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 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.
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.
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.
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 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?
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
Am I reading this right that most of their currently operating satellites are servicing Flock - the US surveillance company? Or is it just a naming accident?
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.
It completely is. Planet names all their constellations after birds (Dove, Pelican, Tanager, Owl - SkySat was an acquisition, so not their naming convention) so they describe their constellations as Flocks. Nothing to do with the other company.
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
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.
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.
24 comments
[ 0.24 ms ] story [ 17.4 ms ] threadI 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.
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.
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.
Disclosure: this is run by my team.
https://www.iceye.com/blog/deforestation-solution-9-essentia...
https://www.iceye.com/newsroom/press-releases/iceye-and-the-...
https://janegoodall.org/
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
Such a shame.
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.