The emergence and rapid advancement of large language models (LLMs), represented by OpenAI’s Generative Pre-trained Transformer (GPT), has brought up new opportunities across various industries and disciplines. These cutting-edge technologies a...
Point cloud processing is an essential task in many applications in the AEC domain, such as automated progress assessment, quality control and 3D reconstruction. As much of the procedure used to process the point clouds is shared among these applicat...
While socioeconomic gradients in regional health inequalities are firmly established, the synergistic interactions between socioeconomic deprivation and climate vulnerability within convenient proximity and neighbourhood locations with health dispari...
Public urban spaces are vital settings for fostering social interaction among people. However, understanding how spatial layouts can promote positive social behaviors remains a critical and debated challenge for urban designers and planners aiming to...
As neighborhood walkability has gradually become an important topic in various fields, many cities around the world are promoting an eco-friendly and people-centered walking environment as a top priority in urban planning. The purpose of this study i...
The alteration of land use and land cover caused by human activities on a global scale has had a notable impact on ecosystem services at regional and global levels, which are crucial for the survival and welfare of human beings. Merbil, a small fresh...
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...
National-level traditional villages in Liandu District and Qingyuan County, Lishui City, in the southwestern mountainous area of Zhejiang Province serve as research objects in our exploration of the external representation and deep spatial structure...
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...
The accuracy of most SAR-based flood classification and segmentation derived from semi-automated algorithms is often limited due to complicated radar backscatter. However, deep learning techniques, now widely applied in image classifications, have de...
Sensing urban vitality is a useful method for understanding urban development. However, the spatio-temporal characteristics of the association between the built environment and urban vitality in Shenzhen, the youngest mega-city in China, have not yet...
With recent advancements, large language models (LLMs) such as ChatGPT and Bard have shown the potential to disrupt many industries, from customer service to healthcare. Traditionally, humans interact with geospatial data through software (e.g., ArcG...
Cloud and cloud shadow detection are essential in remote sensing imagery applications. Few semantic segmentation models were designed specifically for clouds and their shadows. Based on the visual and distribution characteristics of clouds and their...
Predicting house prices is a challenging task that many researchers have attempted to address. As accurate house prices allow better informing parties in the real estate market, improving housing policies and real estate appraisal, a comprehensive ov...
The past decade has witnessed an increasing frequency and intensity of disasters, from extreme weather, drought, and wildfires to hurricanes, floods, and wars. Providing timely disaster response and humanitarian aid to these events is a critical topi...
Landslide susceptibility assessment (LSA) based on machine learning methods has been widely used in landslide geological hazard management and research. However, the problem of sample imbalance in landslide susceptibility assessment, where landslide...
Flood is one of the most frequently occurring and devastating disasters in Nepal. Several locations in Nepal are at high risk of flood, which requires proper guidance on early warning and safe evacuation of people to emergency locations through optim...
Whenever natural and human-made disasters strike, the proper response of the concerned authorities often relies on search and rescue services. Search and rescue services are complex multidisciplinary processes that involve several degrees of interdep...
Crime is always one of the most important social problems, and it poses a great threat to public security and people. Accurate crime prediction can help the government, police, and citizens to carry out effective crime prevention measures. In this pa...
Recent times have witnessed wildfires causing harm to both ecological communities and urban–rural regions, underscoring the necessity to comprehend wildfire triggers and assess measures for mitigation. This research hones in on Cartagena del Ch...
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...
Nowadays, city forms are changing due to rapid urbanization and increasing population. In urban morphology studies, walkable street network is examined through the city form to create sustainable cities. This study aims to examine accessibility of st...
Building façade colors play an important role in influencing urban imageability, attraction and citizens’ experience. However, the relations between street functions and the building façade color distribution, color harmony and co...
Spatial epidemiology investigates the patterns and determinants of health outcomes over both space and time. Within this field, Bayesian spatiotemporal models have gained popularity due to their capacity to incorporate spatial and temporal dependenci...
Predicting ship trajectories plays a vital role in ensuring navigational safety, preventing collision incidents, and enhancing vessel management efficiency. The integration of advanced machine learning technology for precise trajectory prediction is...
Normalized difference vegetation index (NDVI) time series data, derived from optical images, play a crucial role for crop mapping and growth monitoring. Nevertheless, optical images frequently exhibit spatial and temporal discontinuities due to cloud...
