Cloud shadows dramatically affect the appearance of outdoor scenes. We describe two approaches that use video of cloud shadows to estimate a cloudmap, a spatio-temporal function that represents the clouds passing over the scene. Our first method makes strong assumptions about the camera geometry and estimates the cloud motion direction. Our second method uses techniques from manifold learning and does not require known geometry. Neither method requires directly viewing the clouds, but instead uses the pattern of intensity changes caused by the cloud shadows. We show renderings of cloudmaps extracted using both methods from videos of real outdoor scenes as well as quantitative results on synthetic datasets. An accurate estimate of the cloudmap has potential applications in surveillance and graphics, as well as scientific studies that depend on solar radiation.