Paper Citation Record · LEDGER

Deep Learning for Remote Sensing to Improve Flood Inundation Mapping

As of 5 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2606.02310.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2606.02310 v1

Coverage vector

measured 26 of 26 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-06-28T15:15:52.080302Z

measured 26 of 26 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

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26 of 26 outbound references displayed

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Outbound references

Observation d85990cc-8d62-41d2-8ad8-1731cd367998 · outbound

This paper cites Mobility and Resilience : A Global Assessment of Flood Impacts on Road Transportation Networks,.

Deep Learning for Remote Sensing to Improve Flood Inundation Mapping Mobility and Resilience : A Global Assessment of Flood Impacts on Road Transportation Networks,

Reference 1

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Observation 9440aa00-0f74-4c07-b8b2-f40ab9e114c4 · outbound

This paper cites High-resolution mapping of global surface water and its long-term changes,.

Deep Learning for Remote Sensing to Improve Flood Inundation Mapping High-resolution mapping of global surface water and its long-term changes,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-28T15:15:52.080302Z

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Observation fb03feed-a70f-46c7-9d59-2698d68395fd · outbound

This paper cites Global flood extent segmentation in optical satellite images,.

Deep Learning for Remote Sensing to Improve Flood Inundation Mapping Global flood extent segmentation in optical satellite images,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-28T15:15:52.080302Z

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Observation 1d625ba5-bf63-4b77-baa9-14ff1b609a9e · outbound

This paper cites Effective- ness of sentinel-1 and sentinel-2 for flood detection as- sessment in europe,.

Deep Learning for Remote Sensing to Improve Flood Inundation Mapping Effective- ness of sentinel-1 and sentinel-2 for flood detection as- sessment in europe,

Reference 4

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Observation c5bbc7b0-92f4-49d8-8c90-aea4ff982917 · outbound

This paper cites Intro- ducing a new index for flood mapping using sentinel-2 imagery (sfmi),.

Deep Learning for Remote Sensing to Improve Flood Inundation Mapping Intro- ducing a new index for flood mapping using sentinel-2 imagery (sfmi),

Reference 5

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Observation d6bbb9d8-4cf3-4fa9-a118-005d90c77ea7 · outbound

This paper cites Overview of sentinel-2,.

Deep Learning for Remote Sensing to Improve Flood Inundation Mapping Overview of sentinel-2,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-28T15:15:52.080302Z

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Observation 9b87d834-26cd-4a84-aec4-0158919c2056 · outbound

This paper cites Sentinel-2: Esa’s optical high-resolution mission for gmes operational services,.

Deep Learning for Remote Sensing to Improve Flood Inundation Mapping Sentinel-2: Esa’s optical high-resolution mission for gmes operational services,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-28T15:15:52.080302Z

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Observation f398d738-dbda-4753-85f7-9a07e0ae280c · outbound

This paper cites Cloud cover throughout the agricultural growing season: Impacts on passive optical earth obser- vations,.

Deep Learning for Remote Sensing to Improve Flood Inundation Mapping Cloud cover throughout the agricultural growing season: Impacts on passive optical earth obser- vations,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-28T15:15:52.080302Z

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Observation 20b7ad49-d165-4cc9-a2c3-9d4f736fd7c0 · outbound

This paper cites Sentinel 1 evolution: Sentinel-1c and-1d mod- els,.

Deep Learning for Remote Sensing to Improve Flood Inundation Mapping Sentinel 1 evolution: Sentinel-1c and-1d mod- els,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-28T15:15:52.080302Z

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Observation bf70a9a5-ebb2-4578-84be-0c5f5b419452 · outbound

This paper cites A speckle filter for sentinel-1 sar ground range detected data based on residual convolutional neu- ral networks,.

Deep Learning for Remote Sensing to Improve Flood Inundation Mapping A speckle filter for sentinel-1 sar ground range detected data based on residual convolutional neu- ral networks,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-28T15:15:52.080302Z

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Observation 175613b8-e0d0-4237-bef9-1eafc9e43b83 · outbound

This paper cites Sensitivity of sentinel-1 backscatter to characteristics of buildings,.

Deep Learning for Remote Sensing to Improve Flood Inundation Mapping Sensitivity of sentinel-1 backscatter to characteristics of buildings,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-28T15:15:52.080302Z

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Observation a16bb9b4-57a1-4457-9810-6f6583dea3f2 · outbound

This paper cites A method for compositing polar modis satellite images to remove cloud cover for landfast sea-ice detection,.

Deep Learning for Remote Sensing to Improve Flood Inundation Mapping A method for compositing polar modis satellite images to remove cloud cover for landfast sea-ice detection,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-28T15:15:52.080302Z

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Observation 4386d54b-2787-436b-abf6-8fed84d441ea · outbound

This paper cites Automatic mosaicking of satellite imagery con- sidering the clouds,.

