Paper Citation Record · LEDGER

Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery

As of 8 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2608.01906.

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

pith.paper-citation-record.v1
2608.01906 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T18:34:00.740051Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

35 of 35 outbound references displayed

verified exact0
verified fuzzy0
unresolved34
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 97a5f922-c734-4b23-81aa-f36ddadd01c2 · outbound

This paper cites Large Language Models for Mathematical Reasoning: Progresses and Challenges.

Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery Large Language Models for Mathematical Reasoning: Progresses and Challenges

Reference 1

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Observation 5ce303d8-f77a-4520-9c8f-640c8bb1576b · outbound

This paper cites Do As I Can, Not As I Say: Grounding Language in Robotic Affordances.

Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

Reference 2

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Observation b5632d41-86f2-4eaa-9db5-80067aedd978 · outbound

This paper cites MMDetection: Open MMLab Detection Toolbox and Benchmark.

Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery MMDetection: Open MMLab Detection Toolbox and Benchmark

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This paper cites an unresolved cited work.

Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery Unresolved cited work

Reference 4

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Observation d38b4a16-ba89-4780-b3c3-a7b1b8600674 · outbound

This paper cites Scientific reports12(1) (2022).

Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery Scientific reports12(1) (2022)

Reference 5

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This paper cites (2020),https://www.fema.

Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery (2020),https://www.fema

Reference 6

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Observation 2b645bac-2bff-4bd9-b76e-c43566af114a · outbound

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Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery Unresolved cited work

Reference 7

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Observation c8d47161-bf10-4a3d-84e1-ce0355723183 · outbound

This paper cites arXiv preprint arXiv:2602.16931 (2026) Post-Disaster Building Damage Assessment from UAV Imagery 17.

Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery arXiv preprint arXiv:2602.16931 (2026) Post-Disaster Building Damage Assessment from UAV Imagery 17

Reference 8

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This paper cites In: Proceedings of the IEEE international conference on computer vision.

Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery In: Proceedings of the IEEE international conference on computer vision

Reference 9

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This paper cites In: ICLR (2022).

Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery In: ICLR (2022)

Reference 10

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Observation d15d44c9-9652-4598-b331-479007a0e7aa · outbound

This paper cites In: The Thirteenth International Conference on Learning Representations (2025).

Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery In: The Thirteenth International Conference on Learning Representations (2025)

Reference 11

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Observation 082afdb4-ccfe-4709-87f9-e5dbf483761c · outbound

This paper cites In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.

Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Reference 12

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Observation 842ee7bc-7382-428f-ac0d-11110cd6b6f4 · outbound

This paper cites LLaVA-OneVision: Easy Visual Task Transfer.

Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery LLaVA-OneVision: Easy Visual Task Transfer

Reference 13

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Observation 316086ef-5861-4be6-8b90-bccb62e2e1dd · outbound

This paper cites LoRA Dropout as a Sparsity Regularizer for Overfitting Control.

Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery LoRA Dropout as a Sparsity Regularizer for Overfitting Control

Reference 14

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Observation c1994894-892a-4383-a6a7-7438da8830da · outbound

This paper cites Advances in neural information processing systems36, 34892–34916 (2023).

Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery Advances in neural information processing systems36, 34892–34916 (2023)

Reference 15

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Observation 188d0d48-1917-452c-b061-2b06b01a31d4 · outbound

This paper cites In: European conference on computer vision.

Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery In: European conference on computer vision

Reference 16

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Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery Unresolved cited work

Reference 17

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Observation 44e9de48-b71a-4be9-b450-34a23b37226f · outbound

This paper cites In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing.

Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Reference 18

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Observation 9997b078-ba67-4cb6-bcfe-e8bd7db88011 · outbound

This paper cites Scientific data10(1) (2023).

Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery Scientific data10(1) (2023)

Reference 19

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Observation 98616e40-6e4f-4030-97a7-b1e718e38f1d · outbound

This paper cites IEEE Access9, 89644–89654 (2021).

Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery IEEE Access9, 89644–89654 (2021)

Reference 20

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Observation 99a39684-4e80-458f-9afe-9ba5bfd274dd · outbound

This paper cites Real-Time Flying Object Detection with YOLOv8.

Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery Real-Time Flying Object Detection with YOLOv8

Reference 21

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Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery In: International Conference on Medical image computing and computer-assisted intervention

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Observation f9e28746-08c9-4120-be69-14c382ce2929 · outbound

This paper cites Frontiers in Earth Science11(2023).

Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery Frontiers in Earth Science11(2023)

Reference 23

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This paper cites arXiv preprint arXiv:2509.25164 (2025).

Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery arXiv preprint arXiv:2509.25164 (2025)

Reference 24

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This paper cites Information Fusion126, 103575 (2026).

Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery Information Fusion126, 103575 (2026)

Reference 25

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This paper cites DRespNeT: A UAV Dataset and YOLOv8-DRN Model for Aerial Instance Segmentation of Building Access Points for Post-Earthquake Search-and-Rescue Missions.

Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery DRespNeT: A UAV Dataset and YOLOv8-DRN Model for Aerial Instance Segmentation of Building Access Points for Post-Earthquake Search-and-Rescue Missions

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Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery In: Proceedings of the Computer Vision and Pattern Recognition Conference

Reference 27

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Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery In: Findings of the Association for Computational Linguistics: ACL 2023

Reference 28

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Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery Qwen3 Technical Report

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Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery Unresolved cited work

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This paper cites In: The Thirty-ninth Annual Conference on Neural Information Processing Systems Datasets and Benchmarks Track (2025).

Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery In: The Thirty-ninth Annual Conference on Neural Information Processing Systems Datasets and Benchmarks Track (2025)

Reference 31

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This paper cites InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency.

Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency

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Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery IEEE transactions on pattern analysis and machine intelligence45(4), 4768–4781 (2022)

Reference 33

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This paper cites International Journal of Computer Vi- sion133(2), 825–843 (2025).

Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery International Journal of Computer Vi- sion133(2), 825–843 (2025)

Reference 34

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Observation cdc343dd-349b-44b8-8854-aaac89962a93 · outbound

This paper cites In: European Conference on Computer Vision.

Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery In: European Conference on Computer Vision

Reference 35

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Pith citing papers

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