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.Google DeepMind is powering a new agricultural landscape understanding layer in Google Earth for portions of the Asia-Pacific region to bring powerful data to our iconic imagery. Leveraging machine learning and satellite imagery, this data layer is able to visualize the ""atomic units"" of agriculture: individual field boundaries. This...

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231,640 görüntüleme • 8 ay önce •via X (Twitter)

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DaCon profil fotoğrafı
DaCon8 ay önce

@GoogleDeepMind You could put a solar yield sim on top. Simulating average or point in time solar irradiance. Seasonal and daily light distribution mapping is key for crop planing and yield. As this varies through a solar cycle, you can also plan crop and soil rest cycles, or grazing rotation.

Shami profil fotoğrafı
Shami8 ay önce

@GoogleDeepMind I've used it and it's amazing 🤩

Sebastian Buzdugan profil fotoğrafı
Sebastian Buzdugan8 ay önce

@GoogleDeepMind that land cover segmentation must be seriously complex

Techificial.ai profil fotoğrafı
Techificial.ai8 ay önce

@GoogleDeepMind @googleearth & @GoogleDeepMind, this initiative is mind-blowing! Utilising AI to improve agricultural insights will empower farmers and improve resource management. I'm really excited to see the impact it will have in the region.

@Mforja profil fotoğrafı
@Mforja8 ay önce

@GoogleDeepMind Defining agriculture in “atomic units” feels like the right abstraction for applying AI to real land management. This is where geospatial ML starts becoming infrastructure, not just visualization.

Luke profil fotoğrafı
Luke8 ay önce

@GoogleDeepMind Been putting code and AI into the insights of the family farm productions. My mom has rain precipitation, production, prices and other data points for the last 7 years of production.

CosmicNomad profil fotoğrafı
CosmicNomad8 ay önce

@GoogleDeepMind Why just Asia-pacific? Can’t we have this everywhere?

Wessel van Keulen profil fotoğrafı
Wessel van Keulen8 ay önce

@GoogleDeepMind Good progress. What’s next?

Ram profil fotoğrafı
Ram8 ay önce

@GoogleDeepMind Does it also integrated with local weather forecast? Like rainy days?

Yeppi profil fotoğrafı
Yeppi8 ay önce

@GoogleDeepMind google earth is build with flutter, right?

suiscats（ゆらゆら） profil fotoğrafı
suiscats（ゆらゆら）8 ay önce

@GoogleDeepMind @grok これは耕作、維持管理を判別できる？

J Dominique A. profil fotoğrafı
J Dominique A.8 ay önce

@GoogleDeepMind All satellite image process: training site, groundtruething, image classification, etc should be

3DTOPO profil fotoğrafı
3DTOPO8 ay önce

@GoogleDeepMind @yohaniddawela

Amit profil fotoğrafı
Amit8 ay önce

@GoogleDeepMind Modeling agricultural landscapes from satellite imagery is harder than people think. You get radiance, clouds, seasonal artifacts, and messy ground truth. The real progress is in harmonizing temporal sequences into actionable, high-granularity crop maps.

I Have Many Names profil fotoğrafı
I Have Many Names8 ay önce

@GoogleDeepMind 🚀

Vitucho. profil fotoğrafı
Vitucho.8 ay önce

@GoogleDeepMind Brutal !

🔻🍉🇰🇼 🇸🇦 profil fotoğrafı
🔻🍉🇰🇼 🇸🇦8 ay önce

@GoogleDeepMind It has been two years almost and my area image isn’t updated, is this intended?

Shreya profil fotoğrafı
Shreya8 ay önce

@GoogleDeepMind Oh

Tobe Duru profil fotoğrafı
Tobe Duru8 ay önce

@GoogleDeepMind to provide real-time insights on crop health and land use

Rahul Raghav profil fotoğrafı
Rahul Raghav8 ay önce

@GoogleDeepMind curious how this will impact crop yield predictions and resource allocation in the region

#INTERNETofAGENTS profil fotoğrafı
#INTERNETofAGENTS8 ay önce

@GoogleDeepMind Refinement! 👌

Sachin 1990 Sacbin G profil fotoğrafı
Sachin 1990 Sacbin G8 ay önce

@GoogleDeepMind flustered

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