Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites | Paper Citation Record

Observation 534fdbfd-3f3c-4eaf-94b6-93aafea4543c · outbound

This paper cites A determination of the cloud feedback from climate variations over the past decade,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites A determination of the cloud feedback from climate variations over the past decade,

Reference 1

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Observation e3d59389-353b-4bc4-af3a-65558fb68f15 · outbound

This paper cites Earth’s global energy budget,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites Earth’s global energy budget,

Reference 2

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Observation 32ffcc06-e96a-4fb2-8ac8-b9b8de166e45 · outbound

This paper cites Global distributions of multi-layer and multi- phase clouds and their cloud radiative effects,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites Global distributions of multi-layer and multi- phase clouds and their cloud radiative effects,

Reference 3

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Observation 70c85031-0f68-440f-9144-8c9b1e0659ef · outbound

This paper cites Diurnal cloud cycle biases in climate models,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites Diurnal cloud cycle biases in climate models,

Reference 4

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Observation 35409b85-f3ba-470b-ac69-f235435ff9a5 · outbound

This paper cites Building unified global 3d cloud data from multiple satellites for advancing weather and climate research,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites Building unified global 3d cloud data from multiple satellites for advancing weather and climate research,

Reference 5

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This paper cites an unresolved cited work.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites Unresolved cited work

Reference 6

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Observation 798e830f-2cb7-4e84-8857-4c625963f5b1 · outbound

This paper cites Detection of multi-layer and vertically-extended clouds using a-train sensors,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites Detection of multi-layer and vertically-extended clouds using a-train sensors,

Reference 7

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Observation d82b36d9-2eca-4f63-b51b-66546481650f · outbound

This paper cites Discriminating clear sky from clouds with modis,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites Discriminating clear sky from clouds with modis,

Reference 8

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Observation 7994425f-2d33-4773-b0d1-657f7635d461 · outbound

This paper cites The modis cloud products: Algorithms and examples from terra,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites The modis cloud products: Algorithms and examples from terra,

Reference 9

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Observation 34ba4a2f-97a3-4b0c-8d8b-9ad8fd77af71 · outbound

This paper cites Validation of MODIS cloud mask and multilayer flag using CloudSat-CALIPSO cloud profiles and a cross-reference of their cloud classifications,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites Validation of MODIS cloud mask and multilayer flag using CloudSat-CALIPSO cloud profiles and a cross-reference of their cloud classifications,

Reference 10

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Observation 0f55d7b3-ad26-47e3-8e93-237303e2c58a · outbound

This paper cites A multilayer cloud detection algorithm for the suomi-npp visible infrared imager radiometer suite (viirs),.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites A multilayer cloud detection algorithm for the suomi-npp visible infrared imager radiometer suite (viirs),

Reference 11

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Observation 942883e8-4d61-41a8-bf0c-e977f327fa0c · outbound

This paper cites A Global Multilayer Cloud Identification with POLDER/PARASOL,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites A Global Multilayer Cloud Identification with POLDER/PARASOL,

Reference 12

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Observation 8526bbbd-a84e-4718-9f98-ffb91f3c0d3a · outbound

This paper cites The modis cloud optical and microphysical products: Collection 6 updates and examples from terra and aqua,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites The modis cloud optical and microphysical products: Collection 6 updates and examples from terra and aqua,

Reference 13

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Observation f98b4f0e-f6cf-461e-96af-932173d35d33 · outbound

This paper cites Daytime cloud overlap detection from avhrr and viirs,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites Daytime cloud overlap detection from avhrr and viirs,

Reference 14

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Observation a2e125f1-5b47-4557-be19-4513aacd64d3 · outbound

This paper cites Multilayer Cloud Detection with the MODIS Near-Infrared Water Vapor Absorption Band,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites Multilayer Cloud Detection with the MODIS Near-Infrared Water Vapor Absorption Band,

Reference 15

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Observation 8a1c23bd-4edb-4fe0-9856-6ac2091a5b42 · outbound

This paper cites Algorithm for detecting ice overlaying water multilayer clouds using the infrared bands of FY-4a/AGRI,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites Algorithm for detecting ice overlaying water multilayer clouds using the infrared bands of FY-4a/AGRI,

Reference 16

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Observation 1a207bc3-8d26-4c02-8cb4-246114f0d27f · outbound

