High resolution remote sensing imagery is used in a broad range of tasks, including detection and classification of objects. It is, however, expensive to obtain, while lower resolution imagery is often freely available and can be used for a range of social good applications. To that end, we curate a multi-image multi-spectral dataset for super-resolution of satellite images. We use PlanetScope imagery from the SpaceNet-7 challenge as the high resolution reference and multiple Sentinel-2 revisits of the same location as the low-resolution imagery. We provide baselines for both single imager super-resolution and multi-image super-resolution. We also provide an ablation on how number of scenes, cloud cover and dynamism in different scenes in the dataset affect performance. Finally, we provide our code to construct the dataset along with implementations of baselines for the community to build upon.