SatelliteCloudGenerator: Controllable Cloud and Shadow Synthesis for Multi-Spectral Optical Satellite Images

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 number of high-quality simulated pair data with controllable parameters to adjust cloud appearance, with no annotation cost. The tool is available as open-source. 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.

Automated summary

This work proposes an open-source simulation tool that can generate a diverse and unlimited number of high-quality simulated data, which can be used to adjust cloud appearance without any annotation cost. The performance of models trained on simulated data for cloud detection and removal approaches that of models trained on real data, demonstrating the quality and utility of the generated clouds.