SatSynth: Augmenting Image-Mask Pairs through Diffusion Models for Aerial Semantic Segmentation

arXiv:2403.16605v1 Announce Type: new
Abstract: In recent years, semantic segmentation has become a pivotal tool in processing and interpreting satellite imagery. Yet, a prevalent limitation of supervised learning techniques remains the need for extensive manual annotations by experts. In this work, we explore the potential of generative image diffusion to address the scarcity of annotated data in earth observation tasks. The main idea is to learn the joint data manifold of images and labels, leveraging recent advancements in denoising diffusion …

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