Landsat program
PulseAugur coverage of Landsat program — every cluster mentioning Landsat program across labs, papers, and developer communities, ranked by signal.
4 day(s) with sentiment data
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New benchmark dataset HeatCast enables neighborhood-scale LST forecasting
Researchers have introduced HeatCast, a new benchmark dataset for forecasting land surface temperature (LST) at a neighborhood scale across 124 U.S. cities. This dataset, derived from Landsat imagery, provides monthly L…
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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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Google retracts AI image generator from Google Earth amid disinformation fears
Google has introduced and subsequently rolled back an AI feature in Google Earth that allowed users to generate fabricated satellite images based on text prompts. This tool, powered by Google's Nano Banana model, raised…
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New GAN reconstructs satellite land surface temperature data with high accuracy
Researchers have developed a novel Multimodal Fast Fourier Convolutional GAN designed to reconstruct land surface temperature (LST) data from satellite imagery, specifically addressing gaps caused by cloud cover. This m…
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AI predicts poverty using satellite images and LLM-generated text
Researchers have developed a multimodal framework to predict household wealth in African neighborhoods using satellite imagery and text generated by AI. The framework combines vision models with LLM-generated text and w…
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GlacierCastAI uses satellite imagery and climate data to predict glacier retreat
Researchers have developed GlacierCastAI, a novel deep learning model designed to predict glacier retreat using a combination of multi-modal satellite imagery and climate data. The model integrates data from the Landsat…
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HLS-GPT Transformer reconstructs NASA satellite reflectance data
Researchers have developed HLS-GPT, a large-scale generative pretrained Transformer model designed to reconstruct NASA's Harmonized Landsat and Sentinel-2 (HLS) surface reflectance data. This model utilizes a hierarchic…
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Vision transformer maps 38 years of US forest disturbances
Researchers have developed a deep learning framework using a vision transformer to map forest disturbances across the contiguous United States over a 38-year period. This approach simultaneously models temporal trajecto…
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Hybrid ML model improves forest height estimation using satellite data
Researchers have developed a hybrid machine learning model that improves forest height estimation by integrating data from TanDEM-X and Landsat satellites. This enhanced model incorporates optical Landsat data to provid…
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New EO-Gym environment trains AI agents for interactive Earth Observation analysis
Researchers have introduced EO-Gym, an interactive framework designed for Earth Observation (EO) agents. This environment supports multimodal analysis and tool usage, simulating real-world EO tasks that often involve ex…
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Machine learning model maps soil salinity in Bangladesh
Researchers have developed a machine-learning framework to map and predict soil salinity in Satkhira, Bangladesh, using field data and satellite imagery. An Extreme Gradient Boosting model, trained on 205 soil samples, …
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Satellite foundation model Tempov maps wealth and poverty in Africa
Researchers have developed Tempov, a novel satellite foundation model designed to enhance wealth monitoring in low- and middle-income countries. This model, trained using self-supervision on millions of satellite image …
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Explainable ML reveals urban morphology's impact on heat stress beyond LST
Researchers have developed a new framework to analyze the differences between land surface temperature (LST) and human-centric heat stress metrics like the Universal Thermal Climate Index (UTCI). Using machine learning …