MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting | Paper Citation Record

Observation 4932c4dd-36d5-42e8-9cc1-d7341c991843 · outbound

This paper cites Machine learning for fog-and-low-stratus nowcasting from meteosat seviri satellite images.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting Machine learning for fog-and-low-stratus nowcasting from meteosat seviri satellite images

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.702891Z

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Observation de32c73f-c56e-4574-b9c3-b2741294756b · outbound

This paper cites Sea fog monitoring method based on deep learning satellite multi-channel image fusion (in chi- nese).

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting Sea fog monitoring method based on deep learning satellite multi-channel image fusion (in chi- nese)

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.688220Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0664b592-6285-4249-8190-61aef4de3da4 · outbound

This paper cites Encoder-decoder with atrous separable convolution for semantic image segmentation.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting Encoder-decoder with atrous separable convolution for semantic image segmentation

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.673083Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation efc35f41-f065-401e-ae12-3130a90f16c6 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T21:15:58.852371Z

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Observation 4578e062-30bc-4373-9044-5cbb62350258 · outbound

This paper cites A climatology of arctic fog along the coast of east greenland.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting A climatology of arctic fog along the coast of east greenland

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.659447Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d79e9e37-e279-44a5-9b38-a79d70788fed · outbound

This paper cites Disentangling physi- cal dynamics from unknown factors for unsupervised video prediction.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting Disentangling physi- cal dynamics from unknown factors for unsupervised video prediction

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.644802Z

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Observation 0c2aeef5-670f-4823-967a-19a135add55f · outbound

This paper cites A scse-linknet deep learning model for daytime sea fog detection.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting A scse-linknet deep learning model for daytime sea fog detection

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.630094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 40b4a01d-1177-4b38-a69f-46478ef5dfa3 · outbound

This paper cites UNetFormer: A Unified Vision Transformer Model and Pre-Training Framework for 3D Medical Image Segmentation.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting UNetFormer: A Unified Vision Transformer Model and Pre-Training Framework for 3D Medical Image Segmentation

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-15T21:15:59.087419Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5b2d3edf-d10e-42e8-bde2-b8d3d691394d · outbound

This paper cites Algorithm for sea fog monitoring with the use of in- formation technologies.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting Algorithm for sea fog monitoring with the use of in- formation technologies

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.615230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bf2c3d8d-1f45-4634-b8bf-7eb0e3437906 · outbound

This paper cites Learning to decompose and dis- entangle representations for video prediction.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting Learning to decompose and dis- entangle representations for video prediction

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.599715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6bc68f14-b13c-4d99-beeb-a3be62d55efa · outbound

This paper cites Monitoring sea fog over the yellow sea and bohai bay based on deep convolutional neural network.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting Monitoring sea fog over the yellow sea and bohai bay based on deep convolutional neural network

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.584201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4847f468-d8dc-4572-ac36-4083b3db30a8 · outbound

This paper cites Weakly supervised sea fog detection in remote sensing images via prototype learn- ing.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting Weakly supervised sea fog detection in remote sensing images via prototype learn- ing

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.570465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4c02aa4d-9e8e-40c3-9d11-13fb9b0c2962 · outbound

This paper cites Mesogeos: A multi-purpose dataset for data-driven wildfire modeling in the mediterranean.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting Mesogeos: A multi-purpose dataset for data-driven wildfire modeling in the mediterranean

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.554461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2d7b46ab-de2e-4cf5-ae18-08bfab524ffe · outbound

This paper cites Marine fog: challenges and advancements in observations, modeling, and forecast- ing.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting Marine fog: challenges and advancements in observations, modeling, and forecast- ing

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.538913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c728dd14-08f1-4098-98d1-3668f5d82c2b · outbound

This paper cites Marine fog: A review.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting Marine fog: A review

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.524188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f6e06820-92c4-43bb-a1fb-9d0704326b84 · outbound

This paper cites Uniformer: Unifying convolution and self-attention for visual recogni- tion.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting Uniformer: Unifying convolution and self-attention for visual recogni- tion

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.509364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5c279022-8d1a-479e-9b5f-370642afdae9 · outbound

This paper cites Abcnet: Attentive bilateral con- textual network for efficient semantic segmentation of fine- resolution remotely sensed imagery.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting Abcnet: Attentive bilateral con- textual network for efficient semantic segmentation of fine- resolution remotely sensed imagery

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.495024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8fed984a-1735-4e5f-a1d9-d6437f0a2da7 · outbound

This paper cites Daytime sea fog monitoring using multimodal self-supervised learning with band attention mechanism.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting Daytime sea fog monitoring using multimodal self-supervised learning with band attention mechanism

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.481583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 26e0db60-6a38-4469-b897-180bed869f96 · outbound

This paper cites Daytime sea fog identification based on multi-satellite infor- mation and the eca-transunet model.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting Daytime sea fog identification based on multi-satellite infor- mation and the eca-transunet model

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.467593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d6446888-7713-4112-81d3-59fc7a9c078f · outbound

This paper cites A probability-based daytime algorithm for sea fog detection using goes-16 imagery.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting A probability-based daytime algorithm for sea fog detection using goes-16 imagery

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.452749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5f732f8f-2f2e-4988-8053-9f767461646c · outbound

This paper cites Enhanced oceanic fog nowcasting through satellite- based recurrent neural networks.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting Enhanced oceanic fog nowcasting through satellite- based recurrent neural networks

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.437904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 82a4309c-2c3c-40b3-a75f-d09caa1b03b4 · outbound

This paper cites Sea fog detection based on unsuper- vised domain adaptation.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting Sea fog detection based on unsuper- vised domain adaptation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.422295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b40ca6bf-f2d9-4882-bd37-d412e3af51db · outbound

