On the opportunities and challenges of foundation models for geospatial artificial intelligence
Large pre-trained models, also known as foundation models (FMs), are trained in a task-
agnostic manner on large-scale data and can be adapted to a wide range of downstream …
agnostic manner on large-scale data and can be adapted to a wide range of downstream …
[HTML][HTML] Machine learning for spatial analyses in urban areas: a scoping review
The challenges for sustainable cities to protect the environment, ensure economic growth,
and maintain social justice have been widely recognized. Along with the digitization …
and maintain social justice have been widely recognized. Along with the digitization …
GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond
Recent progress in Artificial Intelligence (AI) techniques, the large-scale availability of high-
quality data, as well as advances in both hardware and software to efficiently process these …
quality data, as well as advances in both hardware and software to efficiently process these …
Urban function classification at road segment level using taxi trajectory data: A graph convolutional neural network approach
Extracting hidden information from human mobility patterns is one of the long-standing
challenges of urban studies. In addition, exploring the relationship between urban functional …
challenges of urban studies. In addition, exploring the relationship between urban functional …
Sphere2Vec: A general-purpose location representation learning over a spherical surface for large-scale geospatial predictions
Generating learning-friendly representations for points in space is a fundamental and long-
standing problem in machine learning. Recently, multi-scale encoding schemes (such as …
standing problem in machine learning. Recently, multi-scale encoding schemes (such as …
Beyond Word2vec: An approach for urban functional region extraction and identification by combining Place2vec and POIs
The actual functions of a region may not reflect the intent of the original zoning scheme from
planners. To identify the actual urban functional regions, numerous methods have been …
planners. To identify the actual urban functional regions, numerous methods have been …
Analytics of location-based big data for smart cities: Opportunities, challenges, and future directions
The growing ubiquity of location/activity sensing technologies and location-based services
(LBS) has led to a large volume and variety of location-based big data (LocBigData), such …
(LBS) has led to a large volume and variety of location-based big data (LocBigData), such …
Learning urban region representations with POIs and hierarchical graph infomax
We present the hierarchical graph infomax (HGI) approach for learning urban region
representations (vector embeddings) with points-of-interest (POIs) in a fully unsupervised …
representations (vector embeddings) with points-of-interest (POIs) in a fully unsupervised …
A framework for urban land use classification by integrating the spatial context of points of interest and graph convolutional neural network method
Land-use classification plays an important role in urban planning and resource allocation
and had contributed to a wide range of urban studies and investigations. With the …
and had contributed to a wide range of urban studies and investigations. With the …
[PDF][PDF] Symbolic and subsymbolic GeoAI: Geospatial knowledge graphs and spatially explicit machine learning.
The field of Artificial Intelligence (AI) can be roughly divided into two branches: Symbolic
Artificial Intelligence and Connectionist Artificial Intelligence (or so-called Subsymbolic AI) …
Artificial Intelligence and Connectionist Artificial Intelligence (or so-called Subsymbolic AI) …