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SpaceNet LLC is a non-profit organization dedicated to accelerating open source, artificial intelligence applied research for geospatial applications, specifically foundational mapping. Since its founding, the dataset has been downloaded millions of times in more than 80 countries. How did all of this get started? In the first of a two-part series, Ryan Lewis (CosmiQ Works), Todd Bacastow (Maxar), Joe Flasher (AWS), and Alexei Bastidas (Intel AI) discuss the origins of SpaceNet and how all the different partners contribute to this initiative.  SpaceNet is a collaboration between CosmiQ Works, Maxar, Intel AI, Amazon Web Services (AWS), and Capella Space.

Dylan George from In-Q-Tel's (IQT) B.Next joins CosmiQ Works' Jake Shermeyer and Ryan Lewis to discuss how a time series of satellite imagery can be used in humanitarian response scenarios. A time series of satellite images can play a major role in these scenarios and assist practitioners in identifying areas that experience a change in population, the loss of electrical power, and/or rapid changes in land-cover. The podcast addresses two main questions: (1) How can practitioners quickly analyze these time series with open source software tools, such as CosmiQ’s CometTS tool suite; and (2) How can those tools be used to support a humanitarian response effort?  Learn how CosmiQ tackled these questions with their CometTS tool suite through the study of satellite imagery of Puerto Rico following Hurricane Maria.

Performing machine learning and analyzing geospatial data are both hard problems requiring a lot of domain expertise. These limitations have historically meant that one needs to be an expert in both to perform even the most basic analyses, making advances in AI for overhead imagery difficult to achieve. Is there anything we can do to reduce this barrier to entry, making it easier to apply machine learning methods to overhead imagery data?   Ryan Lewis and Nick Weir tackle that question as they discuss one of CosmiQ’s new project, Solaris, a new open source Python library for performing and evaluating machine learning analyses of overhead imagery. Solaris provides an easy-to-use, end-to-end analysis pipeline for AI model training, prediction, and performance assessment, along with providing pre-trained winning models from the SpaceNet® Challenges for comparison. 

Determining optimal routing paths in near real-time is at the heart of many humanitarian, civil, military, and commercial challenges. This statement is as true today as it was two years ago when the SpaceNet® partners announced the SpaceNet Challenge 3, focusing on road network detection.  On today’s pod we are joined by Ryan Lewis and Adam Van Etten to learn about SpaceNet’s upcoming fifth challenge that revisits road detection, routing, and now travel time estimation from satellite imagery. This public challenge will test participants’ ability to automatically extract road networks from satellite imagery, along with travel time estimates along all roadways, thereby permitting true optimal routing.  SpaceNet is a collaborative effort between CosmiQ Works, AWS, Maxar, and Intel AI. Learn more at www.spacenet.ai.

Consider all of the onboard analytics necessary for autonomous/semi-autonomous car operations such as lane assist. These offerings present a new attack surface for malicious actors in a myriad of industries ranging from automotive to space systems. Yet, market trends are clearly moving towards more increasing connectivity and expanding edge analytics for most of the devices we use in our daily lives. The question becomes the following: what can organizations do to increase cybersecurity of their embedded systems while these market dynamics unfold? Join us for a discussion with Red Balloon Security’s Founder and CEO, Dr. Ang Cui, and Research Scientist Joseph Pantoga, to learn about security implications (and potential solutions) of deploying artificial intelligence models to connected embedded devices. Mentioned links: https://www.blackhat.com/us-19/ https://www.redballoonsecurity.com/  https://www.escar.info/images/Datastore/2018_escar_USA/AutomotiveSandbox_Nathaniel_Boggs_Escar_USA_2018.pdf 

Peter Wang, CTO and Co-Founder of Anaconda sits down with Ryan Lewis, Adam Van Etten, and Coley Lewis to talk about open source software and its role for both data science applied research as well as product development and deployment.

Below is a link to the research paper, "SpaceNet MOVI: a Multi-View Overhead Imagery Dataset," that Nick mentioned during the episode: https://arxiv.org/abs/1903.12239.   

Adam Van Etten, Jake Shermeyer, and Ryan Lewis discuss CosmiQ's year long project studying the impact of image super resolution on object detection model performance.

Adam Van Etten, CosmiQ's Director of Research, and David Lindenbaum, formerly CosmiQ's Principal Engineer, talk about the SpaceNet 3 Challenge: Road network extract and routing from a single satellite image.

CosmiQ co-founders Ryan Lewis & David Lindenbaum talk about the motivation for startin the lab, trends in geospatial analytics, and upcoming projects.