Strengthening Climate Models with Complementary Data

𝐓𝐞𝐫𝐫𝐚𝐅𝐌: 𝐔𝐧𝐢𝐟𝐲𝐢𝐧𝐠 𝐒𝐀𝐑 𝐚𝐧𝐝 𝐎𝐩𝐭𝐢𝐜𝐚𝐥 𝐃𝐚𝐭𝐚 𝐟𝐨𝐫 𝐄𝐚𝐫𝐭𝐡 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐭𝐢𝐨𝐧 Current EO models face a fundamental limitation: they're often designed for single sensor types, missing the complementary information available when combining radar and optical data. This fragmentation means we can't fully leverage the wealth of satellite observations monitoring our planet. Danish et al. introduced TerraFM, a foundation model that unifies multisensor Earth observation in an unprecedented way. 𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫𝐬: Earth observation data comes from diverse sensors—optical imagery captures surface details but is limited by clouds and darkness, while SAR radar penetrates clouds and works day-night but provides different information types. Many current models handle these separately, but the real world requires integrated understanding. Climate monitoring, disaster response, and agricultural assessment all benefit from fusing these complementary data streams. 𝐊𝐞𝐲 𝐢𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧𝐬: ◦ 𝐌𝐚𝐬𝐬𝐢𝐯𝐞 𝐬𝐜𝐚𝐥𝐞 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠: Built on 18.7M global tiles from Sentinel-1 SAR and Sentinel-2 optical imagery, providing unprecedented geographic and spectral diversity ◦ 𝐋𝐚𝐫𝐠𝐞 𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐭𝐢𝐥𝐞𝐬: Uses 534×534 pixel tiles to capture broader spatial context compared to traditional smaller patches, enabling better understanding of landscape-scale patterns ◦ 𝐌𝐨𝐝𝐚𝐥𝐢𝐭𝐲-𝐚𝐰𝐚𝐫𝐞 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞: Modality-specific patch embeddings handle the unique characteristics of multispectral and SAR data rather than forcing them through RGB-centric designs ◦ 𝐂𝐫𝐨𝐬𝐬-𝐚𝐭𝐭𝐞𝐧𝐭𝐢𝐨𝐧 𝐟𝐮𝐬𝐢𝐨𝐧: Dynamically aggregates information across sensors at the patch level, learning how different modalities complement each other ◦ 𝐃𝐮𝐚𝐥-𝐜𝐞𝐧𝐭𝐞𝐫𝐢𝐧𝐠: Addresses the long-tailed distribution problem in land cover data using ESA WorldCover statistics, ensuring rare classes aren't overshadowed 𝐓𝐡𝐞 𝐫𝐞𝐬𝐮𝐥𝐭𝐬: TerraFM sets new benchmarks on GEO-Bench and Copernicus-Bench, demonstrating strong generalization across geographies, modalities, and tasks, including classification, segmentation, and landslide detection. The model achieves the highest accuracy on m-EuroSat while operating at significantly lower computational cost compared to other large-scale models. 𝐁𝐢𝐠𝐠𝐞𝐫 𝐢𝐦𝐩𝐚𝐜𝐭: TerraFM represents a shift toward unified systems that can seamlessly combine different sensor types to provide more reliable insights. This approach could transform applications from precision agriculture and climate monitoring to disaster response, where the ability to integrate multiple data sources can mean the difference between accurate assessment and missed critical changes. paper: https://lnkd.in/ev_VhSPA code: https://lnkd.in/eQVYrJZV model: https://lnkd.in/eqaeD3dW #EarthObservation #FoundationModels #RemoteSensing #MachineLearning #GeospatialAI

🌍 New paper alert! 🚀 We’re excited to introduce Copernicus-FM, a major step toward unified foundation models for Earth observation (EO) and climate science, a great work led by my brilliant PhD student Yi Wang. 📌 What’s inside: 🔹 Copernicus-Pretrain: The largest and most diverse EO dataset to date—over 18.7M images from all Sentinel missions (S1–S5P + DEM), enabling joint modeling of Earth’s surface and atmosphere. 🔹 Copernicus-FM: A flexible, multimodal foundation model that dynamically handles any spectral or non-spectral EO data—including metadata like time, location, and sensor specs. 🔹 Copernicus-Bench: A comprehensive benchmark spanning 15 tasks (cloud detection, land cover, air quality, biomass & more)—bridging EO, weather, and climate. 💡 Why it matters: 🌐 First model to unify all major Copernicus Sentinel modalities. 🛰️ Outperforms prior models across surface and atmospheric tasks. 📈 Demonstrates that EO embeddings can enhance climate modeling beyond traditional geographic features. 📎 Check it out: 📝 Paper: arXiv link – https://lnkd.in/d8CtuXpY 💻 Code & data: https://lnkd.in/dFz6MV_5 Big shoutout to the amazing team at the Technical University of Munich (Zhitong Xiong, Chenying Liu, Adam Stewart, @Thomas Dujardin), and our partners at National Technical University of Athens (Nikolaos Ioannis Bountos, @Angelos Zavras, Ioannis Papoutsis) and NVIDIA (Franziska Gerken, Laura Leal-Taixé)! 🙌 Thanks a lot to European Commission to fund this work via ThinkingEarth! #EarthObservation #FoundationModels #ClimateAI #RemoteSensing #Copernicus #AIforScience #OpenScience #SatelliteImagery Munich Center for Machine Learning, TUM School of Engineering and Design (ED), TUM MDSI, International Future AI4EO Lab, Research in Bavaria

