AI Solutions For Environmental Data Analysis

🚀 AlphaEarth Foundations (AEF) - New from Google DeepMind I keep looking out for interesting usecases of AI. Deepmind folks are at it again. 📄 Paper: AlphaEarth Foundations on arXiv (https://lnkd.in/giHUwe2d) --- 🌍 What is AlphaEarth Foundations? AEF is a foundation model for Earth observation that turns sparse and messy satellite, climate, LiDAR, and even text data into dense embeddings at 10 m² resolution. These embeddings provide a universal feature space for mapping and monitoring the planet, outperforming all previous approaches — reducing mapping errors by ~24% on average. And the best part? The embeddings are already available as annual global datasets (2017–2024) for free: 👉 Earth Engine Data Catalog: Google Satellite Embedding V1 Annual - https://lnkd.in/g6dcv4-M --- 🛠 Why does this matter? (weekend project ?) For places like Bengaluru, India (or any fast-changing city), AEF makes it possible to: - Track urban growth and land use change with very few ground samples. - Monitor lakes and wetlands for encroachment and seasonal changes. - Map flood risk by combining rainfall, elevation, and land cover. - Identify urban heat islands and vegetation loss. - Support peri-urban agriculture with low-shot crop type classification. - Study biodiversity shifts (tree species, invasive plants) by linking with GBIF/iNaturalist data. In short, it’s like having a plug-and-play geospatial backbone — ready to support everything from city planning to climate adaptation. --- 🔧 For the Geeks Want to try it out? You can get started in minutes using Earth Engine + Python: 📘 Earth Engine Python Quickstart Docs - https://lnkd.in/g9zBBPJv 🌐 This is a big step toward planetary-scale AI for environmental monitoring — making high-quality maps possible even when labels are scarce. --- Further reading : 1. https://lnkd.in/gsXU2BqS 2. https://lnkd.in/gxJpqS6b --- Authors: Christopher Brown, Michal Kazmierski, Valerie Pasquarella, William J. Rucklidge, Masha Samsikova, Chenhui Zhang, Evan Shelhamer, Estefania Lahera, Olivia Wiles, Simon Ilyushchenko, Noel Gorelick, Lihui Lydia Zhang, Sophia Alj, Emily Schechter, Sean Askay, Oliver Guinan, Rebecca Moore, Alexis Boukouvalas, Pushmeet Kohli.

🌲 Forests don’t vanish silently… but satellites see it every day. Illegal logging, urban expansion, and climate change are rapidly reducing forest cover. Traditional monitoring is slow, manual, and often incomplete. Here’s how GeoAI + Deep Learning on Google Earth Engine (GEE) can change the game: ✅ Analyze multi-temporal satellite imagery (Landsat / Sentinel-2) ✅ Extract forest features like NDVI, canopy cover, and texture ✅ Train deep learning models (CNN / U-Net / ResNet) to detect forest loss ✅ Automate real-time forest change maps and alerts Outputs: • Forest loss / gain maps (seasonal or yearly) • Hotspot detection of deforestation • Predictive risk maps for forest degradation • Dashboards for decision-makers This isn’t just mapping — it’s actionable intelligence for conservation, policy, and climate resilience. I’m excited to connect with projects and organizations using GeoAI for environmental monitoring and sustainable forest management. #GeoAI #RemoteSensing #DeepLearning #ForestChange #ClimateTech #GEE #SpatialAnalytics #EnvironmentalMonitoring

