A small building in Mexico tells the violent history of treachery and conquest at the great Aztec city of Cholulu – but for hundreds of years, a secret lay beneath its floors.
Hiding under the grass, trees and soil sits the Great Pyramid of Cholula, deemed the largest monument ever built on Earth, with a base four times the size of the Great Pyramid of Giza.
In 1519, Hernan Cortez and his men marched into the city and massacred 10 percent of the population, building a tiny church on top of a massive hill as a symbol of their conquest.
And, it wasn’t until 1910 that the pyramid underneath was finally discovered.
‘Each minute something is happening in the world,’ said said Tony Frazier, Senior Vice President at DigitalGlobe.
‘While commercial constellations are poised to collect imagery at global scale, we must advance our ability to analyze data to realize its full potential.’
‘SpaceNet is key to unlocking a huge explosion of new AI-driven applications that ultimately will help us better respond to natural disasters, counter global security threats, improve population health outcomes, and much more.
SpaceNet will launch with an initial contribution of DigitalGlobe multi-spectral satellite imagery and 200,000 curated building footprints across the city of Rio de Janeiro, Brazil.
It is a collaboration between DigitalGlobe, CosmiQ Works, and NVIDIA, and the imagery is now freely available as a public data set on Amazon Web Services, Inc. (AWS).
‘Innovation of AI algorithms is fueled by large, high-quality, labeled datasets like SpaceNet and flexible, open-source machine learning tools,’ said Dr. Jon Barker, Solutions Architect at NVIDIA, best known for its computer graphics chips.
‘Researchers will be able to create high-impact geospatial applications by applying our DIGITS deep learning tool to the SpaceNet data corpus.’
GPU-accelerated deep learning has led to huge breakthroughs in the field of computer vision.
Most of this innovation has occurred through research enabled by ImageNet, a database of 14 million photographs labeled in over 20,000 categories.
SpaceNet aims to facilitate similar advances in automating the detection and extraction of features in satellite imagery, fueled by the massive amount of information about our changing planet that DigitalGlobe collects every day, and that of emerging commercial satellite imagery providers.
Until now, high-resolution satellite imagery has not been readily accessible for data scientists and developers to build meaningful computer vision algorithms.
SpaceNet will for the first time open access to a large corpus of curated, high-resolution satellite imagery to incubate algorithm development.
Read more: http://www.dailymail.co.uk/