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 …
Z Li - Computers, Environment and Urban Systems, 2022 - Elsevier
Abstract Machine learning and artificial intelligence (ML/AI), previously considered black box approaches, are becoming more interpretable, as a result of the recent advances in …
G Mai, N Lao, Y He, J Song… - … Conference on Machine …, 2023 - proceedings.mlr.press
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 …
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 …
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 …
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) …
H Wang, Q Yu, Y Liu, D Jin, Y Li - Proceedings of the ACM on interactive …, 2021 - dl.acm.org
With the rapid development of the mobile communication technology, mobile trajectories of humans are massively collected by Internet service providers (ISPs) and application service …
Learning knowledge graph (KG) embeddings is an emerging technique for a variety of downstream tasks such as summarization, link prediction, information retrieval, and question …
Neural network representation learning for spatial data (eg, points, polylines, polygons, and networks) is a common need for geographic artificial intelligence (GeoAI) problems. In …