Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images | Paper Citation Record

Observation ce3642d4-f566-4f5a-8451-1a5feeced01a · outbound

This paper cites Stability of cloud detection methods for land surface temperature (lst) climate data records (cdrs),.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Stability of cloud detection methods for land surface temperature (lst) climate data records (cdrs),

Reference 1

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This paper cites Accurate recon- struction of satellite-derived sst under cloud and cloud-free areas using a physically-informed machine learning approach,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Accurate recon- struction of satellite-derived sst under cloud and cloud-free areas using a physically-informed machine learning approach,

Reference 2

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This paper cites Automated cloud, cloud shadow, and snow detection in multitemporal landsat data: An algorithm designed specifically for monitoring land cover change,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Automated cloud, cloud shadow, and snow detection in multitemporal landsat data: An algorithm designed specifically for monitoring land cover change,

Reference 3

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Observation ea082311-7fa6-4eee-89ab-d49ce9cfa00b · outbound

This paper cites An image transform to characterize and compensate for spatial variations in thin cloud contamination of landsat images,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images An image transform to characterize and compensate for spatial variations in thin cloud contamination of landsat images,

Reference 4

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Observation 9d8bcb70-a6de-4553-bee0-9344f156e1a4 · outbound

This paper cites An iterative haze optimized transformation for automatic cloud/haze detection of land- sat imagery,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images An iterative haze optimized transformation for automatic cloud/haze detection of land- sat imagery,

Reference 5

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Observation 8e30d3ec-cf14-40e7-ae0f-3e9eb63c9f50 · outbound

This paper cites Improvement and expansion 12 of the fmask algorithm: cloud, cloud shadow, and snow detection for landsats 4–7, 8, and sentinel 2 images,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Improvement and expansion 12 of the fmask algorithm: cloud, cloud shadow, and snow detection for landsats 4–7, 8, and sentinel 2 images,

Reference 6

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Observation e47a721b-ed08-4b33-a583-7a425834e39f · outbound

This paper cites Deep learning,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Deep learning,

Reference 8

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This paper cites Gradient-based learning applied to document recognition,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Gradient-based learning applied to document recognition,

Reference 9

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This paper cites Imagenet classification with deep convolutional neural networks,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Imagenet classification with deep convolutional neural networks,

Reference 10

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Observation 94b0682d-543f-49b8-949e-c785c9ddb454 · outbound

This paper cites Going deeper with convolutions,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Going deeper with convolutions,

Reference 11

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Observation 9ef6bf2e-bb65-450b-a5c1-4840b643c55f · outbound

This paper cites Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors,

Reference 12

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Observation ec0f3975-e771-482f-9a4e-c4fca531b04b · outbound

This paper cites A cloud detection algorithm for satellite imagery based on deep learning,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images A cloud detection algorithm for satellite imagery based on deep learning,

Reference 13

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Observation 66960337-0ea6-4711-9386-35f5e696badc · outbound

This paper cites Cloud detection in remote sensing images based on multiscale features-convolutional neural network,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Cloud detection in remote sensing images based on multiscale features-convolutional neural network,

Reference 14

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Observation 57fe0d58-e5ff-4cfb-b0d6-083ea31a04ea · outbound

This paper cites Cloud detection of remote sensing images by deep learning,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Cloud detection of remote sensing images by deep learning,

Reference 15

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Observation 1e746808-bb79-42b7-ac95-bd7123b6a50c · outbound

This paper cites Cdnet: Cnn- based cloud detection for remote sensing imagery,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Cdnet: Cnn- based cloud detection for remote sensing imagery,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:22.948718Z

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Observation 2d45f3f2-6148-4193-8359-3cdade3adc47 · outbound

This paper cites Cdnetv2: Cnn- based cloud detection for remote sensing imagery with cloud-snow coexistence,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Cdnetv2: Cnn- based cloud detection for remote sensing imagery with cloud-snow coexistence,

Reference 17

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Observation 348612f6-cc4c-4d60-83ad-b1e5d013713d · outbound

This paper cites Dabnet: Deformable contextual and boundary-weighted network for cloud detection in remote sensing images,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Dabnet: Deformable contextual and boundary-weighted network for cloud detection in remote sensing images,

