Practical uses of complex climate data

Earth Observation is no longer just about capturing images from orbit. It’s rapidly becoming one of the most important tools we have to understand what’s happening on the ground—and act on it. Recent developments in EO are showing a clear trend: using satellite data to support real-time, local decisions in areas that impact lives, environments, and economies. Here are four examples that stand out: 1. Detecting Wildfires Before They Spread Google and Muon Space are building Fire Sat, a constellation of over 50 satellites that will scan fire-prone areas every 15 minutes. With real-time thermal imaging and cloud-based AI, it’s designed to catch wildfires early—before they become disasters. 2. Mapping Carbon Storage from Orbit The European Space Agency’s Biomass satellite uses a powerful radar system to measure how much carbon Earth’s forests are actually storing—by looking through the canopy itself. This gives scientists a more accurate understanding of climate-related forest change and carbon sinks. 3. Monitoring Land Use with Consistent Imaging EarthDaily Analytics launched the first satellite in a new constellation purpose-built for high-frequency, high-accuracy landscape monitoring. It’s especially relevant in agriculture, forestry, and environmental policy—where visibility over time matters more than snapshots. 4. Enabling Localized Impact Forecasting Xoople has developed a cloud-native Earth Observation platform that blends EO data with local models to forecast regional environmental risks—like floods, soil degradation, or vegetation stress. It’s EO made practical for governments and agencies on the front lines of climate and resource planning. These aren’t just satellites in orbit. They’re part of a growing EO ecosystem that’s focused on enabling faster, more confident action—where and when it’s needed most. From archive to alert. From static to streaming. From observation to intervention.

How can data help us tackle environmental challenges like climate change and deforestation? The Amazon Sustainability Data Initiative (ASDI) is making waves by connecting researchers, businesses, and governments with powerful environmental data resources, so they can take meaningful action on critical issues. Imagine trying to assess the impact of deforestation across a supply chain: The data needed is vast and complex. ASDI steps in to simplify that, offering global datasets for studying climate, natural resources, and biodiversity—all on a shared platform. Take Apple, for example. With ASDI data, they were able to pinpoint specific areas where deforestation was affecting their supply chain and then invest in reforestation. It’s not just corporations benefitting either; in California, government agencies used ASDI’s satellite imagery to identify parts of the coastline most at risk of erosion from rising sea levels, allowing for focused conservation efforts. These stories show that ASDI is more than just a data platform; it’s a tool for turning complex environmental data into clear insights. Having easy access to such data can make all the difference. Researchers, environmental groups, and businesses alike can now collaborate more easily, using shared knowledge to address sustainability challenges more effectively. For those interested in exploring ASDI’s offerings, the AWS Registry of Open Data provides tutorials and hands-on resources that make this data accessible to anyone. Tackling environmental issues takes a collective effort, and initiatives like ASDI are opening doors for more people to get involved in finding solutions that benefit us all. #Biodiversity #DataAnalysis #Sustainability #ClimateChange #EnvironmentalSustainability

Every year, natural disasters hit harder and closer to home. But when city leaders ask, "How will rising heat or wildfire smoke impact my home in 5 years?"—our answers are often vague. Traditional climate models give sweeping predictions, but they fall short at the local level. It's like trying to navigate rush hour using a globe instead of a street map. That’s where generative AI comes in. This year, our team at Google Research built a new genAI method to project climate impacts—taking predictions from the size of a small state to the size of a small city. Our approach provides: - Unprecedented detail – in regional environmental risk assessments at a small fraction of the cost of existing techniques - Higher accuracy – reduced fine-scale errors by over 40% for critical weather variables and reduces error in extreme heat and precipitation projections by over 20% and 10% respectively - Better estimates of complex risks – Demonstrates remarkable skill in capturing complex environmental risks due to regional phenomena, such as wildfire risk from Santa Ana winds, which statistical methods often miss Dynamical-generative downscaling process works in two steps: 1) Physics-based first pass: First, a regional climate model downscales global Earth system data to an intermediate resolution (e.g., 50 km) – much cheaper computationally than going straight to very high resolution. 2) AI adds the fine details: Our AI-based Regional Residual Diffusion-based Downscaling model (“R2D2”) adds realistic, fine-scale details to bring it up to the target high resolution (typically less than 10 km), based on its training on high-resolution weather data. Why does this matter? Governments and utilities need these hyperlocal forecasts to prepare emergency response, invest in infrastructure, and protect vulnerable neighborhoods. And this is just one way AI is turbocharging climate resilience. Our teams at Google are already using AI to forecast floods, detect wildfires in real time, and help the UN respond faster after disasters. The next chapter of climate action means giving every city the tools to see—and shape—their own future. Congratulations Ignacio Lopez Gomez, Tyler Russell MBA, PMP, and teams on this important work! Discover the full details of this breakthrough: https://lnkd.in/g5u_WctW  PNAS Paper: https://lnkd.in/gr7Acz25

