I wish Planet had better pricing for nonprofits. I co-run a conservation nonprofit focused on deforestation in Latin America, and our affordable choices are between 2 year old Google Earth imagery, 10m resolution Nimbo imagery (ok, but not sufficiently detailed to be evidentiary), and Sentinel 1 SAR data, which is useful but very noisy. Planet quoted us something like $30k per year for a strip of coastline that represents something like 5% of the territory we're monitoring, so it's just not a viable option under their current pricing model.
Have you discussed requirements with their account managers? They can be a little more creative than their list prices when they realise you don't have the budget for the data you ideally need but are a potential source of recurring revenue. And the EO market is getting more competitive...
If you are looking for evidentiary purposes, does the Sentinel-1 + Nimbo data give you enough to filter the forest-wide data down to specific areas of interest you might want more spatial/temporal resolution? (or as a former colleague once put it when looking for evidence of more localised and heterogenous environmental damage "to find the needle in the haystack, you first remove the haystack"). Particularly if identifying specific areas of interest is a potential route for you to escalate to another party with an active interest and bigger budget...
Your other route to the commercial data you ideally want would be via partnership with EO consultancies with publicly funded R&D projects to showcase their capabilities (easier if you're European, but not a prerequisite). ESA, for example, devotes a lot of funding for private consortia to demonstrate that Copernicus data (coupled with other data where necessary) yields useful results...
It's a while since I worked in this field (in a non-technical role) but happy to share what I learned in more detail - email in profile.
What if you used the Sentinel data (in assuming your using the same low res stuff I view in CalTopo) and put that through an AI up scaling type algo and also give it the higher resolution but older imagery ? With a decent amount of dinklsr data it might be able to fairly accurately give you an approximation of what the current high res stuff is.
I suppose you could even train it (maybe) on multiple years / time periods of historical high and low res data before using the latest Sentinel stuff ?
Edit: I'm sure you already know this but I bet for the above the false color IR data could also be especially useful
Have you come across ICEYE? It's a high res commercial SAR constellation, and they have a deforestation monitoring service. They partnered with the Jane Goodall Institute - perhaps you could approach them?
We're volunteer driven and currently rely on personal donations, so we're scrappy. We could probably justify somewhere in the region of $10k/yr. The jets/balloon angle is interesting, thanks - hadn't occurred to me, and I'll search through what's out there.
See if you can negotiate reselling the imagery they sell you. It might be hard to get agreement with this as the vendor will have you both as a customer and compeditor but if you pull it off, you could recoup your costs and then some. For a few weeks, no one else in the private sector will have the freshness and/or quality of imagery you do.
Maybe I'm missing something obvious, but what about drones? Probably would have to get permits most likely, but still might be cheaper than alternatives (although balloons are hard to beat when it comes to costs, not always reusable though).
We do use a drone when we know of a site where deforestation has taken place. The gap we're trying to fill is early detection of forest intervention, so that we can alert appropriate agencies (who are generally under-resourced) and get an inspection to take place while activity is ongoing.
How many years away are we from a cheap drone that will takeoff, land, solar recharge, repeat until it returns to you a few weeks later with a few tb of SD cards of high res imagery?
Pretty close. Sesame Energy[0] make these shipping container boxes with solar panels + batteries so you can just drop them where you need no-supply chain energy. The boxes also synthesize hydrogen which can be used to power drones. The talk I heard from the CEO said the automated swapping of hydrogen canisters was not yet there, but they were working on it. Nothing in robotics is easy, but this feels like a very attainable goal that the military would pay to get working.
A lot of the barriers are regulatory in the Western world. In rainforests you might not have to worry about that, but there aren't many shade free spots to land
Really busy, from satellite constellation tasking to planet and co basemaps to the sentinel family to aerial imagery to helicopters, drones and even blimps.
They all have their unique combination of cost, resolution, area coverage, revisit rate and spectral modes. Quite difficult sometimes to find a good fit for the underlying problem. What‘s clear is that resolution and revisit rate correlate with cost. Shit‘s expensive.
I have no business other than as a customer but we’ve had decent success with getting cheap archive imagery at fairly high res out of SkyWatch. It’s worth a look. If you need a very large area the price will grow quite a lot though.
The geometry and bbox in these records aren't the same shape, which matters if
you're indexing them.
