  The increasingly common use of incidental satellite images for stereo
reconstruction versus rigidly tasked binocular or trinocular coincident
collection is helping to enable timely global-scale 3D mapping; however,
reliable stereo correspondence from multi-date image pairs remains very
challenging due to seasonal appearance differences and scene change. Promising
recent work suggests that semantic scene segmentation can provide a robust
regularizing prior for resolving ambiguities in stereo correspondence and
reconstruction problems. To enable research for pairwise semantic stereo and
multi-view semantic 3D reconstruction with incidental satellite images, we have
established a large-scale public dataset including multi-view, multi-band
satellite images and ground truth geometric and semantic labels for two large
cities. To demonstrate the complementary nature of the stereo and segmentation
tasks, we present lightweight public baselines adapted from recent state of the
art convolutional neural network models and assess their performance.
