Remote Sensing Applications

🧠 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

🚀 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.

🛰️ New paper alert! Just published our framework for Earth Observation-based agricultural monitoring in Africa 🌍 The challenge: Many African countries can't fully leverage satellite technology for crop monitoring despite its massive potential for food security. Our solution: EO-NAM - a practical, phased framework that helps nations build sustainable agricultural monitoring systems using open-access satellite data. What it covers: 📋 Step-by-step implementation guide 🏛️ Institutional & technical requirements 💰 Financing strategies 📅 10-year roadmap to full autonomy Impact: Countries can proactively monitor crop conditions, forecast yields, and issue early warnings to mitigate potential risks. This is crucial for achieving the Zero Hunger goals. Big thanks to my amazing co-authors! 🙏 Read more: https://lnkd.in/emUmwgDY #EarthObservation #FoodSecurity #Africa #Agriculture #Sustainability #Research

AI Intensity in EO & GIS is exploding! Over the past few weeks, the geospatial AI landscape has surged with transformative research breakthroughs and I’m thrilled to witness this wave of innovation in real time. Here are some standout developments I’m particularly excited to share: 1. AlphaEarth Foundation Model Google DeepMind This newly unveiled “virtual satellite” model extracts compact, yearly embeddings for every 10 m pixel—from 2017 to 2024—by integrating diverse sources such as optical, radar, LiDAR, climate, and elevation data. These ready-to-use embeddings massively simplify environmental monitoring workflows and reduce storage needs by up to 16x. 2. OmniGeo A cutting-edge multimodal large language model, OmniGeo merges satellite imagery, metadata, and spatial text to enhance geospatial task performance. It represents a major step toward richer, more context-aware geospatial AI. 3. S2Vec This self-supervised framework learns geospatial embeddings through masked autoencoding of rasterized urban features. Its strength lies in socioeconomic inference—especially in areas where labeled data is scarce—making it a powerful tool for urban and social analytics. 4. Meta DINOv3: A Leap in Geo-Benchmarking Meta has launched DINOv3—a 7-billion-parameter frozen vision encoder that delivers state-of-the-art performance across segmentation, classification, depth estimation, and unsupervised object discovery. Impressively, when evaluated on GEO-Bench using Sentinel-2 and Landsat images (RGB only), DINOv3 ranks first in 10 out of 12 tasks—without any satellite-specific pretraining or fine-tuning. 5. ESA’s new Cloud Optimized GeoZarr format Supported by ESA, this project introduces cloud-native, high-performance access to Sentinel data via GeoZarr. It enables efficient handling of ND-array time-series geospatial data, significantly improving performance for large-scale Earth observation analytics. 6. Llama3-MS-CLIP (FAST-EO, ESA-Funded) Another exciting innovation from ESA’s FAST-EO project, Llama3-MS-CLIP is a vision–language model designed for multispectral data—enabling powerful zero-shot classification and retrieval in Earth Observation, going beyond standard RGB capabilities. Would you like to explore any of these topics in more detail, such as real-world use cases, technical deep dives, or how these tools compare with legacy geospatial frameworks? Comment below! #GeospatialAI #EarthObservation #GIS #MachineLearning #RemoteSensing #AI #SatelliteImagery #DeepLearning #FoundationModels #GoogleDeepMind #Meta #ESA #AlphaEarth #DINOv3 #ComputerVision #GeospatialData #Sentinel2 #Landsat #MultimodalAI #SelfSupervisedLearning #GeoZarr #CloudNative #SpatialAnalytics #UrbanAnalytics #EnvironmentalMonitoring #VisionLanguageModels #MultispectralData #GeospatialTechnology #SpatialIntelligence #DataScience #Innovation #TechTrends #Research #Breakthrough

NASA and IBM's #Prithvi just became the first geospatial #foundationmodel ever deployed in orbit. It's now running on the #Kanyini satellite and on a payload aboard the International Space Station. Satellites collect huge amounts of data, but bandwidth back to Earth is limited, so the #AI models running onboard today are usually small and locked to a single task. with a #foundation model it works differently, you upload one big general model, and when you want to teach the #satellite something new, you only send up a small decoder. the team tested this with flood and cloud detection, processing the imagery directly in orbit before it ever came home. #Prithvi was trained on 13 years of #Landsat and #Sentinel2 data, and it can be adapted for tasks like flood mapping, fire monitoring, and crop yield prediction. And because it's #opensource, anyone can build on it. This is the direction I'm most excited about in #remotesensing right now, not just bigger models, but moving the intelligence closer to where the data is actually being captured. Link to the Prithvi model: https://lnkd.in/g9K8RNKn

