Optical satellite images of Earth frequently contain cloud cover and shadows. This requires processing pipelines to recognize the presence, location, and features of the cloud-affected regions. Models that make predictions about the ground behind the clouds face the challenge of lacking ground-truth information, i.e. the exact state of Earth’s surface. Currently, the solution to that is to either (i) create pairs from samples acquired at different times, or (ii) simulate cloudy data based on a clear acquisition. This work follows the second approach and proposes an open-source simulation tool, capable of generating a diverse and unlimited amount of high-quality simulated pair data with controllable parameters to adjust cloud appearance, with no annotation cost. The tool is available at https://github.com/strath-ai/SatelliteCloudGenerator. An indication of the quality and utility of the generated clouds is demonstrated by the models for cloud detection and cloud removal trained exclusively on simulated data, which approach the performance of their equivalents trained on real data
