The chapter explains how NASA's Black Marble team increased effective resolution from the standard 500-meter VIIRS night-lights product to 30-meter Black Marble HD maps. Because the satellite itself cannot resolve finer detail, the team fused VIIRS night data with Landsat daytime imagery, assuming that areas with more impervious surface, meaning more cement and buildings, likely have more lights, and attributing a region's light values across those pixels. The speaker stresses this fused product is for situational awareness only, not a physical measurement, since the cement-to-light assumption does not hold uniformly across countries, and that real measurement-based high-resolution night imagery now comes from NASA's commercial satellite data acquisition program.
- 01
The team turned to this fusion approach after communities asked for house-level detail that the 500-meter product could not provide.
- 02
NASA's commercial satellite data program lets researchers buy night-time imagery from a company's satellite system through an award process, used for example during a Mississippi power outage disaster response.
- 03
The speaker distinguishes the fused HD maps, used for museum displays and visualization, from physical measurements still required for rigorous analysis.
794 words · auto-generated from the episode video
30:21>> that's that's pretty cool I I did want to um one of the one of the innovations I think at least from what I read in this paper um that was kind of Like it sounded miraculous to me when I first read it. It's this figure where it shows the resolution of the of the black marble standard product which is like 500 meters. So half a kilometer by half a kilometer which okay fine that's believable to me right that it's like nighttime. You don't get a lot of photons. So >> how did how did we get here? >> Yeah. So but then it's it's completely unclear to me what data magic happened to get you to 30 m resolution. >> I can explain.
31:01>> Yeah. So that's that's why um >> this came out of that. Yeah. >> This came out of a >> Yeah. >> When we were talking to FEMA >> Mhm. >> and local communities, they're saying, "We love this. You're telling us where the lights have gone down, but your data set is so coarse. I need to get down to the house level." And I'm like, I can't help you, man. >> I can't help you. I only have, right? So, but then one of our team members say but but we have Lancet and Lancet another Noah USGS NASA mission can look at buildings at the Hector scale right you could see street
31:41lights and streets you don't know where this where those streets have street lights or not why didn't we merge those two data sets LA archive which is the the like the crown jewel of like our science is >> yeah it's it's an amazing going in >> and we're like well we know we can count on Lancet being good. >> Yeah. Because there's other guys like you and Lancad team who >> there are other big girls at that we know like oh they can get down the quality of the down to >> yeah their whole thing is quality of data. So >> so we know that if we if we can get imperous area from NASA meaning anywhere that there's cement and you make this assumption
32:23the more cement the more legs. Okay, I think it's a fair assumption. >> Fair assumption. >> Then now you can create a fused image between in that pixel anywhere where there's cement the attribute the lights of that value over that area over that region. This came out of a need because we don't have a black marble satellite at 10 m resolution. We do now thanks to NASA's uh commercial satellite data acquisition program. So we I can now this is the awesome thing about industry being part of NASA. I can now ask a company to take a picture at night and buy the data for
33:04research and the they go through a an award and then they make their satellite system available to researchers like so we have done as of last year there was a major power outage in Mississippi and our disaster response system was I want high resolution images and we detected power allergist with real real nighttime nights but between now and then we had we were basically creating a you know a a a simulated map. >> Yeah. >> And that that map does it's not SI tracer. >> Mhm. What does that mean? >> It means you can visualize it but you don't know what the values mean. It's
33:46just for situational awareness only. Nothing replaces a physical measurement. So every time that's why these maps you can see them in museum displays you can see them but analysis you still use the fire in a nearby need something of high integrity and you cannot use that assumption in purpose area lights and assume that that's going to apply everywhere >> right >> that's not the case in the across the world different countries use lights differently it that assumption only holds visually and I you know from And as a point of view is like then why are you doing it? I'm doing it because our users leave
34:27>> because a lot of our users want a 60% solution. >> Mhm. And it's good enough. >> The decisions they're making are like in and out. >> They can't wait 3 months for a science paper, you know, three years to make a decision. So we we were tailoring a solution and that required a lot of technical skill not just in building the algorithms >> but figuring out the data pipe mics to do this because it's a lot of it's there's a lot of high volume in there.
Remote SensingEarth ObservationEnergy AccessOpen Data