超越边缘图：面向多适配器地图到卫星扩散的小波域条件控制
Beyond Edge Maps: Wavelet-Domain Conditioning for Multi-Adapter Map-to-Satellite Diffusion
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- Tribhuvan University（特里布文大学）
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中文总结 AI 辅助
本文针对资源匮乏地区卫星底图缺失问题，提出基于OSM栅格地图和SWT子带的多适配器ControlNet扩散框架，在尼泊尔数据集和Pix2Pix基准上验证了其合成卫星图像的有效性。
中文摘要 AI 辅助
在资源匮乏地区，商业测绘合作往往难以开展，导致卫星底图更新滞后，因此催生了利用独立维护的制图数据合成卫星图像的需求。现有基于ControlNet的扩散方法通常以从目标图像本身提取的边缘或分割等结构信号为条件，默认目标图像已存在，这恰好限制了其在合成需求最突出场景中的应用。以地图为条件的替代方案虽加入了边缘检测等线索，但未考虑频域结构。本文提出一种基于ControlNet的扩散框架，仅以独立于目标图像即可获取的制图源为条件：OpenStreetMap（OSM）栅格地图及其平稳小波变换（SWT）子带，这是此前地图到卫星扩散领域未探索过的条件信号。在冻结的Stable Diffusion主干之上，分别针对地图和小波表示训练两个ControlNet适配器，通过MultiControlNet进行融合，无需重新训练多输入模型即可同时利用空间结构和频域细节。我们针对尼泊尔这一数据稀缺、地形多样的区域整理了新的配对地图-卫星数据集，并结合Pix2Pix地图-卫星基准进行评估。在8项指标-数据集对比中，组合条件在两个数据集上的SSIM和PSNR均胜出，在我们的数据集上的LPIPS（Alex和VGG主干）也胜出，在Pix2Pix的两个LPIPS主干上与仅地图条件持平，同时在该数据集上仍略优于仅小波条件；仅小波条件在两个数据集上取得最低FID，平衡了单图像保真度和分布真实性。鉴于我们的测试集规模较小，且FID存在已知的小样本偏差，我们对这一结果持谨慎态度。
英文摘要
Commercial mapping partnerships are often unavailable in low-resource regions, leaving satellite basemaps stale and motivating synthesis of satellite imagery from independently maintained cartographic data. Existing ControlNet-based diffusion methods typically condition on structural signals like edges or segmentation extracted from the target image itself, assuming the imagery already exists and limiting their use exactly where synthesis matters most. Map-conditioned alternatives add cues like edge detection but omit frequency-domain structure. We propose a ControlNet-based diffusion framework conditioned only on cartographic sources obtainable independently of the target imagery: OpenStreetMap (OSM) raster maps and their stationary wavelet transform (SWT) subbands, a conditioning signal previously unexplored for map-to-satellite diffusion. Two ControlNet adapters, trained separately on the map and wavelet representations atop a frozen Stable Diffusion backbone, are fused via MultiControlNet, jointly drawing on spatial structure and frequency detail without retraining a multi-input model. We evaluate on a new paired map-satellite dataset curated for Nepal, a data-scarce, topographically diverse region, alongside the Pix2Pix maps-satellite benchmark. Combined conditioning wins six of eight metric-dataset comparisons -- SSIM and PSNR on both datasets, plus LPIPS (both Alex and VGG backbones) on ours and ties map-only on both Pix2Pix LPIPS backbones while still edging past wavelet-only there. Wavelet-only takes the lowest FID on both datasets, matching the tradeoff between per-image fidelity and distributional realism. We treat this gap cautiously given our modest test-set sizes and FID's known small-sample bias.