I ran the shoelace formula over the Nepal Pelican footprint from the post. The
bounding box around it comes out about 40% larger in area, so somewhere over a
quarter of that box is ground with no pixels behind it. That scene is only 3.8
degrees off-nadir as well. The summarize table further down has some going up
to 28.9, and those would be further off.
Indexing the bbox is the obvious move because it's four numbers and any
database will put an R-tree on it. But coverage queries then hand you scenes
for an AOI and the file turns up empty over that part of it. Took me longer
than it should have to work out why there was a hole in my mosaic.
Separately, eo:cloud_cover is one number for the whole scene. Sorting on it to
find your clearest option over a small area doesn't hold up. 85% cloud can
still be clear over the part you want. The UDM2s give you it per pixel, but
that's a second raster open per candidate before you can rank anything.
Flock is Planet Labs name for the mega-constellation of imaging satellites made up of 3U cubesats known as Doves- hence the name for the full constellation. It has nothing to do with the automated license plate reader company.
The latter I would think. I can see how you can confuse the two. The reason I think otherwise is because you don't name the satellite after one client, especially if you're in the business of selling images/data, not satellites. And a flock of satellites make sense for a name.
Mark Litwintschik's blog is one of the best engineering blogs on the internet. Always practical, with detailed write-ups that make it easy to reproduce workflows. Thanks, Mark.
Where people want to look on this earth is pretty relevent information for the intelligence community, same for "OSINT" databases where you can lookup usernames across social networks
I am using DuckDB + Parquet + HTTP range requests for a current project and it's a great combination for data exploration tools that run in the browser.
I wish Planet had better pricing for nonprofits. I co-run a conservation nonprofit focused on deforestation in Latin America, and our affordable choices are between 2 year old Google Earth imagery, 10m resolution Nimbo imagery (ok, but not sufficiently detailed to be evidentiary), and Sentinel 1 SAR data, which is useful but very noisy. Planet quoted us something like $30k per year for a strip of coastline that represents something like 5% of the territory we're monitoring, so it's just not a viable option under their current pricing model.
Have you discussed requirements with their account managers? They can be a little more creative than their list prices when they realise you don't have the budget for the data you ideally need but are a potential source of recurring revenue. And the EO market is getting more competitive...
If you are looking for evidentiary purposes, does the Sentinel-1 + Nimbo data give you enough to filter the forest-wide data down to specific areas of interest you might want more spatial/temporal resolution? (or as a former colleague once put it when looking for evidence of more localised and heterogenous environmental damage "to find the needle in the haystack, you first remove the haystack"). Particularly if identifying specific areas of interest is a potential route for you to escalate to another party with an active interest and bigger budget...
Your other route to the commercial data you ideally want would be via partnership with EO consultancies with publicly funded R&D projects to showcase their capabilities (easier if you're European, but not a prerequisite). ESA, for example, devotes a lot of funding for private consortia to demonstrate that Copernicus data (coupled with other data where necessary) yields useful results...
It's a while since I worked in this field (in a non-technical role) but happy to share what I learned in more detail - email in profile.
What if you used the Sentinel data (in assuming your using the same low res stuff I view in CalTopo) and put that through an AI up scaling type algo and also give it the higher resolution but older imagery ? With a decent amount of dinklsr data it might be able to fairly accurately give you an approximation of what the current high res stuff is.
I suppose you could even train it (maybe) on multiple years / time periods of historical high and low res data before using the latest Sentinel stuff ?
Edit: I'm sure you already know this but I bet for the above the false color IR data could also be especially useful
Have you come across ICEYE? It's a high res commercial SAR constellation, and they have a deforestation monitoring service. They partnered with the Jane Goodall Institute - perhaps you could approach them?
https://www.iceye.com/blog/deforestation-solution-9-essentia...
https://www.iceye.com/newsroom/press-releases/iceye-and-the-...
https://janegoodall.org/
What sort of budget does your org support?
I had to look into ~19 imagery firms for some telco work in Canada last year.
There are private jets that can capture 10cm imagery and can pick the ideal weather window to fly in.
There are also firms that fly balloons 20 - 80 KM off the ground that can capture 4cm imagery.
The space is pretty busy.
We're volunteer driven and currently rely on personal donations, so we're scrappy. We could probably justify somewhere in the region of $10k/yr. The jets/balloon angle is interesting, thanks - hadn't occurred to me, and I'll search through what's out there.
See if you can negotiate reselling the imagery they sell you. It might be hard to get agreement with this as the vendor will have you both as a customer and compeditor but if you pull it off, you could recoup your costs and then some. For a few weeks, no one else in the private sector will have the freshness and/or quality of imagery you do.