🌍📡 Introducing My Global RUSLE–AI Toolkit in Google Earth Engine (1985–2024) #OnOneClick you will get your results any where in the world. Thrilled to share my latest research contribution — a fully automated RUSLE (Revised Universal Soil Loss Equation) Soil Erosion Analysis Toolkit, built entirely in Google Earth Engine (GEE) and enhanced with state-of-the-art AI/ML models. This toolkit processes 40 years of satellite data (1985–2024) to generate high-resolution, annual soil erosion maps, factor layers, trends, predictions, and AI-assisted susceptibility modelling. 🚀 What the Toolkit Does ✔ Automates R, K, LS, C, P factor computation for every year (1985–2024) ✔ Integrates multi-sensor data (Landsat, Sentinel, CHIRPS, DEM, LandCover datasets) ✔ Generates annual soil erosion maps, spatial statistics, time-series trends ✔ Performs AI-based soil erosion susceptibility modelling using: 🔹 Support Vector Machine (SVM) 🔹ANN 🔹 RNN 🔹 CNN 🔹 Classification & Regression Tree (CART) 🔹 Random Forest (RF) 🔹 k-Nearest Neighbors (kNN) 🔹 Logistic Regression ✔ Produces class-wise area results, charts, accuracy assessment, ROC/AUC ✔ Provides a single-click interface using GEE UI Panels ✔ Allows global-scale or watershed-scale implementation for any AOI 🧠 Why This Toolkit Matters Soil erosion remains one of the most critical global environmental challenges—impacting: Reservoir siltation Agricultural productivity River morphology Water resource planning Climate resilience By combining Earth Observation, Cloud Computing, and Machine Learning, this toolkit bridges the gap between hydrology, geomorphology, and geospatial AI. 🛰 Data Sources (1985–2024) Landsat 5, 7, & 8 surface reflectance Sentinel-2 MSI CHIRPS rainfall SoilGrids / OpenLandMap SRTM / ASTER DEM MODIS NDVI Global LULC datasets 🛠 Applications 🔹 Soil erosion mapping & monitoring 🔹 Sediment yield estimation for reservoirs 🔹 Climate-driven land degradation studies 🔹 Watershed prioritization 🔹 AI-based erosion risk assessment 🔹 Policy & decision-support systems 🎯 Outcome A scalable, reproducible, and globally deployable toolkit enabling researchers, agencies, and policymakers to monitor and predict soil erosion at unprecedented temporal depth and spatial resolution. 📥 If you want access to the toolkit or want to collaborate, feel free to connect! (+923359435216) Advancing geospatial intelligence for a sustainable and climate-resilient future. 🌱🌍

AI has no place in sustainability. There’s a familiar stance I hear a lot in sustainability circles. AI uses a lot of energy. So using it for sustainability sounds… contradictory. But that argument misses the bigger picture. AI isn’t just consuming energy. It’s helping us use less of it too. Used well, AI is already solving real sustainability problems. Not hypotheticals. Not R&D lab demos. Live, operational tools that help businesses reduce emissions, speed up reporting, and make better decisions. Here’s what that looks like in practice: 1. Energy grid optimisation In the UK, the National Grid is using AI to forecast solar energy production by analysing satellite images and weather data. If clouds are expected to lower solar output in, say, Cornwall 30 minutes from now, the grid can prep alternative sources in advance. That means fewer blackouts and lower emissions from fossil backup plants. DeepMind did something similar for wind power. Their AI predicted wind farm output 36 hours in advance, which increased the commercial value of wind energy by around 20 percent. Why? Because energy providers could schedule when to send power to the grid with more certainty. 2. Streamlined carbon accounting AI tools now scan invoices, utility bills and PDF reports to pull out emissions data automatically. They match spend categories to emissions factors and calculate Scope 1, 2 and 3 outputs in seconds. That turns carbon accounting from a once-a-year headache into a real-time management tool. 3. Transparent supply chains Unilever has tested AI platforms that combine satellite imagery with supply data to flag illegal deforestation in palm oil regions. If a patch of rainforest is cleared where it shouldn’t be, AI catches it fast and alerts their team. No need to wait for an audit or third-party tipoff. 4. Faster climate simulations Traditional climate models take weeks or months to run. New AI-driven models can simulate complex climate scenarios up to 25 times faster. That unlocks planning tools for city councils, small businesses and insurers who can’t wait months to model flood risks or heat exposure. Yes, AI needs energy to run. But if it helps avoid 10 times more emissions than it creates, the trade-off makes sense. So the question isn’t whether AI belongs in sustainability. It’s whether we’re serious about using every tool we have to solve the problems in front of us. At Leafr, we’ve seen consultants use AI to cut time and cost on energy audits, validate supplier claims, and surface risks early. When paired with the right human expertise, AI becomes a multiplier. Because the planet doesn’t care if a human or a machine found the emissions. It just cares that they’re found and cut. Follow Gus Bartholomew (Leafr 🌿)for more and repost if you found useful. Use Leafr to find the sustainability specialists you need to support your AI efforts