Amid urban expansion, historic districts face challenges such as declining vitality and deteriorating spatial quality. Using the streets of Xi’an’s historical and cultural district as examples, this research utilizes multisource data, inc...
The occurrence of flash floods in urban catchments within the Mediterranean climate zone has witnessed a substantial rise due to climate change, underscoring the urgent need for early-warning systems. This paper examines the implementation of an earl...
Increasing interest has recently been shown towards the digital twins of cities. These urban digital twins utilize 3D city models together with other data sources, such as open data, which can be accessed, e.g., through application programming interf...
Architectural heritage refers to buildings, complexes, and sites with historical, cultural, artistic, technological, and geographical values, including ancient buildings, historical buildings, places of interest, dwellings, and industrial sites. Chin...
Road accidents are a worldwide problem, affecting millions of people annually. One way to reduce such accidents is to predict risk areas and alert drivers. Advanced research has been carried out on identifying accident-influencing factors and potenti...
In recent years, there has been an increasing demand for personalized experiences in various domains, including the cultural and educational sectors. Museums, as custodians of art, history, and scientific knowledge, are seeking innovative ways to eng...
The development of the county economy in China is a complicated process that is influenced by many factors in different ways. This study is based on multi-source big data, such as Tencent user density (TUD) data and point of interest (POI) data, to c...
In the face of persistent challenges posed by urbanization and climate change, the contemporary era has witnessed a growing urgency for urban intelligence and sustainable development. Consequently, a plethora of smart city schedules and policies have...
The visual quality and spatial distribution of architectural styles represent a city’s image, influence inhabitants’ living conditions, and may have positive or negative social consequences which are critical to urban sensing and designin...
House prices have long been closely related to the built environment of cities, yet whether the subjective perception (SP) of these environments has a differing effect on prices at multiple urban scales is unclear. This study sheds light on the impac...
Compactification of cities, i.e., the opposite of urban sprawl, has been increasingly presented in the literature as a possible solution to reduce the carbon footprint and promote the sustainability of current urban environments. Compact environments...
Gridded gross domestic product (GDP) data are a crucial land surface parameter for many geoscience applications. Recently, machine learning approaches have become powerful tools in generating gridded GDP data. However, most machine learning approache...
In late 2023, the image-reading capability added to a Generative Pre-trained Transformer (GPT) framework provided the opportunity to potentially revolutionize the way we view and understand geographic maps, the core component of cartography, geograph...
Existing research on automatic river network classification methods has difficulty scientifically quantifying and determining feature threshold settings and evaluating weights when calculating multi-indicator features of the local and overall structu...
Exploring the correlation of the built environment with metro ridership is vital for fostering sustainable urban growth. Although the research conducted in the past has explored how ridership is nonlinearly influenced by the built environment, less r...
Agricultural intensification practices have been adopted in the Brazilian savanna (Cerrado), mainly in the transition between Cerrado and the Amazon Forest, to increase productivity while reducing pressure for new land clearing. Due to the growing de...
Rapid damage identification and classification in disastrous situations and natural disasters are crucial for efficiently directing aid and resources. With the development of deep learning techniques and the availability of imagery content on social...
Mountainous cities face various land use challenges, including complex topography, low land use efficiency, and the insufficient control of land use in small-scale areas at the urban fringe. Considering population changes, environmental conservation,...
Riverine coastal megacities, particularly in semi-arid South Asian regions, face escalating environmental challenges due to rapid urbanization and climate change. While previous studies have examined urban growth patterns or environmental impacts ind...
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...
Natural hazards threaten millions of people all over the world. To address this risk, exposure and vulnerability models with high resolution data are essential. However, in many areas of the world, exposure models are rather coarse and are aggregated...
Shallow landslides due to hydro-meteorological factors are one of the most common destructive geological processes, which have become more frequent in recent years due to changes in rainfall frequency and intensity. The present study assessed a dynam...
Crop classification using remote sensing data has emerged as a prominent research area in recent decades. Studies have demonstrated that fusing synthetic aperture radar (SAR) and optical images can significantly enhance the accuracy of classification...
A tropical cyclone is a significant natural phenomenon that results in substantial socio-economic and environmental damage. These catastrophes impact millions of people every year, with those who live close to coastal areas being particularly affecte...
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