Explore how artificial intelligence teaches machines to interpret and understand the visual world 👁️. Discover the latest breakthroughs in image recognition, 3D generation, and visual data analysis.
SSA-Mamba: The Hyperspectral Classifier That Finally Lets Spatial and Spectral Features Talk to Each Other | AI Trend Blend AITrendBlend Machine Learning Computer Vision About Remote Sensing AI · IEEE JSTARS, Vol. 19, 2026 · DOI: 10.1109/JSTARS.2026.3654346 · 22 min read SSA-Mamba: The Hyperspectral Classifier That Finally Lets Spatial and Spectral Features Talk to Each […]
GLMamba: How Global-Local Mamba Detects Change in Satellite Images Better Than CNNs and Transformers | AI Trend Blend AITrendBlend Machine Learning Computer Vision About Remote Sensing & Change Detection · IEEE JSTARS Vol. 19 (2026) · NUIST / Nanjing Forestry University · 27 min read Two Satellite Images, Five Years Apart — How GLMamba Spots
MD2F-Mamba: How Directional Convolution and Dual-Branch Mamba Crack Hyperspectral Image Classification | AI Trend Blend AITrendBlend Machine Learning Computer Vision About Remote Sensing & Hyperspectral AI · IEEE JSTARS Vol. 19 (2026) · Hengyang Normal University · 28 min read 92,000 Parameters That Beat Everything — How MD2F-Mamba Reads the Full Spectrum of a Satellite
Mamba-3: Three Simple Ideas That Finally Fix What Transformers Get Wrong at Inference | AI Trend Blend AITrendBlend Machine Learning NLP & LLMs About Efficient AI · arXiv:2603.15569 · CMU & Princeton · March 2026 · 22 min read Mamba-3: Three Simple Ideas That Finally Fix What Transformers Get Wrong at Inference Time Researchers at
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Through the Perspective of LiDAR: Uncertainty-Aware Annotation Pipeline for TLS Point Cloud Segmentation | AI Trend Blend AITrendBlend Machine Learning Computer Vision About 3D Vision & Forest AI · ISPRS J. Photogramm. Remote Sens. 236 (2026) 141–161 · Rochester Institute of Technology / US Forest Service · 24 min read Seeing the Forest Through LiDAR:
FGI-EMIT: The First Multispectral LiDAR Benchmark That Finally Takes Understory Trees Seriously | AI Trend Blend AITrendBlend Machine Learning Computer Vision About 3D Forest AI & Remote Sensing · ISPRS J. Photogramm. Remote Sens. 236 (2026) 569–605 · FGI / Aalto University · 30 min read The Forest Floor’s Hidden Trees — How a New
CRGenNet: Cloud-Free Optical Image Generation Using SAR and Contaminated Optical Data | AI Trend Blend AITrendBlend Machine Learning Computer Vision About Remote Sensing AI · ISPRS Journal of Photogrammetry and Remote Sensing 236 (2026) 255–272 · 22 min read CRGenNet: How Satellites Can See Through Clouds by Never Assuming the Sky Is Clear Researchers at
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GateMamba: Feature Gated Mixer in State Space Model for Point Cloud 3D Object Detection | AI Trend Blend AITrendBlend Machine Learning Computer Vision About Autonomous Driving AI · ISPRS Journal of Photogrammetry and Remote Sensing 236 (2026) 640–653 · 22 min read GateMamba: How Three Gated Mixers Taught a Mamba Network to Stop Ignoring Cyclists
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The Moon’s Many Faces: A Single Unified Transformer for Multimodal Lunar Reconstruction | AI Trend Blend AITrendBlend Machine Learning Computer Vision About Planetary AI & 3D Reconstruction · ISPRS J. Photogramm. Remote Sens. 236 (2026) 363–379 · TU Dortmund University · 26 min read The Moon’s Many Faces: How One Transformer Learned to Speak All
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CS-EMCF: Compressive Sensing Phase Unwrapping for SAR Interferometry | AI Trend Blend AITrendBlend Machine Learning Computer Vision About Remote Sensing AI · ISPRS Journal of Photogrammetry and Remote Sensing 236 (2026) 120–140 · 22 min read How Compressive Sensing Finally Broke the Phase Unwrapping Bottleneck in SAR Interferometry Researchers at Italy’s CNR-IREA fused decades-old minimum
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