Remote Sensing AI

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.

MeCSAFNet: Dual-Branch ConvNeXt for Multispectral Semantic Segmentation.

MeCSAFNet: Dual-Branch ConvNeXt for Multispectral Semantic Segmentation

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MeCSAFNet: Dual-Branch ConvNeXt for Multispectral Semantic Segmentation | AI Trend Blend Remote Sensing AI · Neurocomputing 685 (2026) 133533 · 22 min read Seeing Every Wavelength at Once: How MeCSAFNet Rewires Multispectral Segmentation Researchers at Universitat Autònoma de Barcelona built a dual-branch ConvNeXt network that separates visible and non-visible spectral information, fuses them with CBAM […]

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ViRefSAM: How Visual Reference Images Are Finally Making SAM Work for Remote Sensing.

ViRefSAM: How Visual Reference Images Are Finally Making SAM Work for Remote Sensing

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ViRefSAM: How Visual Reference Images Are Finally Making SAM Work for Remote Sensing | AI Trend Blend Computer Vision · arXiv:2507.02294 · Remote Sensing & Foundation Models · 20 min read ViRefSAM: Teaching SAM to Segment Anything in Satellite Imagery — Without You Drawing a Single Box Researchers from the Chinese Academy of Sciences built

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SSA-Mamba: The Hyperspectral Classifier That Finally Lets Spatial and Spectral Features Talk to Each Other

SSA-Mamba: The Hyperspectral Classifier That Finally Lets Spatial and Spectral Features Talk to Each Other

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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

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GLMamba: How Global-Local Mamba Detects Change in Satellite Images Better Than CNNs and Transformers.

GLMamba: How Global-Local Mamba Detects Change in Satellite Images Better Than CNNs and Transformers

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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

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MD2F-Mamba: How Directional Convolution and Dual-Branch Mamba Crack Hyperspectral Image Classification.

MD2F-Mamba: How Directional Convolution and Dual-Branch Mamba Crack Hyperspectral Image Classification

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MD2F-Mamba: How Directional Convolution and Dual-Branch Mamba Crack Hyperspectral Image Classification | AI Trend Blend 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 Image Xiaoqing Wan and colleagues at

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Through the Perspective of LiDAR: Uncertainty-Aware Annotation Pipeline for TLS Point Cloud Segmentation.

Through the Perspective of LiDAR: Uncertainty-Aware Annotation Pipeline for TLS Point Cloud Segmentation

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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:

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FGI-EMIT: The First Multispectral LiDAR Benchmark That Finally Takes Understory Trees Seriously.

FGI-EMIT: The First Multispectral LiDAR Benchmark That Finally Takes Understory Trees Seriously

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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

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CRGenNet: Cloud-Free Optical Image Generation Using SAR and Contaminated Optical Data.

CRGenNet: Cloud-Free Optical Image Generation Using SAR and Contaminated Optical Data

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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.

GateMamba: Feature Gated Mixer in State Space Model for Point Cloud 3D Object Detection

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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

The Moon’s Many Faces: A Single Unified Transformer for Multimodal Lunar Reconstruction

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The Moon’s Many Faces: A Single Unified Transformer for Multimodal Lunar Reconstruction | AI Trend Blend 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 Four Languages of Lunar Science Simultaneously

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