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

NEW: Excited to share an “AI for good” story: Imagine if conservation groups, scientists, and local governments could easily use AI to take on challenges like deforestation, crop failure, or wildfire risk, with no AI expertise at all. Until now, that’s been out of reach—requiring enormous, inaccessible datasets, major budgets, and specialized AI know-how that most nonprofits and public agencies lack. Platforms like Google Earth AI, released earlier this year, and other proprietary systems have shown what’s possible when you combine satellite data with AI, but those are closed systems that require access to cloud infrastructure and developer know-how. That’s now changing with OlmoEarth, a new open-source, no-code platform that runs powerful AI models trained on millions of Earth observations—from satellites, radar, and environmental sensors, including open data from NASA, NOAA, and the European Space Agency—to analyze and predict planetary changes in real time. It was developed by Ai2, the Allen Institute for AI, a Seattle-based nonprofit research lab founded in 2014 by the late Microsoft co-founder Paul Allen. https://lnkd.in/eVsrWtkC

The world is changing. 2024 was the first year to surpass 1.5 degrees Celsius. Climate change, deforestation, pollution—the challenges aren’t new. We have been hearing about them for years. But can AI become a true game-changer in addressing them? In 2024, natural disasters caused $368 billion in economic losses worldwide, with 60% of these damages uninsured. Despite this, AI-powered tools are beginning to shift how we respond. ➡️ AI-powered tools, like Google Earth’s Cloud Score+, are stepping up to fill critical gaps. By providing clearer images of ecosystems obscured by clouds, such innovations make monitoring the environment faster and more accurate. ➡️ AI Algorithms now track polar ice melt, analyze deforestation trends, and even alert authorities to illegal logging within hours. ➡️  In Brazil, AI-driven deforestation monitoring cut illegal activities by 20% last year, saving millions of hectares of rainforest. These advancements highlight how AI turns raw satellite data into tools for immediate action. ➡️  Researchers are deploying AI-powered drones to track marine species, improving conservation efforts. Smart fishing systems, driven by AI, help reduce bycatch by distinguishing between target fish and other marine life. ➡️ Air quality monitoring is being transformed by AI. Google’s Air View+ system in India has improved air quality in cities like Aurangabad by 50% over three years, proving how AI can drive cleaner urban environments. The possibilities are limitless, from personalized climate action plans to autonomous drones monitoring remote ecosystems. But technology alone isn't enough. AI gives us the tools to combat environmental crises, but the question remains: how will you contribute? Whether adopting eco-friendly habits, supporting AI initiatives, or staying informed, every action counts. What do you think? #AI #climatechange #technology

New paper – A foundation model for the Earth system Abstract “Reliable forecasting of the Earth system is essential for mitigating natural disasters and supporting human progress. Traditional numerical models, although powerful, are extremely computationally expensive. Recent advances in artificial intelligence (#AI) have shown promise in improving both predictive performance and efficiency, yet their potential remains underexplored in many Earth system domains. Here we introduce Aurora, a large-scale foundation model trained on more than one million hours of diverse geophysical data. Aurora outperforms operational forecasts in predicting air quality, ocean waves, tropical cyclone tracks and high-resolution #weather, all at orders of magnitude lower computational cost. With the ability to be fine-tuned for diverse applications at modest expense, Aurora represents a notable step towards democratizing accurate and efficient Earth system predictions. These results highlight the transformative potential of AI in environmental forecasting and pave the way for broader accessibility to high-quality #climate and #weather information.” Bodnar, C., Bruinsma, W.P., Lucic, A. et al. A foundation model for the Earth system. Nature 641, 1180–1187 (2025). https://lnkd.in/eh8wQ2wx

