Chenxing Sun

Chenxing Sun, Jing Bai

In intelligent cartographic generation tasks empowered by generative models, the authenticity of synthesized maps constitutes a critical determinant. Concurrently, the selection of appropriate evaluation metrics to quantify map authenticity emerges as a pivotal research challenge. Current methodologies predominantly adopt computer vision-based image assessment metrics to compute discrepancies between generated and reference maps. However, conventional visual similarity metrics-including L1, L2, SSIM, and FID-primarily operate at pixel-level comparisons, inadequately capturing cartographic global features and spatial correlations, consequently inducing semantic-structural artifacts in generated outputs. This study introduces a novel Map Feature Perception Metric designed to evaluate global characteristics and spatial congruence between synthesized and target maps. Diverging from pixel-wise metrics, our approach extracts elemental-level deep features that comprehensively encode cartographic structural integrity and topological relationships. Experimental validation demonstrates MFP's superior capability in evaluating cartographic semantic features, with classification-enhanced implementations outperforming conventional loss functions across diverse generative frameworks. When employed as optimization objectives, our metric achieves performance gains ranging from 2% to 50% across multiple benchmarks compared to traditional L1, L2, and SSIM baselines. This investigation concludes that explicit consideration of cartographic global attributes and spatial coherence substantially enhances generative model optimization, thereby significantly improving the geographical plausibility of synthesized maps.

Chenxing Sun, Yongyang Xu, Xuwei Xu et al.

Multi-scale tile maps are essential for geographic information services, serving as fundamental outcomes of surveying and cartographic workflows. While existing image generation networks can produce map-like outputs from remote sensing imagery, their emphasis on replicating texture rather than preserving geospatial features limits cartographic validity. Current approaches face two fundamental challenges: inadequate integration of cartographic generalization principles with dynamic multi-scale generation and spatial discontinuities arising from tile-wise generation. To address these limitations, we propose a scale-aware cartographic generation framework (SCGM) that leverages conditional guided diffusion and a multi-scale cascade architecture. The framework introduces three key innovations: a scale modality encoding mechanism to formalize map generalization relationships, a scale-driven conditional encoder for robust feature fusion, and a cascade reference mechanism ensuring cross-scale visual consistency. By hierarchically constraining large-scale map synthesis with small-scale structural priors, SCGM effectively mitigates edge artifacts while maintaining geographic fidelity. Comprehensive evaluations on cartographic benchmarks confirm the framework's ability to generate seamless multi-scale tile maps with enhanced spatial coherence and generalization-aware representation, demonstrating significant potential for emergency mapping and automated cartography applications.