Paper Citation Record · LEDGER

Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data

As of 8 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2506.06235.

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

pith.paper-citation-record.v1
2506.06235 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T06:03:16.124333Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

27 of 27 outbound references displayed

verified exact2
verified fuzzy15
unresolved10
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3149fe58-02ee-47a4-8f00-54b20908d3ff · outbound

This paper cites Efficient pytorch i/o library for large datasets, many files, many gpus.

Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data Efficient pytorch i/o library for large datasets, many files, many gpus

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:03:16.399353Z

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Observation de2715bc-a468-4b70-aba3-0c17e21608ed · outbound

This paper cites Optuna: A next-generation hyperparameter optimization framework.

Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data Optuna: A next-generation hyperparameter optimization framework

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:03:16.389265Z

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Observation 86c4cecc-e0e1-44bd-a19f-511ae68bee9f · outbound

This paper cites Geotiff compression optimization guide, 2018.

Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data Geotiff compression optimization guide, 2018

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:03:16.379629Z

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Observation 20ce7067-bd28-49f3-ac68-3297e2a9ee33 · outbound

This paper cites Algorithms for hyper-parameter optimization.

Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data Algorithms for hyper-parameter optimization

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:03:16.369951Z

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Observation eca131ae-654d-4503-9d06-0d6059c326a7 · outbound

This paper cites Cogeo: Cloud-optimized geospatial ecosystem.

Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data Cogeo: Cloud-optimized geospatial ecosystem

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:03:16.359608Z

Source-reported events for the cited work

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Observation 1b11eeff-d392-4aac-9464-06a4b8ad33f3 · outbound

This paper cites Dawnbench: An end-to-end deep learning benchmark and competition.

Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data Dawnbench: An end-to-end deep learning benchmark and competition

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:03:16.349290Z

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Observation 1552839a-33b5-4c47-88d0-018acfb3b9eb · outbound

This paper cites an unresolved cited work.

Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-07T06:03:16.338903Z

Source-reported events for the cited work

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Observation 90b5b21f-bb8a-478c-8817-dbf9e5d04fe1 · outbound

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

Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data Sentinel-2: Esa's optical high-resolution mission for gmes operational services

Reference 8

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Source-reported events for the cited work

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Observation b3fc7e3c-9a2c-4733-96df-fee9e4cb78db · outbound

This paper cites J., Irakulis-Loitxate, I., and Guanter, L.

Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data J., Irakulis-Loitxate, I., and Guanter, L

Reference 9

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Source-reported events for the cited work

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Observation 9a925e23-ded7-4037-bc45-3ed3e2ec7e56 · outbound

This paper cites Cloud native data loaders for machine learning using zarr and xarray, March 2024.

Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data Cloud native data loaders for machine learning using zarr and xarray, March 2024

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:03:16.318897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9aa936dc-41c0-4979-96a9-b8fbf12dbf45 · outbound

This paper cites C., Boulch, A., Lefevre, S., and Saux, B.

Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data C., Boulch, A., Lefevre, S., and Saux, B

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:03:16.308474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3f58a05b-ee7f-4652-a15c-19854ddd19c5 · outbound

This paper cites Deep residual learning for image recognition.

Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data Deep residual learning for image recognition

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T06:03:16.070437Z

Source-reported events for the cited work

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Observation 74e8400a-2d2c-465a-aa81-bb2e98e7dead · outbound

This paper cites Isprs 2d semantic labeling benchmark – vaihingen and potsdam, 2014.

Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data Isprs 2d semantic labeling benchmark – vaihingen and potsdam, 2014

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:03:16.291872Z

Source-reported events for the cited work

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Observation 0415c413-02e9-4c11-8b31-3e9556eb1ac8 · outbound

This paper cites J., and Leong, W.

Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data J., and Leong, W

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:03:16.281145Z

Source-reported events for the cited work

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Observation e91f85a7-1324-4447-97e2-579e198729ed · outbound

This paper cites s3-connector-for-pytorch.

Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data s3-connector-for-pytorch

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:03:16.270772Z

Source-reported events for the cited work

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Observation c00403ce-0dbf-46bb-8f77-8a31b155e637 · outbound

This paper cites M., Salman, H., and Madry, A.

Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data M., Salman, H., and Madry, A

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T06:03:16.084389Z

Source-reported events for the cited work

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Observation 07fd07f2-a7bc-4ddf-9f97-e5ab99513473 · outbound

This paper cites Decoupled Weight Decay Regularization.

Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data Decoupled Weight Decay Regularization

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T06:03:16.087683Z

Source-reported events for the cited work

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Observation 24423948-2e29-4c53-987f-159f21dc9259 · outbound

This paper cites Mlperf training benchmark.

Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data Mlperf training benchmark

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:03:16.254375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a46be1e4-7ac8-4a4b-98be-7a94ec0dadba · outbound

This paper cites Analyzing and Mitigating Data Stalls in DNN Training.

Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data Analyzing and Mitigating Data Stalls in DNN Training

Reference 20

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Source-reported events for the cited work

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Observation fb9bb2c2-1eca-414d-a1cc-53f44a1b0ddc · outbound

This paper cites DALI : Nvidia data loading library.

Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data DALI : Nvidia data loading library

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:03:16.244720Z

Source-reported events for the cited work

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Observation c649b92a-99be-4e8f-bc26-67938b57df30 · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T06:03:16.104312Z

Source-reported events for the cited work

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Observation 7542f08b-6fb8-424b-82ec-8c90d34692bc · outbound

This paper cites R., Murayama, Y., and Ranagalage, M.

Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data R., Murayama, Y., and Ranagalage, M

Reference 23

Resolution: verified exact; doi, observed 2026-08-07T06:03:16.167385Z

Source-reported events for the cited work

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Observation e06f9f3a-06a6-4ffe-8bef-c31e3db19933 · outbound

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

Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data U-net: Convolutional networks for biomedical image segmentation

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T06:03:16.111163Z

Source-reported events for the cited work

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Observation fed1f1ce-9dca-462a-9ae6-038564827e2b · outbound

This paper cites L., Araus, J.

Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data L., Araus, J

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T06:03:16.114273Z

Source-reported events for the cited work

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Observation e25ba89c-dd54-4211-b2e9-9633ee7f71fe · outbound

This paper cites O., McFarland, M., Emanuele, R., Morris, D., and Augspurger, T.

Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data O., McFarland, M., Emanuele, R., Morris, D., and Augspurger, T

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T06:03:16.117827Z

Source-reported events for the cited work

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Observation 38fb937b-76aa-4e94-a95f-0e87850cad33 · outbound

This paper cites Profiling and Improving the PyTorch Dataloader for high-latency Storage: A Technical Report.

Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data Profiling and Improving the PyTorch Dataloader for high-latency Storage: A Technical Report

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-07T06:03:16.188800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f31a087c-7b77-4003-a42c-efefae6c1418 · outbound

This paper cites Environmental impacts of earth observation data in the constellation and cloud computing era.

Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data Environmental impacts of earth observation data in the constellation and cloud computing era

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:03:16.228713Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

No inbound Pith citation observations are available.