All images in a data cube are the same shape, though some data cubes have shape 1024 x 1024 pixels, while others have a shape of 1024 x 1023 pixels. For each of the Areas Of Interest (AOIs), the data cube extends for roughly two years, though it varies somewhat between AOIs. Imagery consists of RBGA (red, green, blue, alpha) 8-bit electro-optical (EO) monthly mosaics from Planet’s Dove constellation at 4 meter resolution. The stakeholders of this contest believe this challenge will aid efforts to develop useful tools for overhead change detection.ĬosmiQ Works’ blog, The DownLinQ, provides additional background information on this challenge, details on the SCOT metric used for scoring, and an overview of the dataset used in the challenge.įor an overview of the dataset see here. SpaceNet 7 poses a unique challenge from a computer vision standpoint because of the small pixel area of each object, the high object density within images, and the dramatic image-to-image difference compared to frame-to-frame variation in video object tracking. Challenge participants are asked to track individual building construction over time, thereby directly assessing urbanization. The dataset comprises >40,000 square kilometers of imagery and exhaustive polygon labels of building footprints in the imagery, totaling over 10 million individual annotations. Each geography features significant urban change over the two-year timespan. The competition uses a new open source dataset of Planet satellite imagery mosaics, which includes 24 images (one per month) covering ~100 unique geographies. Beyond its relevance for disaster response, disease preparedness, and environmental monitoring, this task poses interesting technical challenges for the computer vision community.
Spacenet 7 series#
SpaceNet 7 will be featured as a competition at the 2020 NeurIPS conference in December, and seeks to identify and track buildings in a time series of satellite imagery collected over rapidly urbanizing areas. The SpaceNet 7 Multi-Temporal Urban Development Challenge aims to improve these methods, while simultaneously advancing the state of the art in SpaceNet’s core mission of foundational mapping.
Satellite imagery analytics have numerous human development and disaster response applications, particularly when time series methods are involved. The SpaceNet Challenge, round 7 Multi-Temporal Urban Development Challenge
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