MapLayNet: map layout representation learning using weakly supervised structure-aware graph neural networks

doi:10.1080/15230406.2025.2533316

MapLayNet: map layout representation learning using weakly supervised structure-aware graph neural networks

2025-08-12

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王光霞 (Guangxia Wang) *

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Liqiu Meng

DOI：10.1080/15230406.2025.2533316

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Openly accessible map images have become a valuable source for geographic information and cartographic design. Map layout becomes a natural leverage point to unlock fine-grained map information as it facilitates the expression of map content through the spatial coordination of map elements. Current layout studies focus on documents, user interfaces, and floor plans, leading to models that poorly recognize complex map layouts. Inspired by the strong performance of graph neural networks (GNNs) in modeling element-wise layout relationships, this paper proposes MapLayNet, a GNN-based model for map layout representation learning. The model introduces the structural features and proposes a dual-branch network architecture to enhance the model representation of complex map layouts at global and local scales. To ensure model generalization for diverse map layout designs, we employed a weakly supervised learning strategy and trained the model using a heuristic-based triplet loss of map layout similarity, along with a mutual information loss that effectively utilizes the dual-branch architecture. For model evaluation, MapLayNet has outperformed all baseline methods in similarity and stability by 3.2% and 10.3%, respectively, while showing better consistency against human evaluation on a map layout retrieval task. With the effective model design, the embedding learned by MapLayNet is capable of generating a concept hierarchy with compactness in lower-level layout patterns and partial-ordered concept relations in the higher-level layout abstraction. Potential applications such as map layout retrieval and design recommendation can be envisioned with the resulting interpretable and manipulable map layout embedding.

Keywords:

Map layout

GNN

structure-aware

layout similarity

ubiquitous map

embedding model

layout retrieval

MapLayNet: map layout representation learning using weakly supervised structure-aware graph neural networks

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