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 …
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 …
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 …
Towards a foundation model for geospatial artificial intelligence (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 …
[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 …
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 …
Agi for agriculture
Artificial General Intelligence (AGI) is poised to revolutionize a variety of sectors, including
healthcare, finance, transportation, and education. Within healthcare, AGI is being utilized to …
healthcare, finance, transportation, and education. Within healthcare, AGI is being utilized to …
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 …