How I Apply GeoAI to Strengthen Climate Resilience Globally As climate risks escalate floods, droughts, wildfires, extreme heat the question isn't if they will happen, but how prepared are we? As a Senior GIS & GeoAI professional, my mission is to help governments, businesses, and communities turn data into actionable climate intelligence. Here’s how I approach solving these critical challenges through GeoAI frameworks: 1. Data Fusion at Scale I integrate multi source data: satellite imagery (Sentinel, Landsat, MODIS), IoT sensor feeds, historical climate records, socio economic datasets, and terrain models (DEM/DSM). Using cloud platforms like Google Earth Engine, AWS, and BigQuery GIS, I process and scale data pipelines globally. 2. AI/ML Powered Climate Risk Models I design machine learning and deep learning models (e.g., Random Forest, CNNs, LSTMs) to predict climate hazards. For example: • Flood risk modeling using precipitation forecasts + terrain data. • Drought stress analysis via NDVI time series + evapotranspiration trends. • Wildfire susceptibility mapping with vegetation indices + wind simulation. 3. GeoAI for Decision Support GeoAI insights are only valuable if they support real-world decisions. I build interactive dashboards and APIs that provide risk heatmaps, early warning signals, and resilience indicators — helping urban planners, policymakers, and emergency teams take proactive steps. 4. Community Centric Resilience I embed social vulnerability data (poverty, population density, infrastructure gaps) into climate risk models to ensure solutions are equitable, leaving no one behind. Real-World Applications I Work On: Urban climate risk platforms for heatwave mitigation in Asia Flood resilience dashboards for coastal cities AI-powered drought monitoring tools for agritech and NGOs Disaster-ready infrastructure planning for smart cities My vision: To build AI-augmented geospatial systems that move beyond reactive disaster response and enable long-term adaptation strategies. As climate extremes grow, the intersection of AI + GIS is becoming the backbone of resilience planning and I’m committed to leading this transformation globally. #GeoAI #ClimateResilience #GeospatialAI #AIforGood #SustainableCities #DisasterRiskReduction #ClimateAdaptation #RemoteSensing #SmartCities #GIS #ClimateTech #ResilientInfrastructure

Have you ever wondered how we can use cutting-edge technology to better understand snow-covered regions, like the vast, snowy landscapes of Finland? For researchers like me working in snow hydrology, the challenge is real—and so is the excitement! Snow plays a critical role in water resource management, flood forecasting, and even global climate systems. But to model it accurately, we rely on satellite data. And here’s the catch: #Optical satellites give us stunning high-resolution imagery, but clouds and winter darkness (very common in Finland!) often make them unusable. #Microwave satellites, on the other hand, work through clouds and at night but come with very coarse spatial resolution (10–25 km). So how do we integrate these complementary yet vastly different data sources? That’s where #deep_learning comes into play. By applying advanced techniques like: #Super-resolution models, we can enhance the spatial quality of coarse microwave data. #Domain adaptation algorithms, we align data with different resolutions and temporal frequencies. #Data fusion techniques, such as convolutional neural networks (CNNs) and attention-based models, we combine optical and microwave satellite data to get the best of both worlds. These methods allow us to estimate key snow parameters—like snow cover, snow cover fraction, snow depth, snow water equivalent (SWE), and runoff potential—with greater accuracy than ever before. This is particularly important in regions like Finland, where more than 80% of the sky is cloud-covered during winter. Without innovative solutions, crucial snow hydrology models and climate forecasts could fall short of their potential. The journey isn’t without challenges, but the goal is worth it: a future where we use AI and remote sensing to predict, protect, and prepare for climate impacts. What deep learning methods or challenges have you encountered in your field? Let’s collaborate and share ideas to make breakthroughs together! #DeepLearning #SnowHydrology #RemoteSensing #SatelliteData #ClimateForecasting #AIForClimate