Five years ago we set on a mission to easify access to geospatial data. We reasoned there were too many use cases for us to count but if we stayed on mission, millions of customers would benefit. Today millions more geniuses will benefit from that mission. @RobinhoodApp

Join our weekly Generative AI study group every Friday at 8 am PT! Dig into geospatial reasoning, share strategies for patch-text alignment, discuss KV cache routing, and more! For registration, head over to to sign up.

Shared at our flagship Research@ event today: In a new technical report, we demonstrate how combining domain-specific foundation models, spatial tools, and upfront planning enables a Geospatial Reasoning agent to carry out complex spatial analysis and planetary prediction that standalone LLMs can’t. This and more on the blog →

CNN REPORTS THAT 100–200 U.S. MILITARY ADVISERS ARE NOW IN SAUDI ARABIA PROVIDING REAL-TIME INTELLIGENCE, TARGETING AND GEOSPATIAL SUPPORT AGAINST THE HOUTHIS, MARKING A SIGNIFICANT ESCALATION IN U.S. INVOLVEMENT.

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#

#DePIN# Leaders Just Dropped $43.74M in 30D Revenue ($12.35M) & #Helium# ($12.20M) dominating as real-world infrastructure turns into real cash on-chain. Decentralized compute, networks & geospatial the #tokenization# of physical assets is here. This is the backbone of #Web3#.

FFDW project partner @easierdataorg recently released the first Location Protocol spec with the goal of enhancing interoperability, transparency, and trust is geospatial data across decentralized and traditional systems. Learn more ⬇️