  The electronic map plays a crucial role in geographic information systems,
serving various urban managerial scenarios and daily life services. Developing
effective Map Entity Representation Learning (MERL) methods is crucial to
extracting embedding information from electronic maps and converting map
entities into representation vectors for downstream applications. However,
existing MERL methods typically focus on one specific category of map entities,
such as POIs, road segments, or land parcels, which is insufficient for
real-world diverse map-based applications and might lose latent structural and
semantic information interacting between entities of different types. Moreover,
using representations generated by separate models for different map entities
can introduce inconsistencies. Motivated by this, we propose a novel method
named HOME-GCL for learning representations of multiple categories of map
entities. Our approach utilizes a heterogeneous map entity graph (HOME graph)
that integrates both road segments and land parcels into a unified framework. A
HOME encoder with parcel-segment joint feature encoding and heterogeneous graph
transformer is then deliberately designed to convert segments and parcels into
representation vectors. Moreover, we introduce two types of contrastive
learning tasks, namely intra-entity and inter-entity tasks, to train the
encoder in a self-supervised manner. Extensive experiments on three large-scale
datasets covering road segment-based, land parcel-based, and trajectory-based
tasks demonstrate the superiority of our approach. To the best of our
knowledge, HOME-GCL is the first attempt to jointly learn representations for
road segments and land parcels using a unified model.
