New updates and more access to Google Earth AI

https://blog.google/technology/research/new-updates-and-more-access-to-google-earth-ai/

by diogenico • 9 months ago

150 43 9 months ago

blog.google

diogenico• 9 months ago

It shifts from map layers to answer “what/where/why now?” rather than just “show me X.”

And the Gemini-in-Google Earth bit could lower the barrier for non-GIS folks.

chrisshroba• 9 months ago

Bellwether, a moonshot at Alphabet's X, is using Earth AI to provide hurricane predictions insights for global insurance broker McGill and Partners. This enables McGill's clients to pay claims faster so homeowners can start rebuilding sooner.

Hm, I'm quite skeptical about this claim.

tencentshill• 9 months ago

Could be a nice expensive contractor option for replacing the NOAA's public data that we lost. But it probably wont be picked up because it has to study the climate, which is a bad word now.

CobrastanJorji• 9 months ago

You can totally create a private version of NOAA so long as you keep the messaging about insurance intelligence and never, ever speculate out loud about the causes of hurricanes. And if that's not enough, just do what Meta did and hire some shmuck like Robby Starbuck to signal that you're on the right team.

apples_oranges• 9 months ago

Haha yeah. Perhaps a marketing gimmick with an asterisk..

moffkalast• 9 months ago

McGill and Partners

Hi, I'm Saul Goodman. Did you know that you have hurricanes? The constitution says you do! And so do AI.

Legend24402• 9 months ago

Seems plausible to me. It would allow them to start contracting CAT adjusters as soon as a hurricane is expected, before other insurers start bidding for them.

Will this actually pay off for them? Who knows. But insurers are quite into ML for claims/underwriting these days, so I'd believe they're giving it a try.

KRAKRISMOTT• 9 months ago

Wdym by 'bid for them'? Won't the MGA want to get rid of their contracts in an area that's about to be hit by a hurricane ASAP?

notatoad• 9 months ago

quicker approvals probably also means quicker denials, if you want to look at the negative side of it.

giobox• 9 months ago

A quicker denial is still better than a long drawn out one, even if it isn't the outcome you might want.

citizenpaul• 9 months ago

Sorry but our AI said your home destroyed in the hurricane was not in fact destroyed by a hurricane. Claim denied. We accept no further inquiries on the matter.

100% of claims paid out instantly, so its kinda true.

I suspect you don't have an MBA /s

Jordan-1175• 9 months ago

I have some old screenshots of interesting locations from Google Earth circa 2006-2012 that I've never been able to track down. I wonder if something like this would be capable of geolocating them somehow -- like reverse image search for landscapes.

tom1337• 9 months ago

Out of interest: have you already tried using GPT 5 (reasoning / thinking) for that? I've had quite some success in the past using them to track down such places.

Jordan-1175• 9 months ago

Yeah, that and Gemini 2.5. They actually were able to help identify a handful based on context clues, or at least narrow it down enough that I could find it myself. But there were three I couldn't crack -- even a forum dedicated to solving GE puzzles came up empty:

https://googleearthcommunity.proboards.com/thread/10731/ulti...

nomel• 9 months ago

There's a whole community (with world tournaments [1]) around finding places from pictures: geoguessers. The top people are absolutely incredibly [2]. There are also AI trained for this purpose. Although, the perspective they use is usually from street level.

[1] https://www.youtube.com/watch?v=u3sVtwexp0o

[2] https://www.youtube.com/@georainbolt

Jordan-1175• 9 months ago

A few people recommended Geoguessr (and people like Rainbolt are definitely amazing), but yeah I reckon they're hyperspecialized on reading clues in actual street view imagery, not natural satellite footage like this.

lacoolj• 9 months ago

Once Zillow and Redfin start doing this, that will be game-changing

ecommerceguy• 9 months ago

In 2001 we used Erdas Imagine to do this type of work. It required humans to train the software using heads-up digitizing. Dare I say machine learning on Pentium workstations?

edit, looks like they have ai too now. could be neat to play with after how long has it been. jeesh.

polyomino• 9 months ago

I have found that using LLMs to generate queries for Overpass (Open street map query language) works really well. Great alternative if you don't care to deal with corporate nonsense.

macNchz• 9 months ago

I've been able to do really cool stuff that I would never have otherwise bothered with by having an LLM generate Overpass queries + walk me through complex setup steps with QGIS.

groby_b• 9 months ago

The flipside is that QGIS and overpass are so complicated that only with LLM assistance they're truly usable.

(Applies more to QGIS than to overpass, though both could stand to improve a lot in terms of usability)

Mashimo• 9 months ago

Mhh, don't we already have conventional ways of telling where a flodding might happen?

kittikitti• 9 months ago

Guess which corporation just announced they're profiting off of the government shutdown of vital environmental and climate agencies? I wonder why they failed to mention any of that in this press release.

Reubend• 9 months ago

Instead of all this stuff, I'd like to see Google use their ML chops to "solve" weather forecasting and deliver ultra accurate predictions a few days ahead.

elpakal• 9 months ago

But then who’s gonna spend all day Googling stuff when the weather changes their plans again?

NocturnalWaffle• 9 months ago

They are working on this and have had really good results: https://deepmind.google/science/weathernext/ https://deepmind.google/discover/blog/gencast-predicts-weath...

jasongill2• 9 months ago

Just tested and while it seems interesting, there doesn't seem to (yet) be any intelligence about the imagery itself from what I can tell. For example, it can give me insights about vegetation data overlayed on a map (or try to), but it can't "find the most fertile grassland in this radius".

When there is a way to actually "search" satellite images with an LLM, it will be a game changer for businesses (and likely not to the ultimate benefit of consumers, unfortunately)

Demiurge2• 9 months ago

How would you even define “most fertile grassland”? What does “fertile” mean - soil nutrients, water availability, or productivity for a specific crop? And what counts as “grassland”? Are you talking about a 1 acre parcel, something for sale, or land next to a road?

There’s already data for all of this: SSURGO soil maps, vegetation indices, climatology datasets, and more — that could help you find the “most something” in a given radius. But there are too many variables for a single AI to guess your intent. That’s not how people who actually farm, conserve, or monitor land tend to search; they start from a goal and combine the relevant data layers accordingly.

In fact, crop-specific fertility maps have existed for decades, based on soil and climate averages, and they’re still good enough for most practical uses today.

jasongill2• 9 months ago

It was just an example, but you are correct. A more "imagery required" example would be "Find all the houses with roofs that have been damaged in the last 6 months" or something like that which could be used by salespeople or insurers

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