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 …
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 …
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 …
[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) …
The challenges of integrating explainable artificial intelligence into GeoAI
J Xing, R Sieber - Transactions in GIS, 2023 - Wiley Online Library
Although explainable artificial intelligence (XAI) promises considerable progress in
glassboxing deep learning models, there are challenges in applying XAI to geospatial …
glassboxing deep learning models, there are challenges in applying XAI to geospatial …
On the opportunities and challenges of foundation models for geoai (vision paper)
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 …
EVKG: An interlinked and interoperable electric vehicle knowledge graph for smart transportation system
Over the past decade, the electric vehicle (EV) industry has experienced unprecedented
growth and diversification, resulting in a complex ecosystem. To effectively manage this …
growth and diversification, resulting in a complex ecosystem. To effectively manage this …
A hypergraph-based hybrid graph convolutional network for intracity human activity intensity prediction and geographic relationship interpretation
Human activity intensity prediction, ie, estimating the dynamic population distribution, is
crucial to many location-based applications, particularly intelligent transportation systems …
crucial to many location-based applications, particularly intelligent transportation systems …
Qualitative spatial reasoning with uncertain evidence using Markov logic networks
Probabilistic logics combine the ability to reason about complex scenes, with a rigorous
approach to uncertainty. This paper explores the construction of probabilistic spatial logics …
approach to uncertainty. This paper explores the construction of probabilistic spatial logics …
[HTML][HTML] SpatialScene2Vec: A self-supervised contrastive representation learning method for spatial scene similarity evaluation
Spatial scene similarity plays a crucial role in spatial cognition, as it enables us to
understand and compare different spatial scenes and their relationships. However …
understand and compare different spatial scenes and their relationships. However …