This paper cites Multi- layer cloud detection and distributions over the asia–pacific region based on geostationary satellite imagers,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites Multi- layer cloud detection and distributions over the asia–pacific region based on geostationary satellite imagers,

Reference 17

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Observation db27886d-1457-44c2-b9fb-5034bd5caeee · outbound

This paper cites 7.05 - satellite remote sensing of cloud vertical structure,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites 7.05 - satellite remote sensing of cloud vertical structure,

Reference 18

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Observation f1f65f8c-f397-4e67-a949-165a4e5e7ca2 · outbound

This paper cites Evaluation of the MODIS Collection 6 multilayer cloud detection algorithm through comparisons with CloudSat Cloud Profiling Radar and CALIPSO CALIOP products,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites Evaluation of the MODIS Collection 6 multilayer cloud detection algorithm through comparisons with CloudSat Cloud Profiling Radar and CALIPSO CALIOP products,

Reference 19

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Observation dfe12747-bf1a-492c-95eb-272bb51257f2 · outbound

This paper cites Object-based cloud and cloud shadow detection in landsat imagery,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites Object-based cloud and cloud shadow detection in landsat imagery,

Reference 20

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Observation 6969022a-c46d-441b-987d-b592cfdcc471 · outbound

This paper cites Cloud detection methodologies: Variants and development—a review,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites Cloud detection methodologies: Variants and development—a review,

Reference 21

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Observation a5111085-4817-428c-a552-d11a90f3d1e3 · outbound

This paper cites Detection of single and multilayer clouds in an artificial neural network approach,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites Detection of single and multilayer clouds in an artificial neural network approach,

Reference 22

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Observation 4433e458-0a4f-4efd-ada3-d77b5e029225 · outbound

This paper cites Detecting multilayer clouds from the geostationary advanced himawari imager using machine learningtechniques,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites Detecting multilayer clouds from the geostationary advanced himawari imager using machine learningtechniques,

Reference 23

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Observation 68bee103-3181-4a75-ac8d-6f9ef63e7a62 · outbound

This paper cites Low cloud detection in multilayer scenes using satellite imagery with machine learning methods,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites Low cloud detection in multilayer scenes using satellite imagery with machine learning methods,

Reference 24

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Observation 46a2edeb-bd83-4d51-a0c6-38b94dbe407d · outbound

This paper cites Cloud Identification and Properties Retrieval of the Fengyun-4A Satellite Using a ResUnet Model,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites Cloud Identification and Properties Retrieval of the Fengyun-4A Satellite Using a ResUnet Model,

Reference 25

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Observation 43ecef0f-e012-4989-8b50-3c88ce31497e · outbound

This paper cites Detection of single and multilayer clouds in an artificial neural network approach,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites Detection of single and multilayer clouds in an artificial neural network approach,

Reference 26

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Observation 8886b298-c2ad-4767-bda0-4dcb8d6a7c73 · outbound

This paper cites Cloud detection and classification algorithms for himawari-8 imager measurements based on deep learning,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites Cloud detection and classification algorithms for himawari-8 imager measurements based on deep learning,

Reference 27

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Observation 57de2e92-149d-41c2-a3eb-d443c4be72d0 · outbound

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

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites Mcdnet: Multilevel cloud detection network for remote sensing images based on dual- perspective change-guided and multi-scale feature fusion,

Reference 28

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Observation fd71af52-86ef-40c3-a974-fd8202a96ff0 · outbound

This paper cites Cdunet: Cloud detection unet for remote sensing imagery,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites Cdunet: Cloud detection unet for remote sensing imagery,

Reference 29

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Observation 50f3efdb-2b26-4256-be6f-05379c2251e3 · outbound

This paper cites Cnn-transnet: A hybrid cnn-transformer network with differential feature enhancement for cloud detection,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites Cnn-transnet: A hybrid cnn-transformer network with differential feature enhancement for cloud detection,

Reference 30

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Observation a1155120-f1ce-4f73-af5a-5ad3021ad55d · outbound

This paper cites A naive bayesian cloud-detection scheme derived from calipso and applied within patmos-x,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites A naive bayesian cloud-detection scheme derived from calipso and applied within patmos-x,

Reference 31

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Observation 7aa1658b-e615-47fe-aebc-39e668974ddf · outbound

