[HN Gopher] Satlas: Open Geospatial Data Generated by AI
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Satlas: Open Geospatial Data Generated by AI
Author : jonbaer
Score : 102 points
Date : 2023-09-05 03:35 UTC (1 days ago)
(HTM) web link (satlas.allen.ai)
(TXT) w3m dump (satlas.allen.ai)
| pcmill wrote:
| Just a heads up but the super resolution example absolutely spams
| the history API in your browser if you move the map slightly. You
| could add a bit of delay before trying to save the new location
| in the URL.
| favyen wrote:
| Thanks for pointing this out! We will look into fixing it.
| tuukkah wrote:
| They need to learn when to use history.pushState vs
| history.replaceState
| [deleted]
| rob74 wrote:
| Or, how about not saving the location in the URL at all when
| moving the map? I guess there must be a reason why none of the
| mainstream map webapps does something like this...
| mistrial9 wrote:
| it tracks everything you do while interacting with it --
| FEATURE?
| tuukkah wrote:
| Others do this too, just using replaceState.
| jimktrains2 wrote:
| It allows you to copy and paste the url to link someone to
| what you're seeing. It's a good thing if sone right.
|
| Also, google maps changes the url as you pan and zoom.
| uphone wrote:
| I'm curious about the application and implication of using
| generative model for comparative analysis. Wherein, if the
| results are incorrect or a have a slight error in a map, can lead
| to incorrect conclusion and impact on policy. This observation is
| not centered on the Satlas projects because medical image
| analysis is also out there (but may be the FDA can drive some
| regulation). Broader question, how would we have to think about
| generative modeling for applications that are more then
| entertainment and cannot be corrected/ verified by a person (like
| the user in case of ChatGPT)
| favyen wrote:
| I fully agree that errors in extracted data can lead to making
| incorrect decisions/policies. Even for applications where
| accuracy is paramount, though, I think error-prone models still
| have their uses:
|
| - For applications that only need summary statistics over
| certain geographies, analyzing small samples of data can yield
| correction factors and error estimates.
|
| - The data could also be combined with manual verification to
| improve existing higher-precision but lower-recall datasets
| (e.g. OpenStreetMap where features are more likely to be
| correct but also have less overall coverage).
| joelhaasnoot wrote:
| So if I'm understanding correctly this is using AI to fancily
| upscale Senitel 2 data, essentially guessing what it's seeing,
| and then suggesting the output of that should be used for making
| new products/decisions/models. Sounds a bit like CSI Zoom Enhance
| stuff...
| arthur2e5 wrote:
| The super-res is surprisingly usable for making sense of land
| use changes. With OpenStreetMap editing, one common challenge
| is that out of the usable (license-wise) imagery, the high-defs
| ones are old and the new ones from Sentinel are low-def. A lot
| of switching, squinting, and gusssing is required to understand
| of what's going on, even when most of the work is as basic as
| trying to spot this old road in the blurry new image. This
| super-res seems to do that well enough. It doesn't have enough
| information to guess the exact shape of buildings and that's
| okay.
|
| They also do some object recognition, which is useful if you're
| an electric infrastructure nut. It spotted some solar fields in
| Shanghai which I've never heard of before -- a look at the same
| coordinates (30.753, 121.392) on Google sure shows the expected
| blue.
| tempodox wrote:
| If machines can hallucinate in text form, they surely can
| hallucinate in maps.
| supdudesupdude wrote:
| [flagged]
| favyen wrote:
| The models we use to extract the geospatial data (like solar
| farm and offshore platform positions) from Sentinel-2 imagery
| are currently separate from the Sentinel-2 upscaling model,
| which is a more exploratory project.
|
| We report the accuracy of the data at [1]; the Satlas project
| is quite new and we're aiming to improve accuracy as well as
| add more categories over time.
|
| We expect the geospatial data will be useful for certain
| applications, but I agree that the upscaled super-resolution
| output has more limited uses, especially in its current state
| outside the US since it is trained using NAIP imagery that is
| only available in the continental US. We're exploring methods
| to quantify and improve the accuracy of the upscaled imagery.
|
| Note that the model weights, training data, and generated
| geospatial data can all be downloaded at [2].
|
| [1]
| https://github.com/allenai/satlas/blob/main/DataValidationRe...
|
| [2] https://github.com/allenai/satlas
| arthur2e5 wrote:
| Does satlas currently use any channels other than Sentinel's
| visible RGB? I imagine that those near IR bands can be very
| useful for plant-related tasks and (with a long stretch)
| potentially help with object discrimination by adding an
| extra band.
| favyen wrote:
| The marine infrastructure (offshore platform and offshore
| wind turbine) and super-resolution models only use RGB
| bands (B04, B03, B02), while the solar farm, onshore wind
| turbine, and tree cover models use 9 Sentinel-2 bands (add
| B05, B06, B07, B08, B11, and B12). With enough high-quality
| labels, the extra bands do provide slightly improved
| performance (1-2% gain in our accuracy metric, e.g. from
| 89% to 91%), but we don't have a detailed comparison or
| analysis at this time.
|
| Also, all of the models input three or four images of the
| same location (captured within a few months), with max
| temporal pooling used at intermediate layers to enable
| model to synthesize information across the images. This
| helps a lot, definitely when one image has a section
| obscured by clouds (so model can use the other images
| instead), and maybe also when different images provide
| different information (e.g. shadows going in different
| directions due to slightly different times of day).
| giancarlostoro wrote:
| Do you by chance have comparisons of what the terrain
| actually looks like without the AI upscale? would be
| interesting to see how much it gets right.
| favyen wrote:
| We plan to eventually add some real paid high-res imagery
| to the map just as a comparison, but for now you would need
| to look at the map at https://satlas.allen.ai/map (select
| Super Resolution) and compare it to a source of aerial
| imagery like Google Maps or Bing Maps at the same spot.
| giancarlostoro wrote:
| Sounds good, it's been a little while since I've touched
| anything GIS related, but it was kind of fun while at the
| same time stressful for me as a junior developer at the
| time. I'm definitely curious how insanely accurate AI
| upscaling will become with stuff like this, at least in
| terms of getting a good amount of the terrain correct.
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