Geospatial foundation models are a new frontier in artificial intelligence, designed to understand and analyze spatial data at scale. Trained on huge sets of EO data, these models can support a wide range of applications—from monitoring natural...
Urban community safety increasingly depends on transforming citizen-generated information into structured, traceable, and territorially meaningful data, particularly where neighborhood coordination relies on informal communication channels with limit...
The Chinese urban regeneration movement underscores a “people-oriented” paradigm, aimed at addressing urban challenges stemming from rapid prior urbanization, while striving for high-quality and sustainable urban development. At the commu...
This narrative review examines how big data analytics supports geospatial decision-making in smart cities through the combined roles of spatial data foundations, GeoAI methods, and urban digital twins. Methodologically, the article follows a structur...
Geospatial artificial intelligence (Geo-AI) methods have revolutionarily impacted earth observation and remote sensing [...]
GeoAI has maintained strong momentum since the conclusion of the 2024 Special Issue on “Advances in AI-Driven Geospatial Analytics and Data Generation” [...]
This study investigates the potential of fine-tuned large language models (LLMs) to enhance geospatial intelligence by translating natural language queries into executable Python code. Traditional GIS workflows, while effective, often lack usability...
Recent years have witnessed a revolution of artificial intelligence (AI) technologies, highlighted by the rise of generative AI and geospatial artificial intelligence (GeoAI) [...]
This study explores the use of artificial intelligence and geospatial tools to analyze historical explorers’ narratives. Explorers’ accounts provide valuable insights into the cultural, environmental, and logistical dynamics of exploratio...
This paper presents a systematic, narrative review of 62 academic publications (2015–2025) that explore the integration of spatial data infrastructures (SDIs) with emerging smart city technologies to improve local governance. SDIs provide a str...
In geography education, geospatial technologies have been actively researched and widely used in combination with classroom inquiry and fieldwork. Recently, emerging data sources and technologies, such as open data, big data, and Artificial Intellige...
Explainable Artificial Intelligence (XAI) has the potential to open up black-box machine learning models. XAI can be used to optimize machine learning models, to search for scientific findings, or to improve the understandability of the AI system for...
Urban growth and environmental pressures in rapidly transforming cities require innovative governance tools that integrate advanced technologies with institutional assessment. This study develops and applies a strategic integration framework that com...
Wildfires pose significant threats to ecosystems, economies, and human health. The integration of remote sensing (RS), geospatial information systems (GIS), and artificial intelligence (AI) has emerged as a powerful approach for addressing wildfire-r...
The development of Geospatial Artificial Intelligence (GeoAI), combining deep learning and remote sensing imagery, is of great interest for automated spatial inference and decision-making support. In this paper, a GeoAI-based efficient object detecti...
Remote sensing (RS) plays an important role gathering data in many critical domains (e.g., global climate change, risk assessment and vulnerability reduction of natural hazards, resilience of ecosystems, and urban planning). Retrieving, managing, and...
Geospatial sensing and data analytics can support maritime risk and safety management by transforming vessel-movement and environmental observations into transparent evidence for route assessment. Within this context, reducing maritime fuel consumpti...
Digital twins (DTs) are increasingly adopted to enhance the monitoring, management, and planning of urban infrastructure. While DT development for buildings is well established, applications to urban road networks remain limited, particularly in inte...
This study develops a globally adaptable and scalable methodology for high-resolution, building-level population mapping, integrating Earth observation techniques, geospatial data acquisition, and machine learning to enhance population estimation in...
Google Earth Engine (GEE) has become a central platform for planetary-scale geospatial analysis, but its rapid evolution in the last few years is not yet reflected in the existing review literature. Earlier reviews mainly describe the platform’...
Greek olive groves are subject to multiple thermal, water, biotic, and extreme-weather pressures, yet national-scale maps of their combined historical exposure remain limited. The present study quantified climate risk for olive cultivation across Gre...
The maritime traffic status is monitored through the Automatic Identification System (AIS) installed on vessels. AIS data record the trajectory of each ship. However, due to the short sampling interval of AIS data, there is a significant amount of re...
Open-source disaster intelligence (OSDI) is crucial for improving situational awareness, disaster preparedness, and real-time decision-making. Traditional OSDI frameworks often rely on social media data, which are susceptible to misinformation and cr...
Geographic Information Systems (GIS) are central to spatial evidence in Environmental Impact Assessment (EIA). In this review, GIS is used in a broad, integrative sense to refer to an ecosystem of geospatial technologies—such as remote sensing...
The increasing frequency and destructiveness of natural disasters necessitate scalable, transparent, and timely analytical frameworks for risk reduction. Traditional disaster datasets—curated by intergovernmental bodies such as EM-DAT and UNDRR...
