Efficient Geometry-Controlled High-Resolution Satellite Image Synthesis

• URL: http://arxiv.org/abs/2605.04557v2
• Date: Wed, 13 May 2026 11:24:40 GMT
• Title: Efficient Geometry-Controlled High-Resolution Satellite Image Synthesis
• Abstract summary: High-resolution satellite images are often scarce and costly, especially for remote areas or infrequent events.<n>This shortage hampers the development and testing of machine learning models for land-cover classification, change detection, and disaster monitoring.<n>We tackle the problem of geometry-controlled high-resolution satellite image synthesis by adding control over existing pre-trained diffusion models.
• Score: 1.0195618602298684
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: High-resolution satellite images are often scarce and costly, especially for remote areas or infrequent events. This shortage hampers the development and testing of machine learning models for land-cover classification, change detection, and disaster monitoring. In this paper, we tackle the problem of geometry-controlled high-resolution satellite image synthesis by adding control over existing pre-trained diffusion models. We propose a simple yet efficient method for controlling the synthesis process by leveraging only skip connection features using windowed cross-attention modules. Several previously established control techniques are compared, indicating that our method achieves comparable performance while leading to a better alignment with the geometry control map. We also discuss the limitations in current evaluation approaches, amplifying the necessity of a consistent alignment assessment.

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