  Unsupervised region representation learning aims to extract dense and
effective features from unlabeled urban data. While some efforts have been made
for solving this problem based on multiple views, existing methods are still
insufficient in extracting representations in a view and/or incorporating
representations from different views. Motivated by the success of contrastive
learning for representation learning, we propose to leverage it for multi-view
region representation learning and design a model called ReMVC (Region
Embedding with Multi-View Contrastive Learning) by following two guidelines: i)
comparing a region with others within each view for effective representation
extraction and ii) comparing a region with itself across different views for
cross-view information sharing. We design the intra-view contrastive learning
module which helps to learn distinguished region embeddings and the inter-view
contrastive learning module which serves as a soft co-regularizer to constrain
the embedding parameters and transfer knowledge across multi-views. We exploit
the learned region embeddings in two downstream tasks named land usage
clustering and region popularity prediction. Extensive experiments demonstrate
that our model achieves impressive improvements compared with seven
state-of-the-art baseline methods, and the margins are over 30% in the land
usage clustering task.