Groundwater in northwest Tunisia plays a vital role in supporting the domestic, agriculture, industry, and tourism sectors. However, climate change and over-exploitation have led to significant degradation in groundwater quality and quantity. Traditi...
The contemporary digital landscape overwhelms visitors with fragmented and dynamic information, complicating travel planning and often leading to decision paralysis. This paper presents a real-world case study on the design and deployment of an intel...
Large Language Models (LLMs) such as GPT, BART, and Gemini stand at the forefront of Generative Artificial Intelligence, showcasing remarkable prowess in natural language comprehension and task execution. This paper proposes a novel framework develop...
Remote-sensing image analysis increasingly requires workflows that combine heterogeneous sensors, geospatial preprocessing, deep-learning models, uncertainty estimation, and domain-specific quality control. Conventional computer-vision pipelines are...
Global news can provide semantic and geographic observations that complement physical Earth sensing, but its analytical value depends on whether the resulting signals exhibit reproducible spatial and temporal structure. This proceeding paper reanalyz...
Global Digital Elevation Models (DEMs) exhibit systematic biases constrained by acquisition geometry and surface penetration. This study aims to evaluate whether the increasing complexity of geometric deep learning (e.g., Graph Neural Networks, GNNs)...
As commercial geospatial intelligence data becomes more widely available, algorithms using artificial intelligence need to be created to analyze it. Maritime traffic is annually increasing in volume, and with it the number of anomalous events that mi...
Natural disasters cause extensive infrastructure and significant economic losses, hindering sustainable development and impeding social and economic progress. More importantly, they jeopardize community well-being by causing injuries, damaging human...
Accurate subaqueous morphological modelling is crucial for sustainable aquatic ecosystem management, yet single-sensor hydrographic surveys remain constrained by environmental limitations. Through a systematic review, this study aims to evaluate the...
Post-socialist urban landscapes often undergo spontaneous transformation where formal governance is fragmented or discontinuous, yet the spatial logic of these transitions remains insufficiently quantified. This study examines Krzemionki, a landscape...
Faced with increasingly serious environmental problems, scientists have conducted extensive research, among which the importance of air quality prediction is becoming increasingly prominent. This article briefly reviews the utilization of geographic...
Previous studies have utilized machine learning algorithms that incorporate topographic and geological characteristics to model flood susceptibility, resulting in comprehensive flood maps. This study introduces an innovative integration of geospatial...
Urban mobility is a central challenge for sustainable and inclusive cities, as climate change, congestion, and spatial inequality increasingly reveal mobility patterns as expressions of deeper social and spatial structures. Inclusive urban mobility e...
The paper examines the development of forecasting and modeling technologies for environmental processes using classical and quantum data analysis methods. The main focus is on the integration of deep neural networks and classical algorithms, such as...
The rapid urbanization of Abha and its surrounding cities in Saudi Arabia’s mountainous regions poses challenges for sustainable and secure development. This study aimed to identify suitable sites for eco-friendly and safe building complexes am...
Extreme precipitation events, rapid urbanization, and irregular land use have significantly increased flood risk in recent years. In order to mitigate risks and enhance urban resilience, there is a need for the integration of innovative approaches wi...
This study aims to predict vital soil physical properties, including clay, sand, and silt, which are essential for agricultural management and environmental protection. Precision distribution of soil texture is crucial for effective land resource man...
Intensified human activities and changes in land-use patterns have led to numerous eco-environmental challenges. A comprehensive understanding of the eco-environmental effects of land-use transitions and their driving mechanisms is essential for deve...
The convergence of big data and geospatial computing has brought challenges and opportunities to GIScience with regards to geospatial data management, processing, analysis, modeling, and visualization. This special issue highlights recent advancement...
The increasing volume of data from IoT sensors has made manual inspection time-consuming and prone to bias, particularly for spatiotemporal air pollution maps. While rule-based methods are adequate for simple datasets or individual maps, they are ins...
Nowadays, European program Copernicus’ Sentinel missions have allowed the development of several application services. In this regard, to strengthen the use of free satellite data in ordinary administrative workflows, this work aims to evaluate...
Trees play a critical role in climate regulation, biodiversity, and carbon storage as they cover approximately 30% of the global land area. Nowadays, Machine Learning (ML)is key to automating large-scale tree species classification based on active an...
One longstanding complication with Earth data discovery involves understanding a user’s search intent from the input query. Most of the geospatial data portals use keyword-based match to search data. Little attention has focused on the spatial and te...
The integration of geo-spatial databases, advanced modeling, and artificial intelligence provides novel opportunities to investigate regional differentiation with direct implications for urban science and regional development. To avoid homogeneous co...
Driven by the widespread adoption of deep learning (DL) in crop mapping with satellite image time series (SITS), this study was motivated by the recent success of temporal attention-based approaches in crop mapping. To meet the needs of beekeepers, t...
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