This paper cites Physics-driven machine learning algorithm facilitates multilayer cloud property retrievals from geostationary passive imager measurements,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites Physics-driven machine learning algorithm facilitates multilayer cloud property retrievals from geostationary passive imager measurements,

Reference 32

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Observation 91a27689-6fb4-45af-9ef0-6a62bdbaaf96 · outbound

This paper cites Physics-Driven Machine Learning Algorithm Facili- tates Multilayer Cloud Property Retrievals From Geosta- tionary Passive Imager Measurements,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites Physics-Driven Machine Learning Algorithm Facili- tates Multilayer Cloud Property Retrievals From Geosta- tionary Passive Imager Measurements,

Reference 33

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Observation d3aa585d-3016-4b39-979e-262b38648321 · outbound

This paper cites Effects of Linear Calibration Errors at Low Temperature End of Thermal Infrared Band: Lesson from Failures in Cloud Top Property Retrieval of FengYun-4A Geostationary Satellite,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites Effects of Linear Calibration Errors at Low Temperature End of Thermal Infrared Band: Lesson from Failures in Cloud Top Property Retrieval of FengYun-4A Geostationary Satellite,

Reference 34

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Observation 91f1fc79-d9ba-4aa7-ad67-4d84c2fc66d5 · outbound

This paper cites Introducing the new generation of chinese geostationary weather satellites, fengyun-4,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites Introducing the new generation of chinese geostationary weather satellites, fengyun-4,

Reference 35

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Observation 4e044feb-b634-437a-b879-bb29408276b0 · outbound

This paper cites An introduction to himawari-8/9—japan’s new-generation geostationary meteorological satellites,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites An introduction to himawari-8/9—japan’s new-generation geostationary meteorological satellites,

Reference 36

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Observation 8c7e0749-b02f-4959-9d99-f9932c35dd2e · outbound

This paper cites Characterization of bias of ad- vanced himawari imager infrared observations from nwp background simulations using crtm and rttov,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites Characterization of bias of ad- vanced himawari imager infrared observations from nwp background simulations using crtm and rttov,

Reference 37

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Observation 9cc3a170-0352-4690-bbe4-d6441f4f266a · outbound

This paper cites CALIPSO mission: spaceborne lidar for observation of aerosols and clouds,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites CALIPSO mission: spaceborne lidar for observation of aerosols and clouds,

Reference 38

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Observation 3df1df47-2106-4180-ba6e-42e0316fc76f · outbound

This paper cites The cloudsat mission and the a-train.-a new dimension of space-based observations of clouds and precipitation,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites The cloudsat mission and the a-train.-a new dimension of space-based observations of clouds and precipitation,

Reference 39

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Observation c5cffa3f-6e58-4c1b-b5c9-5514b136ea32 · outbound

This paper cites Construction of Nighttime Cloud Layer Height and Classification of Cloud Types,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites Construction of Nighttime Cloud Layer Height and Classification of Cloud Types,

Reference 40

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Observation ba0b4a31-2a89-428b-9a08-567ebbaa6da1 · outbound

This paper cites New insights about cloud vertical structure from CloudSat and CALIPSO observations,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites New insights about cloud vertical structure from CloudSat and CALIPSO observations,

Reference 41

Resolution: verified exact; doi, observed 2026-08-02T08:23:20.958887Z

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Observation 959e01f9-7961-4b21-8484-617fde664e48 · outbound

This paper cites Global distribution of cirrus clouds from cloudsat/cloud-aerosol lidar and infrared pathfinder satellite ob- servations (calipso) measurements,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites Global distribution of cirrus clouds from cloudsat/cloud-aerosol lidar and infrared pathfinder satellite ob- servations (calipso) measurements,

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-02T08:23:14.049834Z

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Observation d96d68da-b1b5-42c5-8ec9-5b666520105c · outbound

This paper cites Identifica- tion of ice-over-water multilayer clouds using multispectral satellite data in an artificial neural network,.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites Identifica- tion of ice-over-water multilayer clouds using multispectral satellite data in an artificial neural network,

Reference 43

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Observation f49b475f-01e9-4310-87df-a9808f5135ad · outbound

This paper cites Available: https://www.semanticscholar.org/paper/ 7205260816f322689fe1bd52245aaeb1aec0d32c.

Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites Available: https://www.semanticscholar.org/paper/ 7205260816f322689fe1bd52245aaeb1aec0d32c

Reference 2024

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