Reference 18

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Observation 040b082a-84c5-4e4e-8a4f-5fad42109007 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 19

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Observation 2a9d8120-84b7-47dc-a025-46d6716e32a6 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 20

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This paper cites Masked autoencoders are scalable vision learners,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Masked autoencoders are scalable vision learners,

Reference 21

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This paper cites Segment anything,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Segment anything,

Reference 22

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Observation 7f31513c-c3b7-49ea-9de8-b3dd82144cae · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images DINOv2: Learning Robust Visual Features without Supervision,

Reference 23

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Observation d007cb02-8680-46f9-99a7-8d23f9eb1195 · outbound

This paper cites Imagenet: A large-scale hierarchical image database,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Imagenet: A large-scale hierarchical image database,

Reference 24

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Observation 07175212-4e8d-4eef-a79b-bf3f8a49f172 · outbound

This paper cites Crossvit: Cross-attention multi- scale vision transformer for image classification,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Crossvit: Cross-attention multi- scale vision transformer for image classification,

Reference 25

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Observation 44fa3ccc-f875-466f-a5bc-9f89e0235311 · outbound

This paper cites Sam-cod: Sam-guided unified framework for weakly-supervised camouflaged object detection,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Sam-cod: Sam-guided unified framework for weakly-supervised camouflaged object detection,

Reference 26

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Observation d9cfe55b-6d6a-4aca-9505-3a68c79e6504 · outbound

This paper cites Swin transformer embedding unet for remote sensing image semantic segmen- tation,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Swin transformer embedding unet for remote sensing image semantic segmen- tation,

Reference 27

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Observation af2e706d-fc2b-4ece-a13d-48a74cbe5ad1 · outbound

This paper cites Mask dino: Towards a unified transformer-based framework for object detection and segmentation,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Mask dino: Towards a unified transformer-based framework for object detection and segmentation,

Reference 28

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Observation d12eeb56-92a0-455e-b375-b360621472d8 · outbound

This paper cites Sem- mae: Semantic-guided masking for learning masked autoencoders,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Sem- mae: Semantic-guided masking for learning masked autoencoders,

Reference 29

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This paper cites Mcdnet: Multilevel cloud detection network for remote sensing images based on dual- perspective change-guided and multi-scale feature fusion,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Mcdnet: Multilevel cloud detection network for remote sensing images based on dual- perspective change-guided and multi-scale feature fusion,

Reference 30

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Observation f6462aa0-64c3-45ee-b1cf-3fd72e54ba77 · outbound

This paper cites Dual-branch network for cloud and cloud shadow segmentation,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Dual-branch network for cloud and cloud shadow segmentation,

Reference 31

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Observation 0d119149-79d1-4aba-89c5-a248a16003fd · outbound

This paper cites Remote sensing image cloud detection using a shallow convolutional neural network,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Remote sensing image cloud detection using a shallow convolutional neural network,

Reference 32

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Observation 3f400632-0ec7-4ff0-b06b-a39b1bd68782 · outbound

This paper cites Kappamask: Ai-based cloudmask processor for sentinel-2,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Kappamask: Ai-based cloudmask processor for sentinel-2,

Reference 33

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Observation 9ed52af3-395d-4420-9f85-6fb88f78b381 · outbound

This paper cites High- resolution cloud detection network,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images High- resolution cloud detection network,

Reference 34

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Observation 7320d147-ffbb-430f-ae6f-2e7aed6d3736 · outbound

This paper cites Transferring deep models for cloud detection in multisensor images via weakly supervised learning,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Transferring deep models for cloud detection in multisensor images via weakly supervised learning,

Reference 35

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This paper cites U-net: Convolutional networks for biomedical image segmentation,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images U-net: Convolutional networks for biomedical image segmentation,

Reference 36

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This paper cites Deep residual learning for image recognition,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Deep residual learning for image recognition,

Reference 37

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This paper cites Feature pyramid networks for object detection,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Feature pyramid networks for object detection,

Reference 38

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Observation 4a96696f-8947-4a7b-bb45-e386f6ca46b7 · outbound

