Large scale overhead image summarization

In this work, we aim to address the needs of human analysts to automatically summarize the content of large swaths of overhead imagery. We present our approach to this problem using deep neural networks, providing detection and segmentation information to enable fine-grained description of scene content for human ingestion. Four different perception systems were run on blocks of large-scale satellite imagery: (1) semantic segmentation of roads, buildings, and vegetation; (2) zone segmentation to identify commercial, industrial, residential, and airport zones; (3) classification of objects such as helipads, silos, and water towers; and (4) object detection to find vehicles. Results are filtered based on a user's zoom level in the swath, and subsequently summarized as textual bullets and statistics. Our framework blocks the image swaths at a resolution of approximately 30cm for each perception system. For semantic …