[HTML][HTML] A review of spatially-explicit GeoAI applications in Urban Geography
P Liu, F Biljecki - International Journal of Applied Earth Observation and …, 2022 - Elsevier
Urban Geography studies forms, social fabrics, and economic structures of cities from a
geographic perspective. Catalysed by the increasingly abundant spatial big data, Urban …
geographic perspective. Catalysed by the increasingly abundant spatial big data, Urban …
A review of location encoding for GeoAI: methods and applications
ABSTRACT A common need for artificial intelligence models in the broader geoscience is to
encode various types of spatial data, such as points, polylines, polygons, graphs, or rasters …
encode various types of spatial data, such as points, polylines, polygons, graphs, or rasters …
Csp: Self-supervised contrastive spatial pre-training for geospatial-visual representations
Geo-tagged images are publicly available in large quantities, whereas labels such as object
classes are rather scarce and expensive to collect. Meanwhile, contrastive learning has …
classes are rather scarce and expensive to collect. Meanwhile, contrastive learning has …
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 …
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 …
Multi-scale representation learning for spatial feature distributions using grid cells
Unsupervised text encoding models have recently fueled substantial progress in NLP. The
key idea is to use neural networks to convert words in texts to vector space representations …
key idea is to use neural networks to convert words in texts to vector space representations …
TransGCN: Coupling transformation assumptions with graph convolutional networks for link prediction
Link prediction is an important and frequently studied task that contributes to an
understanding of the structure of knowledge graphs (KGs) in statistical relational learning …
understanding of the structure of knowledge graphs (KGs) in statistical relational learning …
SE‐KGE: A location‐aware Knowledge Graph Embedding model for Geographic Question Answering and Spatial Semantic Lifting
Learning knowledge graph (KG) embeddings is an emerging technique for a variety of
downstream tasks such as summarization, link prediction, information retrieval, and question …
downstream tasks such as summarization, link prediction, information retrieval, and question …
Towards general-purpose representation learning of polygonal geometries
Neural network representation learning for spatial data (eg, points, polylines, polygons, and
networks) is a common need for geographic artificial intelligence (GeoAI) problems. In …
networks) is a common need for geographic artificial intelligence (GeoAI) problems. In …
Geographic question answering: challenges, uniqueness, classification, and future directions
As an important part of Artificial Intelligence (AI), Question Answering (QA) aims at
generating answers to questions phrased in natural language. While there has been …
generating answers to questions phrased in natural language. While there has been …