Adapted by Daniel Hogan from a post by Adam Van Etten and Nick Weir

Preface: SpaceNet LLC is a nonprofit organization dedicated to accelerating open source, artificial intelligence applied research for geospatial applications, specifically foundational mapping (i.e., building footprint & road network detection). SpaceNet is run in collaboration by co-founder and managing partner CosmiQ Works, co-founder and co-chair Maxar Technologies, and our partners including Amazon Web Services (AWS), Capella Space, Topcoder, IEEE GRSS, the National Geospatial-Intelligence Agency and Planet.

The SpaceNet 7 Challenge centers on a unique time-series dataset, with monthly collects over an approximately-two-year period for more than 100 locations spanning the globe. Challenge participants are asked to identify building footprints in the imagery and, furthermore, to track buildings from month to month revealing the temporal structure of the data. …


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Daniel Hogan and Adam Van Etten

Preface: SpaceNet LLC is a nonprofit organization dedicated to accelerating open source, artificial intelligence applied research for geospatial applications, specifically foundational mapping (i.e. building footprint and road network detection). SpaceNet is solely managed by co-founder, In-Q-Tel CosmiQ Works, in collaboration with co-founder and co-chair, Maxar Technologies, and the other partners: Amazon Web Services (AWS), Capella Space, Topcoder, Institute of Electrical and Electronics Engineers (IEEE) Geoscience and Remote Sensing Society (GRSS), the National Geospatial-Intelligence Agency (NGA) and Planet.

The SpaceNet 7 Multi-Temporal Urban Development Challenge has the ambitious goal of tracking precise building addresses and urban change from satellite imagery. As detailed in our announcement blog, the goal of SpaceNet 7 is relevant to numerous human development and disaster response applications. Furthermore, the unique SpaceNet 7 dataset poses a 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. …


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Within the latest release of Solaris, available now, is a new resource for geospatial deep learning: the Solaris Multimodal Preprocessing Library. This library eases the work of imagery preprocessing, i.e., undertaking data cleaning and image analysis to get the data to a final form for deep learning. It does so by providing building blocks for constructing powerful image-processing workflows.

Sometimes building a dataset is as simple as tiling ready-to-go images and their accompanying vector labels, a task for which Solaris’s subpackage is ably suited. But sometimes the imagery itself must also be modified in more substantial ways. This is especially true for multimodal datasets, where tasks like applying masks from one modality to another are the norm. But it’s also true anytime one wants to process imagery with techniques like pansharpening, noise reduction, etc. …

About

Daniel Hogan

Daniel Hogan, PhD, is a data scientist at CosmiQ Works, an IQT Lab.

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