In recent years, the rapid development of cloud computing and web technologies has led to a significant advancement to chain geospatial information services (GI services) in order to solve complex geospatial problems. However, the construction of a p...
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...
Geospatial knowledge graphs provide critical technology for integrating geographic information and semantic knowledge, which are very useful for geographic data analysis. As the scale of geospatial knowledge graphs continues to grow, the distributed...
While the specialisation of science is important for understanding specific systems, the isolation of scientific schools in their disciplinary silos makes it harder to understand the interactions within and between systems and limits the wisdom about...
The integration of the raster data cube alongside another form of geospatial data (e.g., vector data) raises considerable challenges when it comes to managing and representing it using knowledge graphs. Such integration can play an invaluable role in...
National-level studies present the development techniques and challenges of sustaining energy-rich economies, particularly those in the developing world. However, examples of the application and interpolation of these broad-scale analyses to the regi...
Geospatial data is increasingly being made available on the Web as knowledge graphs using Linked Data principles. This entails adopting the best practices for publishing, retrieving, and using data, providing relevant initiatives that play a prominen...
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...
Information resources have increased rapidly in the big data era. Geospatial data plays an indispensable role in spatially informed analyses, while data in different areas are relatively isolated. Therefore, it is inadequate to use relational data in...
The Web of Data has been fueled significantly by geospatial data over the last few years. In the current link discovery frameworks, there is still a lack of robust support for finding geospatial-aware links between geospatial data sources in the Web...
To help users discover the most relevant spatial datasets in the ever-growing global spatial data infrastructures (SDIs), a number of similarity measures of geospatial data based on metadata have been proposed. Researchers have assessed the similarit...
A semantics-based method for density-based clustering with constraints imposed by geographical background knowledge is proposed. In this paper, we apply an ontological approach to the DBSCAN (Density-Based Geospatial Clustering of Applications with N...
In landscape planning and design, geospatial technologies (GSTs) are used to aid in visualizing and interpreting geographic environments, identifying geospatial patterns, and making decisions around information based on maps and geospatial informatio...
Geospatial factors, because of their spatio-temporal correlations with demand-driven limited service, offer to improve urban planning decisions and expand the knowledge base in cities. Spatio-temporal analyses require an efficient and comprehensible...
Geospatial data, especially remote sensing (RS) data, are of significant importance for public services and production activities. Expertise is critical in processing raw data, generating geospatial information, and acquiring domain knowledge and oth...
The size, volume, variety, and velocity of geospatial data collected by geo-sensors, people, and organizations are increasing rapidly. Spatial Data Infrastructures (SDIs) are ongoing to facilitate the sharing of stored data in a distributed and homog...
Global problems all occur at a particular location on or near the Earth’s surface. Sitting at the junction of artificial intelligence (AI) and big data, knowledge graphs (KGs) organize, interlink, and create semantic knowledge, thus attracting much a...
Unsupervised representation learning can train deep learning models to formally express the semantic connotations of objects in the case of unlabeled data, which can effectively realize the expression of the semantics of geospatial entity categories...
This paper introduces a new GeoAI solution to support automated mapping of global craters on the Mars surface. Traditional crater detection algorithms suffer from the limitation of working only in a semiautomated or multi-stage manner, and most were...
Real-time GIS enables multiple geographically dislocated users to collaboratively edit geospatial data. However, being based on the strong consistency model, traditional real-time GIS implementations cannot provide fully automatic conflict resolution...
Deep learning is a promising method for image classification, including satellite images acquired by various sensors. However, the synergistic use of geospatial data for water body extraction from Sentinel-1 data using deep learning and the applicabi...
As the need for more broad-scale solutions to environmental problems is increasingly recognized, traditional hierarchical, government-led models of coordination are being supplemented by or transformed into more collaborative inter-organizational net...
Discovering and selecting relevant geospatial datasets from heterogeneous sources remains difficult in conventional geoportals, where keyword-based search often fails to capture thematic relationships or user intent. This article presents an ontology...
Virtual 3D city models act as valuable central information hubs supporting many aspects of cities, from management to planning and simulation. However, we noted that 3D city models are still underexploited and believe that this is partly due to ineff...
The halfway point for the implementation of the United Nations Sustainable Development Goals (SDGs) was marked in 2023, as set forth in the 2030 Agenda. Geospatial technologies have proven indispensable in assessing and tracking fundamental component...
Mapping ancient roads is crucial to tell credible geospatial stories about where, how, or why different people might have travelled or transported materials within and between places in the distant past. Achieving this process is challenging and comm...
