xView is one of the largest publicly available datasets of overhead imagery. It contains images from complex scenes around the world, annotated using bounding boxes. The DIUx xView 2018 Detection Challenge is focused on accelerating progress in four computer vision frontiers:
xView follows in the footsteps of challenges such as Common Objects in Context (COCO) and seeks to build off SpaceNet and Functional Map of the World (FMoW) to apply computer vision to the growing amount of available imagery from space so that we can understand the visual world in new ways and address a range of important applications.
xView comes with a pre-trained baseline model using the TensorFlow object detection API, as well as an example for PyTorch. More details about the dataset and initial experiments can be found in our NIPS poster presented at the Machine Learning for the Developing World workshop.
xView features a diverse collection of small, rare, fine grained, and multi-type objects with bounding box annotation.
Applying computer vision to overhead imagery has the potential to detect emerging natural disasters, improve response, quantify the direct and indirect impact — and save lives.
Interviews with disaster response and public safety experts informed xView’s 60-class ontology. We hope that xView will enable research and applications for important disaster relief missions.