mlsanityData to 7 Oct 2026

CD-Mamba: Cloud detection with long-range spatial dependency modeling

arXiv 2509.04729 ↗First posted 5 Sep 2025cs.CVcited by 1

On the map: State-space sequence models (S4, Mamba) → Point clouds, detection and remote sensing

Abstract (arXiv)

Remote sensing images are frequently obscured by cloud cover, posing significant challenges to data integrity and reliability. Effective cloud detection requires addressing both short-range spatial redundancies and long-range atmospheric similarities among cloud patches. Convolutional neural networks are effective at capturing local spatial dependencies, while Mamba has strong capabilities in modeling long-range dependencies. To fully leverage both local spatial relations and long-range dependencies, we propose CD-Mamba, a hybrid model that integrates convolution and Mamba's state-space modeling into a unified cloud detection network. CD-Mamba is designed to comprehensively capture pixelwise textural details and long term patchwise dependencies for cloud detection. This design enables CD-Mamba to manage both pixel-wise interactions and extensive patch-wise dependencies simultaneously, improving detection accuracy across diverse spatial scales. Extensive experiments validate the effectiveness of CD-Mamba and demonstrate its superior performance over existing methods.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Point clouds, detection and remote sensing, one of 10 directions in State-space sequence models (S4, Mamba). Builds on 25 of its 44 references that are in the map. First posted on arXiv 5 Sep 2025.

Builds on

25 of 44 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024RS-Mamba for Large Remote Sensing Image Dense Prediction3 Apr

Other references · 24

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  5. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,616
  6. 2016Gaussian Error Linear Units (GELUs)cited by 3,431
  7. 2018CBAM: Convolutional Block Attention Modulecited by 2,463
  8. 2021Efficiently Modeling Long Sequences with Structured State Spacescited by 2,424
  9. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
  10. 2018Group Normalizationcited by 1,514
  11. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,218
  12. 2024Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Modelcited by 1,051
12 more
  1. 2024VMamba: Visual State Space Modelcited by 955
  2. 2024VM-UNet: Vision Mamba UNet for Medical Image Segmentationcited by 286
  3. 2024Swin-UMamba: Mamba-based UNet with ImageNet-based pretrainingcited by 179
  4. 2024MambaOut: Do We Really Need Mamba for Vision?cited by 142
  5. 2022MALUNet: A Multi-Attention and Light-weight UNet for Skin Lesion Segmentationcited by 59
  6. 2024Vision Mamba: A Comprehensive Survey and Taxonomycited by 53
  7. 2024Vivim: a Video Vision Mamba for Medical Video Segmentationcited by 51
  8. 2024UltraLight VM-UNet: Parallel Vision Mamba Significantly Reduces Parameters for Skin Lesion Segmentationcited by 45
  9. 2022Attention Swin U-Net: Cross-Contextual Attention Mechanism for Skin Lesion Segmentationcited by 21
  10. 2023DA-TransUNet: Integrating Spatial and Channel Dual Attention with Transformer U-Net for Medical Image Segmentationcited by 16
  11. 2024High-Resolution Cloud Detection Networkcited by 8
  12. 2025BS-Mamba for Black-Soil Area Detection On the Qinghai-Tibetan Plateaucited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026CloudMatch: Weak-to-Strong Consistency Learning for Semi-Supervised Cloud Detectioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026CloudMamba: An Uncertainty-Guided Dual-Scale Mamba Network for Cloud Detection in Remote Sensing Imagery
  2. 2025Vision Mamba in Remote Sensing: A Comprehensive Survey of Techniques, Applications and Outlook
  3. 2025CloudMamba: Grouped Selective State Spaces for Point Cloud Analysis
  4. 2024CDMamba: Incorporating Local Clues into Mamba for Remote Sensing Image Binary Change Detection
  5. 2024Exploring contextual modeling with linear complexity for point cloud segmentation
  6. 2025Spectral Informed Mamba for Robust Point Cloud Processing
  7. 2024Point Cloud Mamba: Point Cloud Learning via State Space Model
  8. 2024ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Model