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Paper Citation Record · LEDGER

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series

As of 8 August 2026, this Paper Citation Record lists 100 of 118 outbound references and 0 inbound Pith citation observations for arXiv:2608.04706.

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

pith.paper-citation-record.v1
2608.04706 v1

Coverage vector

measured 100 of 118 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:32:21.965465Z

measured 100 of 100 standing notices

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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

100 of 118 outbound references displayed

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  • verified fuzzy10
  • unresolved68
  • parse uncertain1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fe035e70-3542-41e3-a685-3dd74cc4a32d · outbound

This paper cites Wong and Christian Thomas and Patrick Halpin , keywords =.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Wong and Christian Thomas and Patrick Halpin , keywords =

Reference 1

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Observation 0ef12f4e-d0b3-4b82-92ff-32c8dc73011b · outbound

This paper cites Soviet Physics Doklady , volume=.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Soviet Physics Doklady , volume=

Reference 3

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Unresolved cited work

Reference 4

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This paper cites and Haberland, Matt and Reddy, Tyler and Cournapeau, David and Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and Bright, Jonathan and.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series and Haberland, Matt and Reddy, Tyler and Cournapeau, David and Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and Bright, Jonathan and

Reference 5

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Observation f69c4c04-9f72-4832-b3f9-9eed3eed7b0a · outbound

This paper cites Earth System Science Data , VOLUME =.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Earth System Science Data , VOLUME =

Reference 6

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Observation 2f8662c5-2113-438f-b377-cbf89586e264 · outbound

This paper cites 2017 , note =.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series 2017 , note =

Reference 7

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Observation 20af7974-b8f1-40a7-b4b1-e1215f22b148 · outbound

This paper cites Sensors , VOLUME =.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Sensors , VOLUME =

Reference 8

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Unresolved cited work

Reference 9

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This paper cites Entropy , VOLUME =.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Entropy , VOLUME =

Reference 10

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Observation e3a2b6d6-fbe3-4674-931d-9a63e02955bf · outbound

This paper cites International Journal of Remote Sensing , volume =.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series International Journal of Remote Sensing , volume =

Reference 13

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This paper cites GIScience & Remote Sensing , volume =.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series GIScience & Remote Sensing , volume =

Reference 14

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This paper cites Remote Sensing , VOLUME =.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Remote Sensing , VOLUME =

Reference 16

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Observation 997725ee-528f-45df-829a-6fb4a4e1a4f1 · outbound

This paper cites Extraction of Offshore Wind Turbines in China by Combining Multispectral and SAR Image Data , year=.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Extraction of Offshore Wind Turbines in China by Combining Multispectral and SAR Image Data , year=

Reference 17

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series LandTrendr — Temporal segmentation algorithms , journal =

Reference 19

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This paper cites Geo-spatial Information Science , volume =.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Geo-spatial Information Science , volume =

Reference 21

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Observation 489b51a5-aaf5-4266-8f8a-47b400c0ecac · outbound

This paper cites and Matos-Carvalho, João P.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series and Matos-Carvalho, João P

Reference 22

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Observation aa6447e4-8c11-4c4d-b0a9-5616827c7380 · outbound

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Energies , VOLUME =

Reference 23

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Remote Sensing , VOLUME =

Reference 24

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Observation 8a01bd6d-469d-429e-b787-107ea516cd1e · outbound

This paper cites Automatic extraction of offshore platforms using time-series Landsat-8 Operational Land Imager data , journal =.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Automatic extraction of offshore platforms using time-series Landsat-8 Operational Land Imager data , journal =

Reference 27

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series 2023 , issn =

Reference 29

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Unresolved cited work

Reference 30

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series 2023 , issn =

Reference 31

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series 2024 , issn =

Reference 32

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series 2025 , issn =

Reference 33

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series GMES Sentinel-1 mission , journal =

Reference 34

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Unresolved cited work

Reference 35

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series and Porchetta, S

Reference 36

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series 2024 , issn =

Reference 37

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series 2025 , issn =

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series 2022 , issn =

Reference 39

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , year=

Reference 40

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series YOLOv10: Real-Time End-to-End Object Detection

Reference 41

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Journal of Renewable and Sustainable Energy , volume =

Reference 42

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Emergence of floating offshore wind energy: Technology and industry , journal =

