represents the spatiotemporal interaction between individuals and their environment. While most studies measure potential activity space using short-term data, few have defined or measured its actual internal structure. This study intr...
The urban physical environment is composed of multiple elements that collectively influence the spatial pattern of crime. Existing research has predominantly focused on the relationship between individual types of facilities and crime, yet there rema...
This study presents a robust framework for optimizing the site selection of Electric Vehicle Charging Stations (EVCS) in Qatar by integrating a Geographic Information System (GIS) with a Multi-Criteria Decision-Making (MCDM) model. The core innovatio...
This article discusses public transport fare evasion from the point of view of the relations between inspection actions and detected evasion, with the aim of improving the efficacy of the former. By applying spatial statistics methods to a large data...
GNN approaches to property valuation typically rely on spatial proximity, assuming that nearby properties exhibit similar price patterns. In practice, this assumption often fails as neighborhood and administrative boundaries create sharp price discon...
This study proposes a systematic framework for establishing ethical guidelines for GeoAI (Geospatial Artificial Intelligence), which integrates AI with spatial data science, GIS, and remote sensing. While general AI ethics have advanced through the O...
With the development of the Internet of Things (IoT) and smart industry, the demand for high-precision indoor positioning is becoming increasingly urgent. Ultra-ideband (UWB) technology has become a research hotspot due to its centimeter-level rangin...
Exploring the spatiotemporal evolution of building stock in African countries is of great significance for understanding the urbanization process, regional development disparities, and sustainable development pathways in the Global South. Integrating...
Investigating the spatial distribution and dynamics of terrestrial carbon storage is vital for climate change mitigation. However, horizontal spatial analyses often overlook heterogeneity in complex terrains. Here, we focused on the Gonghe Basin on t...
In a situation where cities are increasingly being developed vertically and complexly, a novel approach for analyzing vertical urban functional patterns is proposed. For this purpose, a multi-dimensional GDC (Geo Data Cube) consisting of spatial and...
Spatial data has distinctive properties that differentiate it from non-spatial data. One prominent characteristic is spatial autocorrelation (SA). When machine learning techniques are applied for spatial data modeling, they require spatially explicit...
This observation-based and explainable approach demonstrates the applicability of multi-source remote sensing for flood assessment in data-scarce regions, offering a robust scientific basis for flood management and spatial planning in monsoon-affecte...
Spatial accessibility to healthcare services is commonly determined by three core components: demand, supply, and traffic speed. Although understanding which factors contribute to accessibility changes can help prioritize interventions to enhance acc...
Forecasting the spatio-temporal evolutions of cellular traffic is crucial for urban management. However, achieving accurate forecasting is challenging due to “complex correlation modeling” and “model-blindness” issues. Specifi...
Pedestrian trajectory prediction plays a vital role in autonomous driving and intelligent surveillance systems. Graph neural networks (GNNs) have shown remarkable effectiveness in this task by explicitly modeling social interactions among pedestrians...
Rebalancing shared bikes poses a significant challenge for dockless bike-sharing (DLBS) operators, as inevitable spatiotemporal mismatches between demand and supply lead to high redistribution costs. Despite its operational significance, empirical re...
The walking accessibility of primary healthcare institutions (PHCIs) is a pivotal determinant of health equity. However, prior studies often lack a comprehensive assessment that integrates the spatiotemporal dynamics of both multi-faceted supply and...
Temporal reasoning is an important part of the field of time geography and spatio-temporal data science. Recent advances in qualitative temporal reasoning have developed a set of 74 relations that apply between discretized time intervals of at least...
In the context of the “Dual Carbon” goals, accurately predicting the spatiotemporal evolution of urban Net Primary Productivity (NPP) is crucial for resilient urban planning. While recent studies have coupled land use models with ecosyste...
Ensuring the safety of autonomous driving systems (ADS) requires scenario-based testing that reflects the complexity and variability of real-world driving conditions. However, the nondeterministic nature of actual traffic environments makes physical...
This article studies the application of deep neural networks for automatic building detection in aerial RGB images. Special focus is put on accuracy robustness in both well-structured and poorly planned urban scenarios, which pose significant challen...
Large language models (LLMs) have advanced geospatial artificial intelligence; however, geospatial knowledge-base question answering (GeoKBQA) remains underdeveloped. Prior systems have relied on handcrafted rules and have omitted the splitting of da...
Population activity drives urban development, and high-spatiotemporal-resolution population distribution provides critical insights for refined urban management and social services. However, mixed population activity patterns and spatial heterogeneit...
Indoor positioning systems (IPSs) are increasingly applied in indoor settings where satellite-based GNSS signals are unavailable, including museums and other cultural heritage spaces. Within the META-MUSEUM project, we present a pilot study integrati...
The importance of GIS models to monitor the spread of infectious diseases and support emergency response has been underlined by a large body of literature and strengthened by the COVID-19 pandemic to identify possible solutions able to recognise spat...
To address the challenge of obtaining high-spatiotemporal-resolution and high-precision temperature grids for agricultural meteorological monitoring, this research focuses on rice heat stress monitoring with the China Meteorological Administration La...
The paper examines the phenomenon of urban envelopes, a conceptual parallel to building envelopes, which is considered an emerging theme in studies of the built environment. The term ‘envelope’ refers to various physical and non-physical...
Crowdsensing data serves as a crucial resource for supporting spatiotemporal applications and services. However, its inherent heterogeneity and quality uncertainty present significant challenges for data usability assessment: the evaluation methods a...
With the proliferation of mobile internet and location-based services, location-based social networks (LBSNs) have accumulated extensive user check-in data, driving the advancement of next Point-of-Interest (POI) recommendation systems. Although exis...
Establishing digital twin scenes facilitates the understanding of geospatial phenomena, representing a significant research focus for GIS scientists and engineers. However, current research on digital twin scenes modeling relies on manual interventio...
The development of the digital economy has highlighted the important value of geospatial data across numerous domains, with data trading being a pivotal link in activating this value. The current user base engaging in data trading is diverse, while t...
The availability of nighttime satellite imagery provides unique opportunities for monitoring fishing activity in data-sparse ocean regions. This study leverages Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band monthly composite imager...
Fifth-generation (5G) wireless networks have been widely deployed across various applications, including indoor positioning. This paper presents a model for 3D indoor localization of an unmanned aerial vehicle (UAV) using 5G millimeter-wave technolog...
Accurate point cloud registration is a fundamental prerequisite for reality-based 3D reconstruction and large-scale spatial modeling. Despite significant international progress, reliable registration in architectural and urban scenes remains challeng...
Building pattern recognition is essential for understanding the dynamics of urban development, facilitating automatic map synthesis, and aiding in municipal planning Sefforts. Traditional research methods, which rely solely on geometric feature extra...
Against the global backdrop of urbanization and sustainable development, urban parks—key public spaces for carbon sequestration, heat island mitigation, and public health promotion—have made their service performance a critical metric for...
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