  Deep learning models in the Earth Observation domain heavily rely on the
availability of large-scale accurately labeled satellite imagery. However,
obtaining and labeling satellite imagery is a resource-intensive endeavor.
While generative models offer a promising solution to address data scarcity,
their potential remains underexplored. Recently, Denoising Diffusion
Probabilistic Models (DDPMs) have demonstrated significant promise in
synthesizing realistic images from semantic layouts. In this paper, a
conditional DDPM model capable of taking a semantic map and generating
high-quality, diverse, and correspondingly accurate satellite images is
implemented. Additionally, a comprehensive illustration of the optimization
dynamics is provided. The proposed methodology integrates cutting-edge
techniques such as variance learning, classifier-free guidance, and improved
noise scheduling. The denoising network architecture is further complemented by
the incorporation of adaptive normalization and self-attention mechanisms,
enhancing the model's capabilities. The effectiveness of our proposed model is
validated using a meticulously labeled dataset introduced within the context of
this study. Validation encompasses both algorithmic methods such as Frechet
Inception Distance (FID) and Intersection over Union (IoU), as well as a human
opinion study. Our findings indicate that the generated samples exhibit minimal
deviation from real ones, opening doors for practical applications such as data
augmentation. We look forward to further explorations of DDPMs in a wider
variety of settings and data modalities. An open-source reference
implementation of the algorithm and a link to the benchmarked dataset are
provided at https://github.com/obaghirli/syn10-diffusion.
