Vishal Batchu

Research Engineer at Google.

[Método Viral @metodo_viral]

Setembro 2026 foi insano no Google: Gemini 4 Argon com 1M tokens (foco em cibersegurança), IA mapeando 9B mutações do DNA no AlphaGenome Atlas e rastreando metano do espaço com MAPL-EMIT 🌍🚀 https://t.co/xGcf3YMmBv

[arabiya @PVrecycle_com]

Google, NASA launch AI model to map methane emissions from space | Waste Dive @WasteDive
https://t.co/gRdmsOg6Qd

[PNASNews @PNASNews]

Identifying sources of methane is key to mitigating climate change because methane is a potent greenhouse gas. Deep learning approaches for analyzing data from the International Space Station can identify previously undetected methane plumes. In PNAS: https://t.co/d2GGmcK4p4

[Dr. Senthazal Ravi @senthazalravi]

Google + NASA taught AI to spot methane leaks from space.

Trained on 3.6M simulated plumes. Finds 50% more than human experts. +23k plumes globally, including 24 of 25 biggest landfill emitters.

https://t.co/NvcFX3e4yN

[Mindplex @Mindplex_AI]

A vision model trained on millions of simulated gas trails finds, outlines, and traces methane sources in NASA images from the International Space Station.

[News from Google @NewsFromGoogle]

A new study in PNAS introduces MAPL-EMIT, an AI model from Google and @NASAJPL that can track methane emissions globally from space.

By making methane sources easier to detect at scale, MAPL-EMIT could help turn detection into action—enabling faster, more targeted climate mitigation. 🌎

Learn more ↓ https://t.co/qcO2Avsb0N

[Climate TRACE @ClimateTRACE]

A powerful new tool to analyze methane plumes using satellite data – developed through a collaboration between Google Earth AI and NASA's Jet Propulsion Laboratory – boosts the world's ability to track and mitigate methane emissions: https://t.co/qAMRjjxnAF

[Mack @Analytics_699]

The challenge of spotting methane from space :

Measuring methane from space requires balancing three key factors:
(1) field of view (spatial coverage/revisit)(2) spatial resolution
(3) spectral resolution

Global mappers like TROPOMI were designed to detect small changes in background methane concentrations by integrating high coverage (approximately 2,600 km swath width), coarse spatial resolution (around 5.5 km x 3.5 km), and fine spectral sampling (0.1 nm)

The Methane Analysis and Plume Localization with EMIT model is a deep-learning framework that automates the detection, enhancement quantification, and source estimation of methane plumes globally, turning raw satellite data into scalable climate action ▶️
#AI #Google
https://t.co/8vPa2CxqXA

[𝐕𝐚𝐥𝐞𝐫𝐢𝐢 @valeriixai]

There are methane leaks on Earth we cannot see with our own eyes.

AI is now helping find them from space.

Google trained a model to search satellite data for signals hidden in light - and trace where the methane is coming from.

This is the kind of AI progress I love seeing
https://t.co/Yum5y8HP0z

[Yossi Matias @ymatias]

Methane drives a significant portion of human-induced warming, and meeting reduction targets under the Global Methane Pledge requires scalable methods to track localized point sources. Increasingly, scientists are looking to space-based imaging to find them.

Building on our long-term efforts in geospatial AI and Google Earth AI, our latest research published in Proceedings of the National Academy of Sciences (PNAS) introduces Methane Analysis and Plume Localization with EMIT (MAPL-EMIT). This deep-learning framework automates the detection, enhancement quantification, and source estimation of methane plumes globally using raw satellite data.

[Dushyant Arora @dushyantz]

Mapping global methane emissions from space with deep learning https://t.co/s4LKfwg2FS

[shipfrontier @shipfrontierai]

🛰️ Google + NASA MAPL-EMIT: vision transformer that detects methane leaks from space and pinpoints their exact source. Trained on 3.6M physics-simulated plumes injected into real NASA EMIT satellite scans.

Captures 84% of expert-labeled plumes and maps 50% more leaks than existing methods. Methane drives ~25% of global warming and is invisible to the naked eye. Open-sourced: model, synthetic data, Google Earth Engine app, and code.
https://t.co/LUbAunrGaQ

[👩‍💻 Paige Bailey @DynamicWebPaige]

👀 You can’t fix what you can’t see.

Methane is invisible to the naked eye, but drives ~25% of global warming. @Google + @NASAJPL just released MAPL-EMIT: a vision transformer that spots methane leaks from space and then pinpoints their exact source.

To train it, the researchers injected 3.6M physics-simulated plumes into real NASA EMIT satellite scans. It captures 84% of expert plumes and maps 50% more leaks than existing methods.

@GoogleAI's open-sourcing the model, synthetic data, @GoogleEarth Engine app, and code - take a look! 🛰️🌍

https://t.co/yTT3WRr0TQ

[Google Research @GoogleResearch]

Methane is a potent greenhouse gas driving approximately 25% of human-induced warming. MAPL-EMIT is a deep-learning model that automates global methane tracking from space to enable scalable climate action.

Read the full post and check out the dataset: https://t.co/2y38UXFwno

[Google AI @GoogleAI]

Residential solar power is set to play a crucial role in addressing increasing energy demand, but barriers to adoption persist. Today we announce an experimental expansion in the coverage of the Google Maps Platform Solar API. Learn more →https://t.co/eE7MxbbN2o

[Google AI @GoogleAI]

Join @iambettypeng today at 2pm in the #NeurIPS2024 Google Research booth to discuss Google's new Solar API, which aids the global transition to renewable energy by providing detailed imagery and solar potential estimates for buildings. Learn more at https://t.co/6aVpZ6RMeC

[Machine Learning (ML) Papers @Memoirs]

Satellite Sunroof: High-res Digital Surface Models and Roof Segmentation for Global Solar Mapping. https://t.co/LCjPphzzS7

[Varshit @Varshit_97]

@Hyderabad_Dev #Med4Dev

[Nishanth Reddy @Nishna_J]

Ship of Theseus @Hyderabad_Dev #Med4Dev