The core of intelligent virtual geographical environments (VGEs) is the formal expression of geographic knowledge. Its purpose is to transform the data, information, and scenes of a virtual geographic environment into “knowledge” that can...
This study aims to improve interoperability between Geographic Information Systems (GIS) and geospatial databases for Intelligent Transport Systems (ITS). Road authorities maintain authoritative information for legal and safe navigation in GIS databa...
The continuous increase in the volume of geospatial data has led to the creation of storage tools and the cloud to process data. Google Earth Engine (GEE) is a cloud-based platform that facilitates geoprocessing, making it a tool of great interest to...
As the global climate continues to change, extreme weather events such as hurricanes and heavy rainfall are becoming more frequent. Subsequently, flooding and standing water disrupt and negatively impact many communities. The use of nature-based solu...
Accurate detection of geospatial objects, particularly landslides, is a critical challenge in geospatial data analysis due to the complex nature of the data and the significant consequences of these events. This paper introduces an innovative topolog...
Education has always been modified by employing different technologies to enhance the knowledge acquisition and performance of students. Virtual Reality (VR) along with the Game Industry is among those evolving technologies for educational applicatio...
An analysis of global statistics shows a substantial increase in flood damage over the past few decades. Moreover, it is expected that flood risk will continue to rise due to the combined effect of increasing numbers of people and economic assets in...
Geographic data visualization is an important research area of Web Geographic Information System (GIS). Owing to the detailed subassemblies and exhaustive knowledge database, building information modeling (BIM) plays an important role in geospatial r...
LiDAR technology can provide very detailed and highly accurate geospatial information on an urban scene for the creation of Virtual Geographic Environments (VGEs) for different applications. However, automatic 3D modeling and feature recognition from...
Motivation is a determining factor in the learning process, and encourages the student to participate in activities that increase their performance. Learning strategies supplemented by computer technology in a scenario-based learning environment can...
To safely protect workplaces and the workforce during and after the COVID-19 pandemic, a scalable integrated sensing solution is required in order to offer real-time situational awareness and early warnings for decision-makers. However, an informatio...
This paper presents a geospatial methodology for zoning the earthquake-induced soil liquefaction risk at a continental scale and set-up in a Geographic Information System (GIS) environment by coupling data-driven and knowledge-driven approaches. It i...
Travel blogs are a significant source for modeling human travelling behavior and characterizing tourist destinations owing to the presence of rich geospatial and thematic content. However, the bulk of unstructured text requires extensive processing f...
Communities and countries around the world are gearing up efforts to implement the 2030 Agenda goals and targets. In this paper, the water and migration scenarios are explained with a focus on Sustainable Development Goals (SDGs) 6 (water-related), 1...
The growing complexity of cities and the unprecedented pace of urbanisation create exposure and vulnerabilities to extreme events and crises that are difficult to manage and plan for as widely acknowledged by the existing literature. In this paper, t...
Forests are part of a complex landscape mosaic and play a crucial role for people living both in rural and urbanized spaces. Recent progresses in modelling and Decision Support System (DSS) applied to the forestry sector promise to improve public par...
Earth observation data serve as a fundamental resource in Earth system science. The rapid advancement of remote sensing and in situ measurement technologies has led to the generation of massive volumes of data, accompanied by a growing body of geogra...
Comprehensive datasets for nature-based solutions (NBS), and their diverse relationships have not yet been accumulated into a deployable format. This research describes the development of a novel National Spatial Data Infrastructure (NSDI) system for...
The proliferation of geospatial data from diverse sources, such as Earth observation satellites, social media, and unmanned aerial vehicles (UAVs), has created a pressing demand for cross-platform data integration, interoperation, and intelligent dat...
Sustainable business development is connected with environmental impact, natural resources and people. This makes the location a crucial factor for the operation of a business. Therefore, a combination of both geo-spatial data analysis and traditiona...
Groundwater Dependent Ecosystem (GDE) protection is increasingly being recognized as essential for the sustainable management and allocation of water resources. GDE services are crucial for human well-being and for a variety of flora and fauna. Howev...
The signature of the 2019 Declaration of Cooperation on advancing the digitization of cultural heritage in Europe shows the important role that the 3D digitization process plays in the safeguard and sustainability of cultural heritage. The digitizati...
Geospatial data conflation is the process of identifying and merging the corresponding features in two datasets that represent the same objects in reality. Conflation is needed in a wide range of geospatial analyses, yet it is a difficult task, often...
In this paper, a Segment Anything Model (SAM)-based pedestrian infrastructure segmentation workflow is designed and optimized, which is capable of efficiently processing multi-sourced geospatial data, including LiDAR data and satellite imagery data....
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