Reference 43

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Unresolved cited work

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series An innovative platform for exploring the Earth , howpublished =

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series and Bachofer, F

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series and Kuenzer, C

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series doi:10.5281/zenodo.18735421 , url =

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Frontiers in Handwriting Recognition (ICFHR), 2014 14th International Conference on , pages=

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Soft Computing and Pattern Recognition (SoCPaR), 2014 6th International Conference of , pages=

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This paper cites Advanced Data Mining and Applications: 12th International Conference, ADMA 2016, Gold Coast, QLD, Australia, December 12-15, 2016, Proceedings 12 , pages=.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Advanced Data Mining and Applications: 12th International Conference, ADMA 2016, Gold Coast, QLD, Australia, December 12-15, 2016, Proceedings 12 , pages=

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series 2025 , howpublished =

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series The Journal of Supercomputing , year =

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series A Review of Deep Learning Models for Time Series Prediction , year=

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series LSTM can Solve Hard Long Time Lag Problems , url =

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series , journal=

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series , journal=

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series 1990 , issn =

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series and Paliwal, K.K

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) , year =

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series 2018 , howpublished =

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Language Models are Few-Shot Learners , url =

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Learning Complex, Extended Sequences Using the Principle of History Compression , year=

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series 2024 , eprint =

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Akhtar, B

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Brown, B

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Djath and J

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series EnBW he dreiht: First wind turbine installed

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series An eu strategy to harness the potential of offshore renewable energy for a climate neutral future

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Union of the esri country shapefile and the exclusive economic zones (version 4)

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Download data | global energy monitor

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Observation a9c9da44-2331-42b6-aaf8-ca456f6fda56 · outbound

This paper cites Graves and J.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Graves and J

Reference 107

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Observation 7c048305-e0f3-40c7-b929-bec9af6519e9 · outbound

This paper cites an unresolved cited work.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Unresolved cited work

Reference 108

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 0de55ba7-3338-4f9a-98a0-53e5a65f1f68 · outbound

This paper cites an unresolved cited work.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Unresolved cited work

Reference 109

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 08cbddd8-8019-429b-9aec-7eeb456d087c · outbound

This paper cites an unresolved cited work.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Unresolved cited work

Reference 110

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verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 03efa657-038a-41bf-87f9-d44eb8792889 · outbound

This paper cites Hochreiter and J.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Hochreiter and J

Reference 111

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 25ef6b67-572a-4d45-93e6-e8fb2f17d618 · outbound

This paper cites Hochreiter and J.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Hochreiter and J

Reference 112

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source=arxiv_source observed=2026-08-06T18:32:20.503469Z digest=sha256:8dd40588bd64f0de62dc4ac7fbb64cd154bf672fa746de5f86cddf7bc5511ebe

Observation 10f4eb81-7f2e-4f35-a640-7b7af671fadd · outbound

This paper cites Hoeser and C.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Hoeser and C

Reference 113

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:32:20.587829Z digest=sha256:78048a0e5d2aa000035dab927182688c82cc190458e84f813870b5ac40172237

Observation c4fb4440-0677-4141-a8a1-c7237d502c06 · outbound

This paper cites Hoeser, S.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Hoeser, S

Reference 114

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verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 2e1e1f3c-da79-4af3-8013-ca789b7c5a9f · outbound

This paper cites Hoeser, F.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Hoeser, F

Reference 115

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no resolver link, observed 2026-08-06T18:32:20.733078Z

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Observation 6a8f3458-dc4d-4170-b79f-456f1454db90 · outbound

This paper cites Howard and S.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Howard and S

Reference 116

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no resolver link, observed 2026-08-06T18:32:20.810693Z

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Observation 973d2d40-dacf-4e46-a3cc-c822239cc80c · outbound

This paper cites Ismail Fawaz, G.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Ismail Fawaz, G

Reference 117

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source=arxiv_source observed=2026-08-06T18:32:20.876715Z digest=sha256:8b6ac796b6c96fb65539cc22630c80d99743f21eaa61c6222caf2cc3bfd19dc1

Observation bab33724-6734-4763-a763-a0c94913e1c2 · outbound

This paper cites Lai, C.-Y.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Lai, C.-Y

Reference 118

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source=arxiv_source observed=2026-08-06T18:32:20.938744Z digest=sha256:fb3a337527f489106f8f9de03d6860ae6c0fe6ae3537b517151284e54e4c1860

