66 citations · 74 across the 5 of their papers we have counts for
5 papers
Self-Supervised Representation Learning for Geospatial Objects: A Survey
Yile Chen, Weiming Huang, Kaiqi Zhao +2
The proliferation of various data sources in urban and territorial environments has significantly facilitated the development of geospatial artificial intelligence (GeoAI) across a…
Road Network Representation Learning with the Third Law of Geography
Haicang Zhou, Weiming Huang, Yile Chen +3
Road network representation learning aims to learn compressed and effective vectorized representations for road segments that are applicable to numerous tasks. In this paper, we id…
LAMP: A Language Model on the Map
Pasquale Balsebre, Weiming Huang, Gao Cong
Large Language Models (LLMs) are poised to play an increasingly important role in our lives, providing assistance across a wide array of tasks. In the geospatial domain, LLMs have…
City Foundation Models for Learning General Purpose Representations from OpenStreetMap
Pasquale Balsebre, Weiming Huang, Gao Cong +1
Pre-trained Foundation Models (PFMs) have ushered in a paradigm-shift in Artificial Intelligence, due to their ability to learn general-purpose representations that can be readily…
On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence
Gengchen Mai, Weiming Huang, Jin Sun +11
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 tasks by…