[new publication][open access] SpectralEarth: Training Hyperspectral Foundation Models at Scale, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Nassim Ait Ali Braham, Conrad Albrecht, Julien Mairal, Jocelyn Chanussot, Yi Wang and Xiaoxiang Zhu The dataset, pretrained models, and code are publicly available ! https://lnkd.in/gFeZuzpp Foundation models have triggered a paradigm shift in computer vision and are increasingly being adopted in remote sensing, particularly for multispectral imagery. Yet, their potential in hyperspectral imaging (HSI) remains untapped due to the absence of comprehensive and globally representative hyperspectral datasets. To close this gap, we introduce SpectralEarth, a large-scale multi-temporal dataset designed to pretrain hyperspectral foundation models leveraging data from the Environmental Mapping and Analysis Program (EnMAP). SpectralEarth comprises 538,974 image patches covering 415,153 unique locations from 11,636 globally distributed EnMAP scenes spanning two years of archive. Additionally, 17.5% of these locations include multiple timestamps, enabling multi-temporal HSI analysis. Utilizing state-of-the-art self-supervised learning (SSL) algorithms, we pretrain a series of foundation models on SpectralEarth, integrating a spectral adapter into classical vision backbones to accommodate the unique characteristics of HSI. In tandem, we construct nine downstream datasets for land-cover, crop-type mapping, and tree-species classification, providing benchmarks for model evaluation. Experimental results support the versatility of our models and their generalizability across different tasks and sensors. We also highlight computational efficiency during model fine-tuning. IEEE Geoscience and Remote Sensing Society (GRSS) #artificialintelligence #foundationmodels #remotesensing #hyperspectral Technical University of Munich Inria German Aerospace Center (DLR)

🌍 New Research Alert 🌍 In geospatial AI, we often assume that #FoundationModels must be trained with self-supervised learning on huge unlabeled corpora. But for tasks like land use & land cover (#LULC) mapping, we already have global LULC products. Yes—they’re noisy. But they’re also massive, global, and free. Why not use them as weak labels to train task-specific Geo FMs? 🚀 Introducing: #LandSegmenter A task-specific LULC foundation model built from weak supervision. Developed by my PhD student Chenying Liu, with @Wei Huang and myself. Highlights: 🔹 Trains on LAS, a 150k-sample global dataset (≈80% weak labels) 🔹 Handles RGB + multispectral inputs, from 5 cm to 30 m 🔹 Supports user-defined classes via text prompts 🔹 Zero-shot mapping across sensors & regions 🔹 New confidence-guided fusion to recover unseen categories 🔹 Strong performance across six benchmarks, especially at medium/low resolution 🧠 Takeaway: Weak supervision at global scale can rival—sometimes outperform—heavy SSL pipelines for task-specific Geo FMs. 📄 Paper: https://lnkd.in/dj6bNQAC 💻 Code & data: https://lnkd.in/d9gKG5d3 This work is supported by Munich Center for Machine Learning.

Glacial Lake Mapping Using Remote Sensing Geo-Foundation Model -- https://lnkd.in/gfKTw4BQ <-- shared paper -- https://lnkd.in/gFD5XCtZ <-- shared technical article - "How The Greenland Ice Sheet Fared In 2025" -- "HIGHLIGHTS:  • Proposed U-ViT model based on Prithvi GFM for multi-sensor glacial lake mapping.  • Achieved an F1 score of 0.894 on Sentinel-1&2, surpassing CNNs scoring below 0.8.  • Maintains strong performance with 50% less training data, proving efficiency.  • Excels in detecting small lakes (<0.01km²) and handling clouds and complex terrains. ABSTRACT: Glacial lakes are vital indicators of climate change, offering insights into glacier dynamics, mass balance, and sea-level rise. However, accurate mapping remains challenging due to the detection of small lakes, shadow interference, and complex terrain conditions. This study introduces the U-ViT model, a novel deep learning framework leveraging the IBM-NASA Prithvi Geo-Foundation Model (GFM) to address these issues. U-ViT employs a U-shaped encoder–decoder architecture featuring enhanced multi-channel data fusion and global-local feature extraction. It integrates an Enhanced Squeeze-Excitation block for flexible fine-tuning across various input dimensions and combines Inverted Bottleneck Blocks to improve local feature representation. The model was trained on two datasets: a Sentinel-1&2 fusion dataset from North Pakistan (NPK) and a Gaofen-3 SAR dataset from West Greenland (WGL). Experimental results highlight the U-ViT model’s effectiveness, achieving an F1 score of 0.894 on the NPK dataset, significantly outperforming traditional CNN-based models with scores below 0.8. It excelled in detecting small lakes, segmenting boundaries precisely, and handling cloud-shadowed features compared to public datasets. Notably, the U-ViT demonstrated robust performance with a 50% reduction in training data, underscoring its potential for efficient learning in data-scarce tasks. However, its performance on the WGL dataset did not surpass that of DeepLabV3+, revealing limitations stemming from differences between pre-training and input data modalities. The code supporting this study is available online. This research sets the stage for advancing large-scale glacial lake mapping through the application of GFMs…” #GIS #spatial #mapping #glaciallake #GeospatialFoundationModel #satellite #Sentinel #GaoFen #remotesensing #earthobservation #model #modeling #climatechange #glacial #glacier #melt #melting #UViT #deepleanring #AI #framework #performance #metrics #opensource

