Satellite-based assessment of power tower damage is essential for rapid disaster response but is challenged by the scarcity of damage samples and the cross-scale mismatch between close-range UAV imagery and satellite imagery. Existing data augmentation methods, including copy-based strategies and diffusion-based generation, often fail to produce reliable samples due to their dependence on the training data distribution and the lack of explicit control over object scale and domain discrepancy. To address these issues, we propose a scale-constrained and frequency-adaptive diffusion-based data construction framework that explicitly models the scale distribution prior of power towers in the remote sensing domain and incorporates frequency-domain adaptation before image generation. Specifically, scale-aware instance embedding is used to construct training samples that conform to satellite-scale statistics, while frequency-domain adaptation is introduced to reduce spectral and texture discrepancies between UAV-derived damaged references and satellite imagery. A diffusion-based inpainting model is then trained on the constructed dataset to reconstruct damage at original tower locations. The experimental results, including feature statistical analysis and downstream change detection validation, demonstrate that the proposed method achieves better alignment with real satellite-scale distributions, reduces geometric and spectral–textural inconsistencies, and improves boundary continuity and structural realism under cross-resolution conditions.