Sentinel-1
PulseAugur coverage of Sentinel-1 — every cluster mentioning Sentinel-1 across labs, papers, and developer communities, ranked by signal.
7 day(s) with sentiment data
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Allen AI releases OlmoEarth embeddings for Earth observation analysis
Allen AI has released OlmoEarth embeddings, which are numerical representations of Earth observation data derived from their open-source OlmoEarth foundation models. These embeddings, available through the OlmoEarth Stu…
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New Deep Evidential Regression method estimates forest height with uncertainty
Researchers have developed a new method called Deep Evidential Regression (DER) to estimate forest height from satellite imagery, which also quantifies predictive uncertainty. This approach is particularly useful for sp…
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New SLED method offers scalable, cost-effective geospatial data encoding
Researchers have developed a new method called Scalable Location Encoding via Distillation (SLED) for creating efficient location encoders from geospatial data. Unlike previous methods that rely on computationally expen…
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Deep learning models benchmarked for offshore wind infrastructure monitoring
Researchers have benchmarked various deep learning models for classifying events related to offshore wind infrastructure using Sentinel-1 satellite data. The study compared ten different model training variants, includi…
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New open dataset OSSDD released for Sentinel-1 ship detection
Researchers have introduced OSSDD, a new open dataset designed for training neural networks to detect ships in Synthetic Aperture Radar (SAR) images. This dataset, built upon the OpenSARShip 1.0 dataset, provides 15,197…
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PhenoStitch pipeline maps crops without task-specific training
Researchers have developed PhenoStitch, a novel pipeline for panoptic crop mapping using satellite imagery that eliminates the need for extensive task-specific training. The system first employs a frozen Segment Anythin…
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New GEOID-Flood dataset advances AI-driven flood segmentation
Researchers have introduced GEOID-Flood, a new large-scale, multi-modal benchmark dataset designed for flood segmentation tasks. This dataset, derived from over ten years of Copernicus Emergency Management Service activ…
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Space2Ground 2.0 fuses street-level and satellite imagery for enhanced agricultural monitoring
Researchers have developed Space2Ground 2.0, a new framework and dataset designed to improve agricultural monitoring by fusing street-level imagery with satellite data. This system processes large volumes of crowdsource…
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TerraMind vs THOR: Architectural Differences Drive GFM Performance
A new research paper systematically compares two Geospatial Foundation Models (GFMs), TerraMind and THOR, developed under the European Space Agency's $\Phi$-lab. The study moves beyond aggregate scores to analyze archit…
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New AI models improve uncertainty quantification for Earth Observation data
Researchers have developed new methods for uncertainty quantification in Earth Observation (EO) regression tasks, crucial for applications like urban planning and climate policy. The proposed Gaussian UC and Quantile UC…
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New Optical-SAR framework improves slum mapping in Sub-Saharan Africa
Researchers have developed a novel Optical-SAR framework to improve the mapping of informal settlements in Sub-Saharan Africa, addressing the challenge that optical imagery alone struggles to differentiate these areas f…
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New method improves evaluation of rare-event AI detectors
A new paper proposes a "prior-matched evaluation" method for Earth-observation classifiers, specifically demonstrated on Sentinel-1's internal-wave detection. The method addresses the discrepancy between balanced-test s…
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Machine learning system provides 72-hour flash flood warnings for Bangladesh wetlands
Researchers have developed HaorFloodAlert, a machine learning system designed to provide 72-hour early warnings for flash floods in Bangladesh's Haor wetlands. The system utilizes free data sources including Sentinel-1 …
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New method probes geospatial SSL representations using environmental signals
Researchers have developed a new method to evaluate self-supervised learning (SSL) representations in geospatial satellite imagery. Instead of relying solely on downstream tasks, this approach probes the representations…
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New method probes geospatial SSL representations with environmental signals
Researchers have developed a new method to evaluate self-supervised learning (SSL) representations in geospatial data by probing them with environmental signals. This approach uses co-located ERA5 reanalysis variables, …
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TESSERA v2 study reveals optimal scaling for Earth-observation models
Researchers have conducted a large-scale study on scaling pixel-wise Earth-observation foundation models, involving 395 training runs on 1,024 NVIDIA GH200 superchips. The study found that pretraining loss is a poor pre…
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SibFly launches API for measured ground motion data for US addresses
SibFly has launched an API that provides measured ground motion data for US addresses, accessible via the MCP protocol. This service offers real-time measurements of ground subsidence or uplift in millimeters per year, …
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New AI framework enhances flood mapping with satellite imagery · 2 sources tracked
Researchers have developed a new framework for high-resolution flood mapping using Sentinel-1 and Sentinel-2 satellite imagery. This approach addresses limitations such as cloud cover in optical data and speckle noise i…
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AI model jointly retrieves wheat crop data using satellite imagery
Researchers have developed an Iterative Energy-Based Transformer (iEBT) model to jointly retrieve soil moisture, leaf area index, and plant height for wheat crops using satellite data. This multimodal transformer proces…
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New SAR data corpus enables global monitoring of offshore wind infrastructure
Researchers have developed a new method using Sentinel-1 synthetic aperture radar (SAR) time series data to monitor offshore wind infrastructure globally. This approach provides dense temporal and semantic information o…