Observation e0adee4d-9439-41f8-915b-fb8584c92d33 · outbound

This paper cites an unresolved cited work.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Unresolved cited work

Reference 119

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raw_fallback, observed 2026-08-06T18:32:31.699770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 47acd1d2-5806-44c0-a68d-bee48d60a889 · outbound

This paper cites Lin and Y.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Lin and Y

Reference 120

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raw_fallback, observed 2026-08-06T18:32:26.963133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:32:21.087030Z digest=sha256:6156d7395693104877389c6605ef5c0ad4488d365de5f226b50c18e1e7990daf

Observation e87fba3a-8138-4bc2-b1a2-3f55eac7d601 · outbound

This paper cites an unresolved cited work.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Unresolved cited work

Reference 121

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:32:21.159083Z digest=sha256:6adb8aa803cdc5bed65d3192877f14816f5d03502920bbf4e42c08d12676ff1f

Observation fb61407d-08fe-4159-926a-0b2e4bfb6128 · outbound

This paper cites an unresolved cited work.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Unresolved cited work

Reference 122

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation b0943437-c151-4da5-9ef7-43f79323fc14 · outbound

This paper cites SGDR: Stochastic Gradient Descent with Warm Restarts.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 123

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no resolver link, observed 2026-08-06T18:32:21.268061Z

Source-reported events for the cited work

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Observation fc6c6e81-8db5-4a49-9284-ead496dc9f5e · outbound

This paper cites Decoupled Weight Decay Regularization.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Decoupled Weight Decay Regularization

Reference 124

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no resolver link, observed 2026-08-06T18:32:21.357168Z

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source=arxiv_source observed=2026-08-06T18:32:21.357168Z digest=sha256:ff266bff0541d3b0412f9919f0602a74203d71012f4f1ba3c24ddd8a37aaeeaf

Observation 4397a144-8404-4993-9f04-6a3a28adff06 · outbound

This paper cites an unresolved cited work.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Unresolved cited work

Reference 125

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 51c61768-ba40-4ece-954a-3734d3f1d414 · outbound

This paper cites an unresolved cited work.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Unresolved cited work

Reference 126

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verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 2da1a8fb-dc63-4e96-9ef6-2213a92792be · outbound

This paper cites an unresolved cited work.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Unresolved cited work

Reference 127

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source=arxiv_source observed=2026-08-06T18:32:21.554396Z digest=sha256:c6fcd47f1d7da2a32ad3c729c7686b8f96fc17e7578c2bbafe43c4a9362da96c

Observation 640dc354-dff5-4579-a985-5430769512b9 · outbound

This paper cites an unresolved cited work.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Unresolved cited work

Reference 128

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation c22ac76c-e6a1-4b83-8a26-5657eb67c293 · outbound

This paper cites Radford, K.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Radford, K

Reference 129

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raw_fallback, observed 2026-08-06T18:32:31.537432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T18:32:21.690422Z digest=sha256:d724028d43ec307ebcfc3c5ad6bde8ad6ab1377a1f35d5e2681aff09b02da69b

Observation 807d39ec-c838-4672-bb94-08d6f7250c75 · outbound

This paper cites Rußwurm and M.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Rußwurm and M

Reference 130

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no resolver link, observed 2026-08-06T18:32:21.799391Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:32:21.799391Z digest=sha256:703de94c3dbe32f08f6464ba9294c14a2a733bba94fb74c2e16bde2bc5b807e7

Observation eaefdc9f-a184-49d1-8ba0-83acdb67892a · outbound

This paper cites Schmidhuber.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Schmidhuber

Reference 131

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no resolver link, observed 2026-08-06T18:32:21.858181Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:32:21.858181Z digest=sha256:ef7725d0d5f5a2accaecf95c06e0ff668dfd8502dde217e58cbc07beb8657de0

Observation b307fb0b-8882-42db-a3af-eb2bee733a2b · outbound

This paper cites Schuster and K.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Schuster and K

Reference 132

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no resolver link, observed 2026-08-06T18:32:21.965465Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:32:21.965465Z digest=sha256:4f97ff0a597c1c1483a1fe090baa75911243c2bd3bc7318df59c382636ff0c3c

Pith citing papers

No inbound Pith citation observations are available.