In a The World Bank blog, German Caruso and Inés de Marcos introduce the Climate Effects Navigator Toolkit (CLIENT), a new interactive platform that combines climate and human capital data to analyze the long-term effects of disasters on health, education, and livelihoods. Key features: 📊 Tracks six hazard types (e.g. droughts, floods, heatwaves, hurricanes) over nearly five decades. Users can tweak thresholds, timeframes, and measure by land or population to analyze exposure, frequency, and severity at subnational levels. 🧍Uses census microdata to show who’s most affected. Users can explore how disasters impact school attendance, employment, electricity access, and more, before and after events, to highlight vulnerable groups like children or underserved households. ⚙ Overlays World Bank project data with climate-affected areas, helping identify where current initiatives are helping, and where gaps remain, enabling better targeting of climate-smart investments. 🔍 Integrates almost five decades of climate data across 38,000+ subnational regions and harmonizes climate records, census data, population stats, and administrative boundaries into a flexible toolkit with over 300 customizable parameters. 🗒️ Read the blog: https://lnkd.in/gGsURKjD 🖥️ Try the toolkit: https://lnkd.in/gUJB3Kkc 💻 Check out the Climate Change Knowledge Portal: https://lnkd.in/gw2eThqb

Unlocking the Power of GeoAI: From Raw Geospatial Data to Actionable Insights GeoAI is fundamentally changing the way we work with geospatial data. Today, artificial intelligence is not just a research topic, but a practical tool that helps us turn massive amounts of aerial imagery and lidar data into real, actionable information. By combining neural networks with proven photogrammetry and rule-based quality assurance, we can now extract detailed land cover maps, analyze urban surfaces, and even simulate urban climate with a level of precision that was unthinkable just a few years ago. One of the most exciting aspects is how GeoAI enables us to move beyond traditional mapping. With AI-powered segmentation, we can distinguish even the smallest features in urban environments and keep our data up to date. Thanks to TrueOrthos and advanced photogrammetric workflows, geometric distortions are a thing of the past, so data from different times and sensors can be perfectly aligned. This is essential for reliable change detection and multi-source analysis. But the possibilities go even further. Automated analysis of sealed and unsealed surfaces helps cities identify where to prioritize “desealing” for climate resilience. Parcel indexing allows us to aggregate key indicators like green space, building area, or solar installations at any scale, supporting truly data-driven decisions in urban planning and environmental monitoring. And with urban climate simulation, we can combine pixel-precise land cover data with 3D voxel models and CFD to visualize the effects of new trees, green roofs, or lighter pavements, before any construction begins. Even lidar point cloud classification benefits from GeoAI. By combining AI with rule-based checks and external data sources, we achieve robust, scalable, and quality-assured 3D mapping, reducing manual effort and increasing reliability, even in complex or changing environments. GeoAI is already a productive, scalable approach that is shaping the sustainable, data-driven development of our cities and landscapes. With annual updates and hybrid workflows, we ensure that results are not only precise and up to date, but also trusted and actionable. If you want to learn how to turn your geospatial data into valuable information using GeoAI, just reach out or send me a message. Let’s move from data to information, using GeoAI. 💡 Comment | Like | Share   👉 Follow me (Dr. Uwe Bacher) for more Information on exciting topics from the world of geospatial #GeoAI #Geospatial #AerialImagery #Lidar #UrbanPlanning #AI #SmartCities