Machine learning for satellite and aerial imagery: hyperspectral and multispectral fusion, change detection, and multi-sensor pipelines. Coverage focuses on what makes these methods reliable when the sensors disagree and the scenes change.
RASD-FuNet routes each feature point to a low-rank or attention branch based on a learned confidence score, topping hyperspectral fusion benchmarks at a fraction of the FLOPs.
When you only have a handful of real sinkhole examples, you either accept poor detection or get creative about generating training data. Researchers at Arts et Métiers ParisTech and SNCF Réseau chose the latter — building physics-informed synthetic sinkholes, embedding…
Every night, weather satellites go partially blind — their visible cameras shut off the moment the sun dips below the horizon. Researchers at Ocean University of China built a reparameterized GAN that reads three thermal infrared channels and reconstructs what…
RepVIS-GAN: Nighttime Satellite Visible Image Retrieval from Infrared Data Read More »
Researchers at Wuhan University built a distillation framework that transfers SAM’s powerful visual priors to a compact student network using only unlabeled satellite imagery — no source data, no labels — and deployed the result live on an edge AI…
Shan Zhao, Ioannis Prapas, and their colleagues at TU Munich and the National Observatory of Athens built a causally informed graph neural network that forecasts burned areas with 64% lower prediction variance and outperforms correlation-based baselines by 2–5 AUROC points…
Causal Graph Neural Networks for Wildfire Forecasting Across Geographic Shifts Read More »
Researchers at Universitat Autònoma de Barcelona built a dual-branch ConvNeXt network that separates visible and non-visible spectral information, fuses them with CBAM attention, and maps land cover at accuracy levels that leave U-Net, SegFormer, and DeepLabV3+ behind — without needing…
MeCSAFNet: Dual-Branch ConvNeXt for Multispectral Semantic Segmentation Read More »
Researchers from the Chinese Academy of Sciences built a framework that feeds a handful of annotated reference images into SAM’s pipeline, eliminating the need to manually prompt each aerial scene — and in the process, achieved state-of-the-art few-shot segmentation across…
ViRefSAM: How Visual Reference Images Are Finally Making SAM Work for Remote Sensing Read More »
Researchers at Guangzhou Maritime University diagnosed a fundamental flaw in every existing hyperspectral classification model — spatial and spectral features are either forced to share the same representation space, or kept so separate they never productively interact. SSA-Mamba fixes both…
Shengyan Liu and Min Xia at NUIST introduce GLMamba: a Siamese Mamba network that pairs global state-space modeling with local convolutional detail extraction for remote sensing change detection. On LEVIR-CD it posts F1=91.27% and IoU=83.94%, outperforming ChangeMamba, ChangeFormer, and nine…
Xiaoqing Wan and colleagues at Hengyang Normal University introduce MD2F-Mamba: a dual-branch architecture pairing multidirectional depthwise convolution with a hierarchical state-space Mamba for hyperspectral classification. With just 92K parameters and 8.3M FLOPs, it posts 99.81% overall accuracy on Pavia University…