> The jets/balloon angle is interesting
Maybe I'm missing something obvious, but what about drones? Probably would have to get permits most likely, but still might be cheaper than alternatives (although balloons are hard to beat when it comes to costs, not always reusable though).
We do use a drone when we know of a site where deforestation has taken place. The gap we're trying to fill is early detection of forest intervention, so that we can alert appropriate agencies (who are generally under-resourced) and get an inspection to take place while activity is ongoing.
Drones (and helicopters) do have their market as well. A lot of engineering firms I saw would go with one of these two, depending on the situation.
How many years away are we from a cheap drone that will takeoff, land, solar recharge, repeat until it returns to you a few weeks later with a few tb of SD cards of high res imagery?
Pretty close. Sesame Energy[0] make these shipping container boxes with solar panels + batteries so you can just drop them where you need no-supply chain energy. The boxes also synthesize hydrogen which can be used to power drones. The talk I heard from the CEO said the automated swapping of hydrogen canisters was not yet there, but they were working on it. Nothing in robotics is easy, but this feels like a very attainable goal that the military would pay to get working.
[0] https://www.sesame.solar/audience/us-military-allies
A lot of the barriers are regulatory in the Western world. In rainforests you might not have to worry about that, but there aren't many shade free spots to land
Autel dragonfish and hanger and you're halfway there. Will need a fat checkbook (the bird is ~$200k) and be outside of the US.
Really busy, from satellite constellation tasking to planet and co basemaps to the sentinel family to aerial imagery to helicopters, drones and even blimps.
They all have their unique combination of cost, resolution, area coverage, revisit rate and spectral modes. Quite difficult sometimes to find a good fit for the underlying problem. What‘s clear is that resolution and revisit rate correlate with cost. Shit‘s expensive.
I have no business other than as a customer but we’ve had decent success with getting cheap archive imagery at fairly high res out of SkyWatch. It’s worth a look. If you need a very large area the price will grow quite a lot though.
Have you considered using https://planetarycomputer.microsoft.com/?
Disclosure: this is run by my team.
Refreshingly doesn't feel like AI. If someone asked me "what does software engineering look like", I would point to this.
The geometry and bbox in these records aren't the same shape, which matters if you're indexing them.
I ran the shoelace formula over the Nepal Pelican footprint from the post. The bounding box around it comes out about 40% larger in area, so somewhere over a quarter of that box is ground with no pixels behind it. That scene is only 3.8 degrees off-nadir as well. The summarize table further down has some going up to 28.9, and those would be further off.
Indexing the bbox is the obvious move because it's four numbers and any database will put an R-tree on it. But coverage queries then hand you scenes for an AOI and the file turns up empty over that part of it. Took me longer than it should have to work out why there was a hole in my mosaic.
Separately, eo:cloud_cover is one number for the whole scene. Sorting on it to find your clearest option over a small area doesn't hold up. 85% cloud can still be clear over the part you want. The UDM2s give you it per pixel, but that's a second raster open per candidate before you can rank anything.
Also checkout https://nlnet.nl/project/Mapterhorn-imagery for planet high-def satellite PMtiles. Not yet released.
Very cool but looks like it's only aggregating European data (on top of a low res planet basemap). Also is pmtiles useful for satellite imagery?
Are most of their currently operating satellites servicing Flock - the US surveillance company? Or is it just a naming accident?
Flock is Planet Labs name for the mega-constellation of imaging satellites made up of 3U cubesats known as Doves- hence the name for the full constellation. It has nothing to do with the automated license plate reader company.
Their satellites have nothing to do with the street-level surveillance camera firm.
The latter I would think. I can see how you can confuse the two. The reason I think otherwise is because you don't name the satellite after one client, especially if you're in the business of selling images/data, not satellites. And a flock of satellites make sense for a name.
I don't think it's an accident. Flock != Flock Safety
Mark Litwintschik's blog is one of the best engineering blogs on the internet. Always practical, with detailed write-ups that make it easy to reproduce workflows. Thanks, Mark.
Where people want to look on this earth is pretty relevent information for the intelligence community, same for "OSINT" databases where you can lookup usernames across social networks
This is cool, learned of two new file extensions: .tle and .parquet
I am using DuckDB + Parquet + HTTP range requests for a current project and it's a great combination for data exploration tools that run in the browser.