" At around $500 per square kilometer (apologies for the mixed units, but we’re going with industry standards), a single US collection would be $5 billion." That's about right, in that the US is about 10M km^2. But then, "current retail pricing of around $50/sq. km, it would only cost you $150 million to cover the US, or $7.7 billion to cover the whole Earth. ". But if it'S 10x cheaper, it should cost $500M to cover the US. You seem to have the right multiple of US/earth surface area (earth:US :: 51:1), so the cost for the earth should be ~$25B.
Fascinating article. Only thought is why does the Array multi static SAR ring need to be dedicated ? Ie if know position of satellites in LEO then why not add capability,to say, thousands of starlink sats. I am guessing the ring shape and close Comms between sats in ring critical.
I started a company to address parking in big cities more than 15 years ago, now. The problems with parking in big cities are myriad - from knowing utilization, to inventory, to keeping up with regulations on towards the sexier bits of avoiding parking tickets and finding parking.
Ultimately the effort failed because GPS is simply nowhere remotely accurate enough: not accurate enough to reliably determine the side of the street a car is parked on much less the actual location. Without this information, the use case was simply too difficult despite the potential value.
3D point cloud location calculations were the way I identified to resolve this issue but ultimately there was no way I was going to be able to compete with the self driving, drone, etc companies for engineering hiring.
Note what was not a problem: a 3D map of the cities in question.
So good luck to this company - maybe the 3D point cloud engineering talent war is less intense now than back then.
- the SAR concept is basically the same used in seismic data acquisition. The processing of SAR data will have many similarities to seismic data processing.
- "formation flying of the satellites" - appears to me that most important is that, at any time imagery is acquired, the relative position of the sat's to each other is known to high accuracy
- and finally: why don't you test the concept - the processing in particular - by taking high resolution band imagery that is currently acquired by 'big satellites', take several adjacent bands of data,, build your spatial image, chuck data points to reduce resolution and the do the spatial software processing to see if you can recreate the resolution of the original data. It should work. But you need to prove it.
This is a great essay on many aspects of the Array Labs concept. Thanks for the shoutout to NASA JPL for building and operating Seasat, the first civilian SAR satellite. You might have also mentioned that JPL and the DLR built and processed the data from the first bistatic SAR topographic map of most of the Earth, with the Shuttle Radar Topography Mission, which was largely funded by the DoD agency now called NGA (but called NIMA in 2000 when SRTM flew). SRTM used a 60 meter boom extending from the Shuttle payload bay to get the separation of the two antennas. There were two sets of antennas, one at C-band built by JPL and another at X-band built by DLR. The near-global 3D topographic map was made by the NASA JPL system for NIMA and later released as free and open data.
Awesome post Packy! I have really been thinking more deeply about "ISR as a Service" vs. the traditional acquisition process and what that means for EO startups. Like you stated, the markets that open up when high resolution becomes a commodity will be game changing.
math error, I think
" At around $500 per square kilometer (apologies for the mixed units, but we’re going with industry standards), a single US collection would be $5 billion." That's about right, in that the US is about 10M km^2. But then, "current retail pricing of around $50/sq. km, it would only cost you $150 million to cover the US, or $7.7 billion to cover the whole Earth. ". But if it'S 10x cheaper, it should cost $500M to cover the US. You seem to have the right multiple of US/earth surface area (earth:US :: 51:1), so the cost for the earth should be ~$25B.
Fascinating article. Only thought is why does the Array multi static SAR ring need to be dedicated ? Ie if know position of satellites in LEO then why not add capability,to say, thousands of starlink sats. I am guessing the ring shape and close Comms between sats in ring critical.
lovely post. Such a great read, and so inspiring!
I started a company to address parking in big cities more than 15 years ago, now. The problems with parking in big cities are myriad - from knowing utilization, to inventory, to keeping up with regulations on towards the sexier bits of avoiding parking tickets and finding parking.
Ultimately the effort failed because GPS is simply nowhere remotely accurate enough: not accurate enough to reliably determine the side of the street a car is parked on much less the actual location. Without this information, the use case was simply too difficult despite the potential value.
3D point cloud location calculations were the way I identified to resolve this issue but ultimately there was no way I was going to be able to compete with the self driving, drone, etc companies for engineering hiring.
Note what was not a problem: a 3D map of the cities in question.
So good luck to this company - maybe the 3D point cloud engineering talent war is less intense now than back then.
Great post! This will disrupt the earth observation industry, very bullish
Great article & couple of remarks:
- the SAR concept is basically the same used in seismic data acquisition. The processing of SAR data will have many similarities to seismic data processing.
- "formation flying of the satellites" - appears to me that most important is that, at any time imagery is acquired, the relative position of the sat's to each other is known to high accuracy
- and finally: why don't you test the concept - the processing in particular - by taking high resolution band imagery that is currently acquired by 'big satellites', take several adjacent bands of data,, build your spatial image, chuck data points to reduce resolution and the do the spatial software processing to see if you can recreate the resolution of the original data. It should work. But you need to prove it.
This is a great essay on many aspects of the Array Labs concept. Thanks for the shoutout to NASA JPL for building and operating Seasat, the first civilian SAR satellite. You might have also mentioned that JPL and the DLR built and processed the data from the first bistatic SAR topographic map of most of the Earth, with the Shuttle Radar Topography Mission, which was largely funded by the DoD agency now called NGA (but called NIMA in 2000 when SRTM flew). SRTM used a 60 meter boom extending from the Shuttle payload bay to get the separation of the two antennas. There were two sets of antennas, one at C-band built by JPL and another at X-band built by DLR. The near-global 3D topographic map was made by the NASA JPL system for NIMA and later released as free and open data.
Awesome post Packy! I have really been thinking more deeply about "ISR as a Service" vs. the traditional acquisition process and what that means for EO startups. Like you stated, the markets that open up when high resolution becomes a commodity will be game changing.