Map Feature Perception Metric for Map Generation Quality Assessment and Loss Optimization
Jing Bai, Chenxing Sun, Hongyu Chen, Xiechun Lu, Zhanlong Chen · Remote Sensing · 2026
Evaluating the quality of synthesized maps remains a critical challenge in generative cartography. Prevailing methods rely on pixel-wise computer vision metrics (e.g., PSNR, SSIM). However, these metrics prioritize low-level signal fidelity over high-level geographical logic features and treat pixels as independent units, which prevents them from capturing the complex topological interdependencies and global semantics inherent in maps. Consequently, they inadequately assess essential cartographic features and spatial relationships, often producing outputs with semantic and structural artifacts. To address this limitation, we introduce the map feature perception (MFP) metric, a novel approach that quantifies disparities in high-level cartographic structures and spatial configurations. Unlike pixel-based comparisons, MFP extracts deep elemental-level features to encode cartographic structural integrity and topological relationships comprehensively. Experimental validation demonstrates MFP’s superior capability in evaluating cartographic semantics. Furthermore, when implemented as a loss function, our MFP-based objective consistently outperforms conventional loss functions across diverse generative frameworks and benchmarks. Our findings establish that explicitly optimizing for cartographic features and spatial coherence is crucial for enhancing the geographical plausibility of synthesized maps.