This paper cites Fog detection based on me- teosat second generation-spinning enhanced visible and in- frared imager high resolution visible channel.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting Fog detection based on me- teosat second generation-spinning enhanced visible and in- frared imager high resolution visible channel

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.407606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2a728a43-0f38-411a-9bd1-b87e8066571a · outbound

This paper cites Spatio-temporal network for sea fog fore- casting.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting Spatio-temporal network for sea fog fore- casting

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.392890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8e679f9c-daff-4312-98bb-c82e6f3db7ca · outbound

This paper cites an unresolved cited work.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-15T21:15:59.378869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4f1290c1-51dc-4b36-9fd4-b6f25c8d3dcc · outbound

This paper cites U- net: Convolutional networks for biomedical image segmen- tation.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting U- net: Convolutional networks for biomedical image segmen- tation

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.365071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6ac0068d-ce44-4a07-a14c-c6d28f92f076 · outbound

This paper cites Convolutional lstm network: A machine learning approach for precipitation nowcasting.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting Convolutional lstm network: A machine learning approach for precipitation nowcasting

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.348410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e3afc5f8-fbb1-4a77-ace4-c20666479275 · outbound

This paper cites E2sam: A pipeline for efficiently extending sam’s capability on cross-modality data via knowledge in- heritance.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting E2sam: A pipeline for efficiently extending sam’s capability on cross-modality data via knowledge in- heritance

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.332880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 42dad57b-67d1-4975-bb7d-7da2bf0f5278 · outbound

This paper cites SimVPv2: Towards Simple yet Powerful Spatiotemporal Predictive Learning.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting SimVPv2: Towards Simple yet Powerful Spatiotemporal Predictive Learning

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T21:15:58.967248Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83c9ed6a-9c27-46c5-a2c5-14005aec6fad · outbound

This paper cites Temporal attention unit: To- wards efficient spatiotemporal predictive learning.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting Temporal attention unit: To- wards efficient spatiotemporal predictive learning

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.316934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 19c3ee27-8049-423a-bcae-286ca62890f0 · outbound

This paper cites Open- stl: A comprehensive benchmark of spatio-temporal predic- tive learning.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting Open- stl: A comprehensive benchmark of spatio-temporal predic- tive learning

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.301048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e6b19a9a-a457-4617-877c-c422b2912b93 · outbound

This paper cites Sea fog detec- tion based on dynamic threshold algorithm at dawn and dusk time.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting Sea fog detec- tion based on dynamic threshold algorithm at dawn and dusk time

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.284742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 42ebd0d8-4acb-4dd4-b8a4-82c80fd1ff3e · outbound

This paper cites Transformer meets convolution: A bilateral awareness network for semantic segmentation of very fine resolution urban scene images.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting Transformer meets convolution: A bilateral awareness network for semantic segmentation of very fine resolution urban scene images

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.270222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6b09f4d9-5173-479e-bd81-9948e7cda94d · outbound

This paper cites Predrnn: Recurrent neural networks for predictive learning using spatiotemporal lstms.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting Predrnn: Recurrent neural networks for predictive learning using spatiotemporal lstms

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.254736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 93467010-3f22-44ac-83d7-4bdaa77ad730 · outbound

This paper cites Memory in memory: A predictive neural network for learning higher-order non- stationarity from spatiotemporal dynamics.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting Memory in memory: A predictive neural network for learning higher-order non- stationarity from spatiotemporal dynamics

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.239117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 313e1172-03d9-440e-a011-1edae433573e · outbound

This paper cites Automatic de- tection of daytime sea fog based on supervised classification techniques for fy-3d satellite.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting Automatic de- tection of daytime sea fog based on supervised classification techniques for fy-3d satellite

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.224316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6a87e43b-ea16-4967-8a19-1c5ae1fe5bdc · outbound

This paper cites Image quality assessment: from error visibility to structural similarity.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting Image quality assessment: from error visibility to structural similarity

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T21:15:59.003801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43e7f8ed-252e-48c2-ba96-fdd0d11503af · outbound

This paper cites Deep spatial–spectral difference net- work with heterogeneous feature mutual learning for sea fog detection.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting Deep spatial–spectral difference net- work with heterogeneous feature mutual learning for sea fog detection

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.198406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 163455aa-667e-43e1-82e0-2d608033c110 · outbound

This paper cites Seamae: Masked pre-training with meteorological satellite imagery for sea fog detection.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting Seamae: Masked pre-training with meteorological satellite imagery for sea fog detection

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.181909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e99b6cb5-a86e-48fe-b391-e08b68d22744 · outbound

This paper cites Arctic fog detection using infrared spectral measurements.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting Arctic fog detection using infrared spectral measurements

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.166620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7ef2d6a3-b2e8-44b5-ba40-160be8fd3433 · outbound

This paper cites Physical processes in sea fog formation and characteristics of turbulent air-sea fluxes at socheongcho ocean research station in the yellow sea.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting Physical processes in sea fog formation and characteristics of turbulent air-sea fluxes at socheongcho ocean research station in the yellow sea

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.152758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ceaec215-0f97-4fed-a9e2-72ffcec902b2 · outbound

This paper cites Dual-branch neu- ral network for sea fog detection in geostationary ocean color imager.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting Dual-branch neu- ral network for sea fog detection in geostationary ocean color imager

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.136090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f15bac66-5ade-41db-805b-2128c283a48f · outbound

This paper cites Unet++: Redesigning skip connections to exploit multiscale features in image segmen- tation.

MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting Unet++: Redesigning skip connections to exploit multiscale features in image segmen- tation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:15:59.120951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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