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.
Fei Zhang and colleagues at RIT built a semi-automated pipeline that projects millions of mangrove LiDAR points onto a 2D spherical canvas, trains an ensemble of segmentation networks, harvests uncertainty maps to guide human annotators, and then back-projects everything into…
Lassi Ruoppa and colleagues at the Finnish Geospatial Research Institute introduce FGI-EMIT: 1,561 manually annotated boreal trees across three wavelengths, with a deliberate emphasis on the small, occluded understory trees that have defeated every benchmark before it. ForestFormer3D wins at…
Researchers at the University of Twente built an image generation network that does what every prior method refused to do — accept that your helper image might also be covered in clouds. CRGenNet fuses Sentinel-1 SAR radar data with contaminated…
CRGenNet: Cloud-Free Optical Image Generation Using SAR and Contaminated Optical Data Read More »
Mamba-based 3D detectors achieve impressive overall numbers but consistently under-perform on small and distant targets — the problem is architectural: unidirectional scanning and crude downsampling let weak foreground signals drown in background noise. Researchers at NUDT and Sun Yat-sen University…
GateMamba: Feature Gated Mixer in State Space Model for Point Cloud 3D Object Detection Read More »
A team at TU Dortmund built a single 29.7-million-parameter foundation model that can translate any combination of lunar data — grayscale images, elevation maps, surface normals, and albedo — to any other, all in a single forward pass. The key…
The Moon’s Many Faces: A Single Unified Transformer for Multimodal Lunar Reconstruction Read More »
Researchers at Italy’s CNR-IREA fused decades-old minimum cost flow theory with modern compressive sensing to build a phase unwrapping algorithm that detected a 40 cm volcanic displacement at Stromboli that the classic EMCF method simply could not recover — and…
CS-EMCF: Compressive Sensing Phase Unwrapping for SAR Interferometry Read More »
Researchers at Jiangsu University asked a simple question: what if we treated cross-modal feature fusion the same way electrical engineers treat differential amplifiers? The answer — IRDFusion — hits 88.3 mAP50 on FLIR and works as a plug-and-play module inside…
IRDFusion: Iterative Differential Feedback for Multispectral Object Detection Read More »
Researchers at Northwestern Polytechnical University and the University of Girona built a physically motivated knowledge distillation framework that corrects motion-induced geometric distortions in side-scan sonar images from a single distorted input — no GPS, no inertial sensors, no navigation data…
PCKD: Physically Motivated Knowledge Distillation for Blind Side-Scan Sonar Correction Read More »
Researchers at the Chinese Academy of Sciences identified a deceptively simple flaw in every DIP-based hyperspectral fusion method published before them — the random noise input — and replaced it with two scene-aware coarse estimations derived entirely from the observations…
EDIP-Net: Enhanced Deep Image Prior for Unsupervised Hyperspectral Super-Resolution Read More »
Land cover mapping from aerial and satellite imagery has always been treated as a deterministic task by default. A convolutional network takes in a patch, produces a class distribution for every pixel, and outputs one hard prediction map. What it…
A bayesian Segmentation Model That Flags Its Own Uncertain Pixels Read More »