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Scalable Feature Extraction with Aerial and Satellite Imagery

Virginia Ng

Daniel Hofmann



Deep learning techniques have greatly advanced the performance of the already rapidly developing field of computer vision, which powers a variety of emerging technologies—from facial recognition to augmented reality to self-driving cars. The remote sensing and mapping communities are particularly interested in extracting, understanding and mapping physical elements in the landscape. These mappable physical elements are called features, and can include both natural and synthetic objects of any scale, complexity and character. Points or polygons representing sidewalks, glaciers, playgrounds, entire cities, and bicycles are all examples of features. In this paper we present a method to develop deep learning tools and pipelines that generate features from aerial and satellite imagery at large scale. Practical applications include object detection, semantic segmentation and automatic mapping of general-interest features such as turn lane markings on roads, parking lots, roads, water, building footprints.

We give an overview of our data preparation process, in which data from the Mapbox Satellite layer, a global imagery collection, is annotated with labels created from OpenStreetMap data using minimal manual effort. We then discuss the implementation of various state-of-the-art detection and semantic segmentation systems such as the improved version of You Only Look Once (YOLOv2), modified U-Net, Pyramid Scene Parsing Network (PSPNet), as well as specific adaptations for the aerial and satellite imagery domain. We conclude by discussing our ongoing efforts in improving our models and expanding their applicability across classes of features, geographical regions, and relatively novel data sources such as street-level and drone imagery.


computer vision, deep learning, neural networks, satellite imagery, aerial imagery



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