This paper cites Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,

Reference 39

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This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Bert: Pre-training of deep bidirectional transformers for language understanding,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:22.591180Z

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Observation dcac7ce3-a752-4b4f-b2f8-1cf602530620 · outbound

This paper cites Language models are few-shot learners,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Language models are few-shot learners,

Reference 41

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Observation 1344a90c-b34a-43a6-99df-4f47287da494 · outbound

This paper cites Learning transferable visual models from natural language supervision,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Learning transferable visual models from natural language supervision,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:22.552861Z

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This paper cites A survey on transfer learning,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images A survey on transfer learning,

Reference 43

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This paper cites Unsupervised domain adaptation by backpropagation,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Unsupervised domain adaptation by backpropagation,

Reference 44

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This paper cites Parameter-efficient transfer learning for nlp,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Parameter-efficient transfer learning for nlp,

Reference 45

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Observation 07e5fde2-daba-48ce-85cb-5a57f9fdca7d · outbound

This paper cites LoRA: Low-rank adaptation of large language models,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images LoRA: Low-rank adaptation of large language models,

Reference 46

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Observation 51bfcd4a-ef38-4fbf-9224-13dc19fea63d · outbound

This paper cites Ip-adapter: Text compatible image prompt adapter for text-to-image diffusion models,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Ip-adapter: Text compatible image prompt adapter for text-to-image diffusion models,

Reference 47

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This paper cites T2i- adapter: Learning adapters to dig out more controllable ability for text- to-image diffusion models,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images T2i- adapter: Learning adapters to dig out more controllable ability for text- to-image diffusion models,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:22.439262Z

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This paper cites Vision transformer adapter for dense predictions,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Vision transformer adapter for dense predictions,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:22.415481Z

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Observation 363aa0c3-1c7f-46a9-a9e2-09d50567cd35 · outbound

This paper cites Sam-adapter: Adapting segment anything in underperformed scenes,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Sam-adapter: Adapting segment anything in underperformed scenes,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:22.391253Z

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Observation d10e8a9b-c1de-478f-a0e4-e395b8721df0 · outbound

This paper cites Xception: Deep learning with depthwise separable convo- lutions,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Xception: Deep learning with depthwise separable convo- lutions,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:22.370228Z

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This paper cites Cat: Cross attention in vision transformer,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Cat: Cross attention in vision transformer,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:22.345404Z

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Observation a818562a-c3bc-4b41-8864-72e16bf86685 · outbound

This paper cites Ccnet: Criss-cross attention for semantic segmentation,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Ccnet: Criss-cross attention for semantic segmentation,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:22.319744Z

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This paper cites U-net transformer: Self and cross attention for medical image segmen- tation,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images U-net transformer: Self and cross attention for medical image segmen- tation,

Reference 54

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This paper cites PMAA: A progressive multi- scale attention autoencoder model for high-performance cloud removal from multi-temporal satellite imagery,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images PMAA: A progressive multi- scale attention autoencoder model for high-performance cloud removal from multi-temporal satellite imagery,

Reference 55

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This paper cites Cloudsen12, a global dataset for semantic understanding of cloud and cloud shadow in sentinel-2,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Cloudsen12, a global dataset for semantic understanding of cloud and cloud shadow in sentinel-2,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:22.238208Z

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This paper cites Cloud detection algorithm comparison and validation for operational landsat data products,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Cloud detection algorithm comparison and validation for operational landsat data products,

Reference 57

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This paper cites Decoupled weight decay regularization,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Decoupled weight decay regularization,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:22.211367Z

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This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,

Reference 59

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This paper cites Masked-attention mask transformer for universal image segmentation,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Masked-attention mask transformer for universal image segmentation,

Reference 60

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This paper cites A convnet for the 2020s,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images A convnet for the 2020s,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:22.140608Z

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This paper cites Rsam-seg: A sam-based approach with prior knowledge integration for remote sensing image semantic segmentation,.

Adapting Vision Foundation Models for Robust Cloud Segmentation in Remote Sensing Images Rsam-seg: A sam-based approach with prior knowledge integration for remote sensing image semantic segmentation,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:52:22.113544Z

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