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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 7 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

One-hop event checks from named stored sources.

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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
  • malformed identifier0
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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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Observation 3db2ccdd-4792-4936-a43c-0a6899124bc8 · outbound

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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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 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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Observation ca896c47-eb2d-4c2e-b0f0-74c6502ff2f2 · outbound

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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Observation 0beeb2be-6737-446c-be56-44177b5d4fdc · outbound

This paper cites LandTrendr — Temporal segmentation algorithms , journal =.

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 =

Reference 38

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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

Reference 47

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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 48

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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

Reference 50

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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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This paper cites Frontiers in Handwriting Recognition (ICFHR), 2014 14th International Conference on , pages=.

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 Unresolved cited work

Reference 56

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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 58

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

Reference 59

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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=

Reference 68

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

Reference 69

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

Reference 70

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Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series and Kaiser, Lukasz and Polosukhin, Illia , title =

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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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Reference 98

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Observation 84a1ae47-43e2-471a-9b87-8c22faac4772 · outbound

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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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Reference 101

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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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This paper cites Union of the esri country shapefile and the exclusive economic zones (version 4).

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)

Reference 104

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This paper cites Download data | global energy monitor.

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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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.

source=arxiv_source observed=2026-08-06T18:32:20.059241Z digest=sha256:6a3d5812822719054379c208b81a46df20641eeb9a5c464af8a56a0af6ee6f8d

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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unresolved
no resolver link, observed 2026-08-06T18:32:20.121801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:32:20.121801Z digest=sha256:53f4ac26e35268d2157445ce0c11b2b95bb54a3c67a9de9ee965f35ebb07246a

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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metadata mismatch
raw_fallback, observed 2026-08-06T18:32:25.318870Z

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.

source=arxiv_source observed=2026-08-06T18:32:20.179613Z digest=sha256:8cdfbd85bf7979b188bc30db3d06775e599ac85a2658de83746aa2c8ca899688

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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metadata mismatch
raw_fallback, observed 2026-08-06T18:32:30.567517Z

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.

source=arxiv_source observed=2026-08-06T18:32:20.268640Z digest=sha256:101629770da1361d1666b893437f4220c0d900d68f10f0509b68e7ca10816992

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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-06T18:32:20.333311Z digest=sha256:5aa10468ed44e13e279088ab1da57d5bd1eb4a795f6f9c474306c01d06edf5d5

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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verified fuzzy
raw_fallback, observed 2026-08-06T18:32:31.874066Z

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.

source=arxiv_source observed=2026-08-06T18:32:20.431057Z digest=sha256:c94c5bcc76fd73c5fb58b89094eca866155e792b638a50a22d1150311b3b9435

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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unresolved
no resolver link, observed 2026-08-06T18:32:20.503469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:32:20.503469Z digest=sha256:100b1afe8de483de239aa816e9a7a1e5f50dd3c0b4652cfdc24aeffe72d274c6

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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metadata mismatch
raw_fallback, observed 2026-08-06T18:32:30.064470Z

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.

source=arxiv_source observed=2026-08-06T18:32:20.587829Z digest=sha256:3b45516a4daa563946127eee14741f90ad415ffa1436f784ad9827d3465493c0

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
doi, observed 2026-08-06T18:32:23.437401Z

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.

source=arxiv_source observed=2026-08-06T18:32:20.667606Z digest=sha256:b130c77736597734c402d1b30ad1f823bd9881c77e7c10ff64b76deeee7e890b

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:32:20.733078Z digest=sha256:4eed1bc811b9dfc103ee516bcf39efcdf66e89f9d423e78fa36e2ad40b9fc13e

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:32:20.810693Z digest=sha256:1112f43c85e20c07b9aa4179e81d94818ccf784bf67fb5f9fe2b832aeaaa3dcd

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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no resolver link, observed 2026-08-06T18:32:20.876715Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:32:20.876715Z digest=sha256:2846f0a7cbe6752d5ab2d0743ccdc5df669323a677ad2f589e0b0d0a25b24731

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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no resolver link, observed 2026-08-06T18:32:20.938744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:32:20.938744Z digest=sha256:000076155a736e0b4616391e77341f2b48ed18c8998614d7f41237e9a7257f5a

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

Resolution
unresolved
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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-06T18:32:21.033578Z digest=sha256:dc97ce9f4e8d91a3f59e0885f4b93491aebd964c8751cb83d751772cbec562ba

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

Resolution
metadata mismatch
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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-06T18:32:21.087030Z digest=sha256:76ea55dd78590e8059bcf074603ad07b2863b7c19c09e758fb27b30c0a4a75bf

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

Resolution
metadata mismatch
raw_fallback, observed 2026-08-06T18:32:30.392175Z

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.

source=arxiv_source observed=2026-08-06T18:32:21.159083Z digest=sha256:51ae3853a2fea152c16e4ef8ed893a7eccc0617c41ebb1c5bdc868c6947b86d7

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

Resolution
metadata mismatch
raw_fallback, observed 2026-08-06T18:32:29.761669Z

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.

source=arxiv_source observed=2026-08-06T18:32:21.223195Z digest=sha256:749c95651f30e5bf64d58d7909f60bfa02735fa4a6660ef676eb059a73ec7b1f

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:32:21.268061Z digest=sha256:d6ded41efa0e28651b2ee12a7064a29b7388d9b0f9ede2a4d0503e4b9deb3139

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:32:21.357168Z digest=sha256:d532dc29565f4e161eade239b28cfc1a1bd3d956f4bc6d3387ebcb846f66f816

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

Resolution
metadata mismatch
raw_fallback, observed 2026-08-06T18:32:25.803218Z

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.

source=arxiv_source observed=2026-08-06T18:32:21.412421Z digest=sha256:5611fd9ea003f80d3f2d4de1f5ee9796fbdf64c3858e0e575bf2e00a6894b577

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

Resolution
verified exact
doi, observed 2026-08-06T18:32:23.772512Z

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.

source=arxiv_source observed=2026-08-06T18:32:21.484333Z digest=sha256:2f0da1cb941546192accfebf2113a08044e0249c3bf1bb331f0e27c78fb75ec9

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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unresolved
no resolver link, observed 2026-08-06T18:32:21.554396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:32:21.554396Z digest=sha256:e151581cac1331e05f6458bed8ab67da02e8b6497e93c0a668fa2522ec9842fa

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

Resolution
metadata mismatch
raw_fallback, observed 2026-08-06T18:32:26.492079Z

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.

source=arxiv_source observed=2026-08-06T18:32:21.642831Z digest=sha256:6e3f7ef63a47e225013f0c4e52dcf9dd08699a8037df996ad5078a4b0ea0ff9b

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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verified fuzzy
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 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-06T18:32:21.690422Z digest=sha256:406ffe524cc0787e864a3a3ec626ef11d7fe542f89f31abec0e56e6ac859f271

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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unresolved
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=sha256:32b2102c774811a7dab82b01f6300ba1f143b62a98d62514f7ddd66b40be135a

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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unresolved
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:a74b122d385af6c0761a42c7eef90a100ddc3d3c6001f3890b50ef302c8b6fc2

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:de17f9341f1d3100c198caa2dabbd31ea22fe3b21eabdb59e4e365842831f10c

Pith citing papers

No inbound Pith citation observations are available.