𝗛𝗮𝗿𝗻𝗲𝘀𝘀𝗶𝗻𝗴 𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗢𝗳 𝗗𝗮𝘁𝗮 𝗙𝗼𝗿 𝗔 𝗚𝗿𝗲𝗲𝗻𝗲𝗿 𝗧𝗼𝗺𝗼𝗿𝗿𝗼𝘄 𝗔𝗻𝗱 𝗨𝘀𝗶𝗻𝗴 𝗔𝗜 𝗧𝗼 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁𝗹𝘆 𝗥𝗲𝗱𝘂𝗰𝗲 𝗘𝗺𝗶𝘀𝘀𝗶𝗼𝗻𝘀 𝗔𝗰𝗿𝗼𝘀𝘀 𝗧𝗵𝗲 𝗚𝗹𝗼𝗯𝗲. In a world where climate action can often feel daunting, having access to precise, actionable data can give us a clear way forward. Imagine having the ability to understand where your city's emissions are coming from, be it where we live or near our workplaces.   I was recently exploring the Environmental Insights Explorer (EIE), developed by Google. EIE, helps cities/regions measure emission sources and refine strategies to reduce emissions by leveraging access to Google’s mapping data and machine learning capabilities. With a few clicks, EIE reveals the emissions footprint from heating, cooling, and powering residential and commercial buildings. This data can help develop usable insights that act as a roadmap showing how and where to reduce energy consumption and switch to greener alternatives. There are additional features, like measuring the transportation impact, rooftop solar panel potential, and tree canopy coverage across different areas in the city. Air quality data is also available for certain cities across the globe. Public data is currently available for viewing for > 2,400 places in a database that comprises thousands of cities/regions. What an interesting and usable example of leveraging data for impact in local communities – while leveraging AI for climate change solutions and intelligent emissions reductions. Image Source: Google Environmental Insights Explorer Website  #techonology #sustainability #future #impact

15 GeoAI tools you should know about, according to the UN-Habitat (United Nations Human Settlements Programme) • Aino: An open-source, free QGIS plugin that enables users to interact with datasets using natural language prompts. It primarily utilizes OpenStreetMap data and standard QGIS datasets for land-use planning, mobility, and disaster management.  • BEAM (Building & Establishment Automated Mapper): An AI technology developed by UNITAC to detect building footprints from satellite or aerial imagery, specifically used to identify informal settlements.  • ClimateReady Barcelona: A project developing a vulnerability map that combines open geospatial data and AI to simulate and address extreme heat risks in urban environments.  • DTN: A proprietary subscription service that uses satellite and real-time weather datasets for disaster preparedness and environmental monitoring.  • Digital Blue Foam (DBF): A proprietary platform for land-use, housing, and climate resilience planning.  • FlyPix.AI: A subscription platform used for analyzing drone and satellite imagery for waste management and land-use planning.   • GEOVIA (Dassault Systèmes): A proprietary service focused on land-use planning, infrastructure, and climate resilience simulations.  • GeoAI (Python package): An open-source toolkit used for land-use planning, environmental monitoring, and disaster management.  • GeoRetina Inc. AI (GRAI): A platform offering various plans for analyzing raster and vector data in governance and environmental resilience.  • GLOBEHOLDER AI: A proprietary tool that uses "geo-embeddings" to forecast ride-hailing demand and support mobility planning.  • Google AlphaEarth Foundations (DeepMind): A research model accessed via Google Earth Engine that uses global satellite embeddings for water management and climate resilience.  • Green City Watch (TreeTect): An open-source initiative for monitoring vegetation, tree canopy, and public health.  • Heli AI: A subscription-based platform for analyzing user-uploaded GIS data in infrastructure and mobility.  • InflowGo: A proprietary tool for hydrological and drainage management.  • WebGIS Urban Sprawl Information System (USIS): A public tool using satellite data to monitor urban growth and sprawl in India.  General Software, Frameworks & Libraries • ArcGIS Pro: Professional GIS software used in conjunction with deep learning models for tasks like building change detection.  • QGIS: An open-source GIS platform used widely for disaster management, environmental monitoring, and waste management.  • CVAT.AI (Computer Vision Annotation Tool): An open-source tool used for annotating the data required to train computer vision models.  • TensorFlow, PyTorch, & Keras: Standard development libraries used to build and run the deep learning models mentioned throughout the report.