The US government wants to start charging for the best free satellite data on earth

The US government wants to start charging for the best free satellite data on earth

  • May 28, 2018
Table of Contents

The US government wants to start charging for the best free satellite data on earth

The US government may begin charging users for access to five decades of satellite images of Earth, just as academic and corporate researchers are gaining the tools they need to harness them. Nature reports that the Department of Interior has asked an advisory board to consider the consequences of charging for the data generated by the Landsat program, which is the largest continuously collected set of Earth images taken in space and has been freely available to the public since 2008. Since 1972, Landsat has used seven different satellites to gather images of the Earth, with an eighth currently slated for a December 2020 launch.

The data are widely used by government agencies, and since it became free, by an increasing number of academics, private companies and journalists. “As of March 31, 2018, more than 75 million Landsat scenes have been downloaded from the USGS-managed archive!” the agency noted on the 10th anniversary of the program.

Now, the government says the cost of sharing the data has grown as more people access it. Advocates for open data say the public benefit produced through research and business activity far outweigh those costs. A 2013 survey cited by Nature found that the dataset generated $2 billion in economic activity, compared to an $80 million budget for the program.

The Landsat data is considered especially valuable by companies that use machine learning to analyze Earth imagery because of the breadth of the database. While Sentinel, a European Space Agency program, provides similar data to Landsat, it only offers free imagery dating back to 2017. The growing corps of venture-backed Earth-imaging companies, like Planet and IceEye, can likewise only offer data captured in the last several years.

Source: qz.com

Share :
comments powered by Disqus

Related Posts

3D Face Reconstruction with Position Map Regression Networks

3D Face Reconstruction with Position Map Regression Networks

Position Map Regression Networks (PRN) is a method to jointly regress dense alignment and 3D face shape in an end-to-end manner. In this article, I’ll provide a short explanation and discuss its applications in computer vision. In the last few decades, a lot of important research groups in computer vision have made amazing advances in 3D face reconstruction and face alignment.

Read More
Tensor Compilers: Comparing PlaidML, Tensor Comprehensions, and TVM

Tensor Compilers: Comparing PlaidML, Tensor Comprehensions, and TVM

One of the most complex and performance critical parts of any machine learning framework is its support for device specific acceleration. Indeed, without efficient GPU acceleration, much of modern ML research and deployment would not be possible. This acceleration support is also a critical bottleneck, both in terms of adding support for a wider range of hardware targets (including mobile) as well as for writing new research kernels.

Read More