AI-Enhanced Remote Sensing For Environmental Studies

🧠 GPT changed language. Clay might change the way we understand Earth. Clay is an open-source foundation model for Earth: trained on massive amounts of satellite imagery across location and time. It transforms the complexity of environmental data into powerful embeddings that can be used to: ✅ Identify land cover, crop types, or urban expansion ✅ Detect change like wildfires, floods, or deforestation ✅ Power downstream models for prediction, classification, and mapping ✅ Serve as a backbone for custom geospatial AI pipelines The result? A model that understands Earth the way LLMs understand language. Training models is tough, plus you need access to massive amounts of data. As foundational models start to get better, the data backbone being built by Cloud-Native Geospatial Forum (CNG) data and computing systems that can leverage these models like those we are working on at Wherobots can help bring these models to global scale. This is bigger than just another geospatial model. It’s a signal that foundation models are coming to remote sensing, and with them, a new paradigm: 🧠 Pre-trained models that can be adapted everywhere 📡 Build models with fewer labels 🌱 Tackle climate, agriculture, and environmental challenges with speed If you’re working in geospatial AI, Earth observation, or climate data: Clay is worth watching. And using. It's open source and live on Hugging Face and GitHub. The geospatial foundation model era is bound to be an exciting one. 🌎 I'm Matt and I talk about modern GIS, geospatial data engineering, and how spatial thinking is changing. 📬 Want more like this? Join 5k+ others learning from my newsletter → forrest.nyc

Forest carbon monitoring gets an AI boost, reports Abhishyant Kidangoor. Forests have long been surveyed from above. Satellite data reveal where they stand and how they shrink or grow, while lidar—laser-based radar—has allowed scientists to map them in 3D, uncovering details that lie beyond human sight. Now, artificial intelligence is adding a new layer of insight. Earth-imaging company Planet has unveiled a Forest Carbon Monitoring tool that fuses its satellite imagery with lidar data. The tool can estimate carbon storage, tree height, and canopy cover in remote forests at a granular resolution of three meters. “It will help us understand aspects of the forest that might not be initially accessible to the naked eye,” says Andrew Zolli, Planet’s chief impact officer. Satellites track forest cover but not the carbon stored in biomass. Measuring this requires lidar, which calculates tree dimensions by measuring the time laser beams take to bounce off foliage. NASA’s GEDI mission, mounted on the International Space Station, has mapped swathes of forests, but coverage gaps persist. Planet’s tool aims to bridge these voids, training machine-learning models to infer carbon data in areas without lidar coverage. Initial findings from the tool have been striking. While deforestation ravages the Amazon, the northern reaches harbor untouched carbon reserves. “What really resonated with me is the understanding of where we have extant forest carbon stocks which we must absolutely protect,” says Zolli. The data also underpin Project Centinela, which supports conservation efforts in biodiversity hotspots like Tanzania’s Gombe Stream National Park. Meanwhile, carbon markets—often criticized for opacity—may gain credibility through applications of the tool argues Zolli: “The data gives a shared, common picture of what’s actually happening on the ground.” Planet’s innovation rests on decades of data, cutting-edge AI, and cloud computing. “We are the first generation that has had all three in place,” Zolli says, enabling swift, confident assessments of carbon across the globe. 📰 story: https://lnkd.in/gwRWf5Qf 📷: A view of carbon storage in forest and an area of fishbone deforestation in the Brazilian Amazon. Image courtesy of Planet.

🛰️ Imagine an AI that can read the Earth's story from space – pixel by pixel, month by month. Remote sensing AI models vary widely in their capabilities. Gabriel Tseng et al. developed Galileo, a multimodal model designed to more comprehensively analyze Earth observation data. The model integrates multiple data sources: - Multispectral imagery from Sentinel-2 - Synthetic aperture radar (SAR) from Sentinel-1 - Elevation and land cover maps - Time-varying weather data - Static geospatial coordinates Key technical features: - Adapted Vision Transformer (ViT) architecture - Processes 24 monthly time steps - Analyzes 96 × 96 pixel images at 10m resolution - Uses self-supervised learning to capture global and local features In validation across multiple datasets, Galileo's three model variants performed consistently well. Ablation studies provided insights into the model's most critical characteristics. The approach offers a more comprehensive method of analyzing satellite and geospatial data. https://lnkd.in/eqKj5_Dw #RemoteSensing #EarthObservation #AI #Geospatial __________________ Enjoyed this post? Like 👍, comment 💬, or re-post 🔄 to share with others. Click "View my newsletter" under my name ⬆️ to join 1500+ readers.