Deep Learning for Remote Sensing to Improve Flood Inundation Mapping Automatic mosaicking of satellite imagery con- sidering the clouds,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-28T15:15:52.080302Z

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Observation 0353dee1-a1c3-48da-84d0-33306e5e01cb · outbound

This paper cites Multi- temporal landsat data automatic cloud removal using poisson blending,.

Deep Learning for Remote Sensing to Improve Flood Inundation Mapping Multi- temporal landsat data automatic cloud removal using poisson blending,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-28T15:15:52.080302Z

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Observation 3a8f71d3-c96c-4e98-8889-5477ceced640 · outbound

This paper cites Missing information reconstruction of remote sensing data: A technical review,.

Deep Learning for Remote Sensing to Improve Flood Inundation Mapping Missing information reconstruction of remote sensing data: A technical review,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-28T15:15:52.080302Z

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Observation 290f0860-a111-495c-a3f8-21a041c04804 · outbound

This paper cites Generative deep learning models for cloud removal in satellite imagery: A comparative review of gans and diffusion methods,.

Deep Learning for Remote Sensing to Improve Flood Inundation Mapping Generative deep learning models for cloud removal in satellite imagery: A comparative review of gans and diffusion methods,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-28T15:15:52.080302Z

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Observation bdbcd049-2892-4ae7-90e5-a6b1a8e0fa35 · outbound

This paper cites Cloud removal in remote sensing images using genera- tive adversarial networks and sar-to-optical image trans- lation,.

Deep Learning for Remote Sensing to Improve Flood Inundation Mapping Cloud removal in remote sensing images using genera- tive adversarial networks and sar-to-optical image trans- lation,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-28T15:15:52.080302Z

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Observation 1cb8288b-70b3-4b5a-b0ce-4239b89ecd30 · outbound

This paper cites Cloud removal using multimodal gan with adversarial consistency loss,.

Deep Learning for Remote Sensing to Improve Flood Inundation Mapping Cloud removal using multimodal gan with adversarial consistency loss,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-28T15:15:52.080302Z

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Observation f698b0c0-b393-4038-abc0-7854373eb1c0 · outbound

This paper cites An in-depth review and analysis of mode collapse in generative adversarial net- works,.

Deep Learning for Remote Sensing to Improve Flood Inundation Mapping An in-depth review and analysis of mode collapse in generative adversarial net- works,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-28T15:15:52.080302Z

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Observation 120500c9-9c37-4423-a731-d0809c6b2fad · outbound

This paper cites Cloud-gan: Cloud removal for sentinel-2 imagery using a cyclic consistent genera- tive adversarial networks,.

Deep Learning for Remote Sensing to Improve Flood Inundation Mapping Cloud-gan: Cloud removal for sentinel-2 imagery using a cyclic consistent genera- tive adversarial networks,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-28T15:15:52.080302Z

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Observation 2ec4fd88-6fb8-49c0-a601-fd14f04c06cf · outbound

This paper cites Denoising diffusion probabilistic models,.

Deep Learning for Remote Sensing to Improve Flood Inundation Mapping Denoising diffusion probabilistic models,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-28T15:15:52.080302Z

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Observation f0caa2e9-6bc3-4a70-944f-cd1c68a9a6d8 · outbound

This paper cites Masked diffusion transformer is a strong image synthesizer,.

Deep Learning for Remote Sensing to Improve Flood Inundation Mapping Masked diffusion transformer is a strong image synthesizer,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-06-28T15:15:52.080302Z

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Observation 149f6abb-398f-402a-9af9-7241ca5dfd2f · outbound

This paper cites Unraveling the 2021 central tennessee flood event using a hierarchical multi-model inundation modeling frame- work,.

Deep Learning for Remote Sensing to Improve Flood Inundation Mapping Unraveling the 2021 central tennessee flood event using a hierarchical multi-model inundation modeling frame- work,

Reference 23

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Observation 74dcf9c2-8606-42a5-8266-9cb3cb5737ae · outbound

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

Deep Learning for Remote Sensing to Improve Flood Inundation Mapping Cloudsen12, a global dataset for semantic un- derstanding of cloud and cloud shadow in sentinel-2,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-06-28T15:15:52.080302Z

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Observation e6f94caf-5e96-4c72-84b6-e4e0cd279e14 · outbound

This paper cites Nas-unet: Neural architecture search for medical image segmentation,.

Deep Learning for Remote Sensing to Improve Flood Inundation Mapping Nas-unet: Neural architecture search for medical image segmentation,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-06-28T15:15:52.080302Z

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Observation 0e0703c1-4106-4505-8e55-c091e7517ab1 · outbound

This paper cites A vit- based multiscale feature fusion approach for remote sens- ing image segmentation,.

Deep Learning for Remote Sensing to Improve Flood Inundation Mapping A vit- based multiscale feature fusion approach for remote sens- ing image segmentation,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-06-28T15:15:52.080302Z

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Pith citing papers

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