Geoawesome’s Post

Future-proofing Geospatial Intelligence: How Ordnance Survey is Embedding AI Responsibly. Ordnance Survey CTO Manish Jethwa explains how the national mapping agency is using machine learning and generative AI to deliver faster, smarter geospatial insights, while ensuring data transparency, human oversight and accuracy. 🔗 Read editorial here: https://lnkd.in/ebFq4tSw Manish Jethwa #OrdnanceSurvey #GeospatialIntelligence #ArtificialIntelligence #MachineLearning #GenerativeAI #ResponsibleAI #DataTransparency #DigitalMapping #PublicSectorTech #AIinGovernment

In an era defined by autonomy and AI-driven workflows, the role of the geospatial analyst is evolving and reshaping how we handle geospatial intelligence. This new article explores how artificial intelligence is transforming the defense and intelligence landscape—freeing analysts from routine tasks and empowering them to lead complex, multi-INT workflows. https://hubs.ly/Q03PmPrd0 #GeospatialIntelligence #AI #GEOINT #Geospatial

In an era defined by autonomy and AI-driven workflows, the role of the geospatial analyst is evolving and reshaping how we handle geospatial intelligence. This new article explores how artificial intelligence is transforming the defense and intelligence landscape—freeing analysts from routine tasks and empowering them to lead complex, multi-INT workflows. https://hubs.ly/Q03PmS3C0 #GeospatialIntelligence #AI #GEOINT #Geospatial

Google Earth AI: Geospatial Reasoning and Planetary Understanding. tech Review. The podcast provides the technical overview of Google Earth AI paper: Unlocking Geospatial Insights with Foundation Models and Cross-Modal Reasoning, presenting a new interoperable family of geospatial AI models developed primarily by Google Research. The core innovation is "Earth AI," a system that orchestrates foundation models—categorized into Imagery, Population, and Environment—via a Geospatial Reasoning Agent to solve complex planetary analysis queries. The paper provides extensive quantitative evaluation showing that combining these specialized models, such as using Population Dynamics Foundations alongside AlphaEarth Foundations, significantly improves predictive tasks like forecasting health statistics and FEMA risk scores. Furthermore, the source details the architecture and superior performance of the Geospatial Reasoning Agent over baseline large language models in tackling multi-step, complex descriptive and crisis-response scenarios. https://lnkd.in/gf5jK5H4

The End of Just Looking: AI is Having a Conversation with Our Planet What if we could move beyond just looking at satellite images of our planet and start having a deep, intelligent conversation with it? We're witnessing a monumental shift in geospatial intelligence, moving from siloed data analysis to holistic, planetary-scale reasoning, powered by AI. This is more than an incremental improvement; it represents a fundamental leap in planetary understanding. By integrating diverse datasets and enabling complex reasoning, we are gaining insights at a scale and resolution that were previously unimaginable, arming us with the potential to address some of the world's most pressing challenges. https://lnkd.in/dxvE9-pM #AI #GeospatialAI #GoogleEarthAI #DataScience #MachineLearning #ClimateTech #DisasterResponse #PublicHealth #Innovation

Finding a #needle in a #haystack just got easy. The new Mapersive Filter System makes it smoother and faster to locate exactly what you’re looking for across massive spatial datasets. No more endless scrolling or guesswork. Just clean filters, quicker insights, and a more fluid experience our users have been asking for. And this is just the beginning. Smart Filter (AI-based) is coming soon, it will understand what you mean, not just what you type. Discover a more intuitive way to explore your data with Mapersive. 👉 https://lnkd.in/eicuNJWJ #mapersive #productupdate #filtersystem #ai #smartfilter #spatialdata #urbantech #gis #smartcities

A study shows that AI Chatbot summaries produce shallower knowledge than traditional web search. LLMs are thus potentially less useful than web search if the goal is to develop procedural knowledge—an understanding of how to do things. In PNAS Nexus: https://ow.ly/NSCu50XlMaw

📊 𝐅𝐫𝐨𝐦 𝐌𝐚𝐩𝐬 𝐭𝐨 𝐌𝐨𝐝𝐞𝐥𝐬 #GeoAIUnlocked | 4️⃣ Once, we mapped what we could see. Now, GeoAI maps what we can predict. By training machine learning models on years of spatial data, we move from descriptive maps to predictive models — systems that learn where and when patterns repeat. 🧭 𝐂𝐥𝐮𝐬𝐭𝐞𝐫𝐢𝐧𝐠 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 (𝐥𝐢𝐤𝐞 𝐃𝐁𝐒𝐂𝐀𝐍)uncover hidden hotspots. 📈 𝐒𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐦𝐨𝐝𝐞𝐥𝐬 (𝐥𝐢𝐤𝐞 𝐗𝐆𝐁𝐨𝐨𝐬𝐭 𝐨𝐫 𝐑𝐚𝐧𝐝𝐨𝐦 𝐅𝐨𝐫𝐞𝐬𝐭𝐬) forecast event probabilities. 🕒 𝐒𝐩𝐚𝐭𝐢𝐨-𝐭𝐞𝐦𝐩𝐨𝐫𝐚𝐥 𝐝𝐞𝐞𝐩 𝐧𝐞𝐭𝐰𝐨𝐫𝐤𝐬 (𝐋𝐒𝐓𝐌, 𝐂𝐨𝐧𝐯𝐋𝐒𝐓𝐌, 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫𝐬) capture both place and time. Together, they turn the question from “Where did it happen?” to “Where is it likely to happen next?” That’s not just mapping — that’s geospatial foresight. #GeoAI #SpatialML #PredictiveMapping #AI #DataScience #GIS

If you're trying to make sense of geospatial AI, check out how these three companies are approaching this game-changing technology https://ow.ly/3uTc50XmNh8

Curious about how #AI assistants across #ArcGIS can boost productivity? Check out our blog to discover how these AI tools can help you #map, code, and translate. https://ow.ly/kCpb50XqEzt #GIS #Esri #GPN