Agricultural monitoring of cropland is one of the most interesting and challenging applications of Earth Observation (satellite) data because cropland so dynamic and extensive. Most cropland is far more dynamic within a year than natural or semi-natural vegetation (think grasslands and forests): it can go from bare soil, through many distinguishable growth stages, to senescence, harvest, stubble, back to soil, and in some regions do this sequence more than once. In many regions, between years crops are rotated, so each year is different in management and crop growth. Further, that is also reflected in significant diversity between different fields in the same region in the same year, both different crops, and different management practices (especially timing of management). Lastly, the sheer amount and diversity of information which can be derived from EO data is vast compared to other land use/cover categories. You can derive crop type, field boundaries, planting dates, harvest dates, cover cropping practices, tillage events and practices, crop yield, growth stages/phenology, crop growth pressures (impacts of drought and disease), crop damage, soil properties (including soil carbon). And most of the above vary every year (or more in multi-cropping systems!)!

Harnessing Data from Space: Revolutionizing Indian Agriculture for a Resilient Future!! In an era where climate variability and resource constraints challenge global food systems, satellite Earth Observation (EO) data emerges as a transformative force for agriculture. As professionals in agribusiness, policy, and technology, we must explore how this "data from space" can drive precision, sustainability, and equity—particularly in India, where farming sustains over 130 million households and contributes 15% to GDP. Drawing from the The World Bank's recent insights on integrating EO with ground surveys, satellite technology offers unprecedented capabilities. Tools like ESA WorldCereal, an open-source platform leveraging Copernicus Sentinel missions, enable real-time crop monitoring and customized mapping at local-to-global scales. Similarly, Sen4Stat empowers National Statistical Offices to fuse EO with in-situ data, enhancing the accuracy and timeliness of agricultural statistics. These innovations address core needs: mapping land use, forecasting droughts, tracking crop health, and assessing productivity amid erratic monsoons. Consider India's context. ISRO - Indian Space Research Organization's RISAT (Radar Imaging Satellite) and collaborations under the Digital Agriculture Mission already provide high-resolution imagery for yield estimation and irrigation planning. Imagine scaling this with EO: smallholder farmers in Punjab's wheat belts could receive alerts on pest outbreaks via apps like Kisan Suvidha, reducing losses by up to 20%. In drought-prone Maharashtra, predictive analytics from Sentinel data could optimize water allocation, mirroring the 40% knowledge gains reported in by African counterparts. Globally, the 50X2030 Initiative demonstrates how such integrations cut survey costs while boosting data relevance—lessons ripe for India's National Crop Forecasting Centre. The benefits are profound: enhanced food security, resilient supply chains, and inclusive growth. By 2030, EO-driven decisions could add $15-20 billion to India's agri-economy through better risk management and export planning. Yet challenges persist—data accessibility gaps, harmonized protocols, and skill-building for rural extension workers. Policymakers must prioritize open-access platforms and public-private partnerships, akin to ESA's Global Development Assistance program. As we stand at this inflection point, let's commit to action. How can we accelerate EO adoption in India? I will be addressing many such questions at the 25th Edition of GeoSmart India, the premier event for geospatial technology to discover how geospatial knowledge is transforming India’s digital public infrastructure and driving innovation, sustainability, and growth. See you in Delhi on 3rd and 4th December 2025.