What if you could see a city as it looked 100 years ago, or visualize a new basketball court at your local community center? Starting today, we're bringing our latest image generation capabilities to @GoogleEarth on the web. Powered by Nano Banana 2, you can combine rich satellite and 3D imagery with text prompts to reimagine your favorite place around the globe. Just zoom in, tap “create image,” and type whatever you want to see! Available now for everyone to start creating:

It’s been a busy couple of weeks! ICYMI, here’s the recap ⬇️ — Gemini Robotics 2 from @GoogleDeepmind brings whole-body intelligence to robots — Gemini 3.5 Flash-Lite is our fastest, most cost-effective model optimized for high-speed agentic workflows — Gemini 3.6 Flash delivers faster, more accurate performance across tasks while using significantly fewer tokens — Gemini 3.5 Flash Cyber is our new, cost-effective model optimized for finding and fixing software vulnerabilities at scale, available exclusively to governments and trusted partners — Nano Banana 2 in @GoogleEarth lets you reimagine places and generate custom images using satellite, aerial, and 3D imagery — Lyria 3.5 is the newest music model from Google Deepmind, now powering @googleflowmusic — @Gemini_Notebook (formerly NotebookLM) launched Collections, a new way to organize your notebooks

Google has added its flight simulator to the web version of Google Earth, making it available to all users worldwide. >The feature runs directly in any modern browser at with no downloads or installations required. >Users can open the simulator from the Tools menu after selecting Explore and pilot a virtual aircraft over detailed 3D terrain, buildings, and satellite imagery across the entire planet. >It uses simplified flight physics designed for casual exploration and works with standard mouse and keyboard controls.

🚨 WARNING: Google Earth now lets users add anything to satellite images using Nano Banana 2 AI. This feature will flood the internet with fake satellite images and seriously damage the credibility of real ones. Example already circulating: a fabricated image of a nuclear power plant in Iran. Trust in satellite imagery just took a major hit.

What if you could build an epidemic outbreak prediction model in minutes rather than weeks? Title: Planetary prediction engine: Automating global models via Earth AI URL: Google's Planetary Prediction Engine (PPE) automatically builds geospatial prediction models from natural language queries. For problems like public health, food security, and infectious disease outbreaks, it compresses weeks of manual data curation and feature engineering into minutes — no specialized engineering team required. Key Points 🌍 A three-stage automated pipeline — from query to model Stage 1 translates natural language into geographic constraints and retrieves covariates from Data Commons and Google Earth Engine. Stage 2 fuses structured covariates with embeddings from Population Dynamics Foundation Models and AlphaEarth. Stage 3 searches across regularized linear models, gradient-boosted trees, and MLPs. Each stage embeds a "Feature Gate" (leakage prevention) and an "Overfitting Guard Protocol" (self-correcting risk assessment). 🔬 Multimodal fusion outperforms either modality alone — across every experiment Combining structured statistical covariates with latent foundation model embeddings captures information neither source provides individually. This synergy was confirmed across all experiments, establishing a new architectural pattern for geospatial prediction. 📊 Validated across three high-stakes domains ・US CDC health indicators (21 types): R² 76.8% vs 60.0% baseline ・Nigeria food security downscaling: R² 66.1% vs 31.5% baseline (2× improvement) ・DRC Ebola outbreak prediction: Recall@10 83.3% (+10.3 percentage points vs Bayesian baseline) The real impact is democratization: humanitarian organizations and researchers without dedicated engineering teams can now get evidence-based predictions within minutes when it matters most. #EarthAI# #PublicHealth#