Earth embeddings encode fire, forest clearing, and crop type
Two annual embedding products differ: one detects small recovering fires, while the other separates crops more strongly.
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12 stories, newest first · one card per story
Stories tagged #remote-sensing
DeepMind releases 10-meter global forest-type map ForTy 2020
ForTy labels six land classes and adds a 2017–2025 series showing what replaced natural forest.
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HiPredict: Predicting the missing HiRISE RED4 spectral channel with machine learning
HiPredict’s U-Net reconstructs failed HiRISE RED4 imagery at 1.1% error, beating linear SYN4.
FairRSFM: A Biome-Aware Benchmark and Debiasing Framework for Remote Sensing Foundation Models
FairRSFM finds biome-level accuracy gaps in remote-sensing foundation models that aggregate scores hide.
NASA and IBM's open source lunar model turns 17 years of orbiter data into a foundation for lunar science
NASA and IBM released an open-source lunar foundation model trained on 17 years of orbiter data for ice and crater tasks.
Cropland PAtteRNS: Parallel Dimensional Attention Networks and Attention to Dataset Disparity for Crop Segmentation in Satellite Imagery Time Series Data
PAtteRNS factorizes temporal, spectral, and spatial attention for Sentinel-2 cropland segmentation.
USAI-Quant: A Quantitative Reasoning Benchmark for Vision-Language Models in Built Environments
USAI-Quant finds vision-language models fail numeric reasoning on remote-sensing urban metrics.
GeoCR: Learning a Generalist Cloud Removal Prior from Heterogeneous Observations
GeoCR is a generalist cloud-removal model unifying RGB and multispectral inputs, with optional SAR, across ten datasets.
GeoSET: Generalist Foundation Model for SAR-to-EO Image Translation
GeoSET is a generalist SAR-to-EO translation model pretrained on over three million pairs and adapted with LoRA.
Enhancing Photogrammetric Digital Surface Models with Pretrained Diffusion Models and Multimodal Conditioning
Researchers refine satellite photogrammetric surface models with a modified Stable Diffusion 3 model conditioned on imagery.
Task-Oriented Semantic Feature Transmission for Multi-Task Satellite Remote Sensing over Low-SNR Channels
A task-oriented semantic transmission framework sends remote-sensing features instead of images, improving classification and detection over low-SNR satellite channels.
Real-World Knowledge-Guided Change Data Synthesis for Remote Sensing
Researchers introduce KnowChange, a framework that uses pretrained vision-language models to synthesize realistic change-detection training data for remote sensing.