Graph Representation Learning for Street Networks

Explore graph representation learning for street networks in this 40-minute presentation by Mateo Neira Álvarez from UCL. Delivered at the weekly DataLearning working group meeting on February 21, 2023, the talk delves into innovative techniques for analyzing and representing complex urban street layouts using graph-based machine learning approaches. Gain insights into how these methods can be applied to better understand and optimize city infrastructure. As part of an interdisciplinary series featuring invited speakers, this presentation showcases cutting-edge research at the intersection of data assimilation and machine learning, offering valuable knowledge for urban planners, data scientists, and researchers interested in spatial analysis and network optimization.

Data Learning: Graph Representation learning for street networks