Paper Citation Record · LEDGER

Multi-Label Classification Framework for Hurricane Damage Assessment

As of 21 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 1 inbound Pith citation observation for arXiv:2507.02265.

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

pith.paper-citation-record.v1
2507.02265 v1

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:35:45.618338Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:29:51.778169Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-15T18:16:14.067578Z

Reference resolution

17 of 17 outbound references displayed

verified exact9
verified fuzzy0
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch5

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a492e9be-e252-4898-a4c8-bd40b2838629 · outbound

This paper cites Relation Network for Multilabel Aerial Image Classification.

Multi-Label Classification Framework for Hurricane Damage Assessment Relation Network for Multilabel Aerial Image Classification

Reference 6

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T20:35:49.230546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ab002d64-a92f-42ce-90f2-0a0efe6d2703 · outbound

This paper cites Post-disaster damage classification based on deep multi-view image fusion.

Multi-Label Classification Framework for Hurricane Damage Assessment Post-disaster damage classification based on deep multi-view image fusion

Reference 9

Resolution: verified exact; doi, observed 2026-08-06T20:35:46.153751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 000ba605-5bb4-4449-8c7e-e63d13a0d6f5 · outbound

This paper cites Mapping hurricane damage: A comparative analysis of satellite monitoring methods.

Multi-Label Classification Framework for Hurricane Damage Assessment Mapping hurricane damage: A comparative analysis of satellite monitoring methods

Reference 11

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T20:35:48.694981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4acf2075-b42a-4fba-9add-61b4ad7ece9c · outbound

This paper cites Global Context-Based Multilevel Feature Fusion Networks for Multilabel Remote Sensing Image Scene Classification.

Multi-Label Classification Framework for Hurricane Damage Assessment Global Context-Based Multilevel Feature Fusion Networks for Multilabel Remote Sensing Image Scene Classification

Reference 13

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T20:35:48.420174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 01c76ba2-6267-4195-afaf-d298de73e78f · outbound

This paper cites Building damage detection from satellite images after natural disasters on extremely imbalanced datasets.

Multi-Label Classification Framework for Hurricane Damage Assessment Building damage detection from satellite images after natural disasters on extremely imbalanced datasets

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T20:35:45.459026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34064ac4-16a9-4575-8139-32264efe38bc · outbound

This paper cites Mortality caused by tropical cyclones in the United States.

Multi-Label Classification Framework for Hurricane Damage Assessment Mortality caused by tropical cyclones in the United States

Reference 16

Resolution: verified exact; doi, observed 2026-08-06T20:35:45.799271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 35765d5a-d18d-4f9d-87f8-a47da4faecef · outbound

This paper cites Feature learning network with transformer for multi-label image classification.

Multi-Label Classification Framework for Hurricane Damage Assessment Feature learning network with transformer for multi-label image classification

Reference 17

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T20:35:48.018544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a862fd61-bc87-4aa7-8e8e-3ef98cb8e2a2 · outbound

This paper cites Wang, X., L.

Multi-Label Classification Framework for Hurricane Damage Assessment Wang, X., L

Reference 913

Resolution: unresolved; no resolver link, observed 2026-08-06T20:35:45.326097Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8217874e-ac00-4bf7-a135-fbd33a680f82 · outbound

This paper cites Deep Residual Learning for Image Recognition.

Multi-Label Classification Framework for Hurricane Damage Assessment Deep Residual Learning for Image Recognition

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-06T20:35:44.888855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca79df7c-9ee8-46fd-99a8-a38bdd7dea25 · outbound

This paper cites Structural analysis of the relationships between implementing tasks and ex-post effectiveness for disaster mitigation practice in Taipei City.

Multi-Label Classification Framework for Hurricane Damage Assessment Structural analysis of the relationships between implementing tasks and ex-post effectiveness for disaster mitigation practice in Taipei City

Reference 2017

Resolution: verified exact; doi, observed 2026-08-06T20:35:46.689146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c6426e5b-e6b6-485e-87ee-941bb7060613 · outbound

This paper cites Normalized hurricane damage in the continental United States 1900–2017.

Multi-Label Classification Framework for Hurricane Damage Assessment Normalized hurricane damage in the continental United States 1900–2017

Reference 2018

Resolution: verified exact; doi, observed 2026-08-06T20:35:45.975308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 188ca17b-2c56-4f53-bdeb-205172e281b8 · outbound

This paper cites Building damage annotation on post-hurricane satellite imagery based on convolutional neural networks.

Multi-Label Classification Framework for Hurricane Damage Assessment Building damage annotation on post-hurricane satellite imagery based on convolutional neural networks

Reference 2020

Resolution: verified exact; doi, observed 2026-08-06T20:35:46.960864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9020163b-d61b-4877-8068-9d7e1e7eef40 · outbound

This paper cites Hurricane Damage Detection using Machine Learning and Deep Learning Techniques: A Review.

Multi-Label Classification Framework for Hurricane Damage Assessment Hurricane Damage Detection using Machine Learning and Deep Learning Techniques: A Review

Reference 2021

Resolution: verified exact; doi, observed 2026-08-06T20:35:46.324092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 768e5c4c-a124-4200-929a-0b5cac9087da · outbound

This paper cites A novel attention-based deep learning method for post-disaster building damage classification.

Multi-Label Classification Framework for Hurricane Damage Assessment A novel attention-based deep learning method for post-disaster building damage classification

Reference 2022

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T20:35:49.013441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2de2551c-0044-4972-97b1-56c36db0f9c6 · outbound

This paper cites Increased U.S. coastal hurricane risk under climate change.

Multi-Label Classification Framework for Hurricane Damage Assessment Increased U.S. coastal hurricane risk under climate change

Reference 2023

Resolution: verified exact; doi, observed 2026-08-06T20:35:47.302589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 28661e70-7d88-4386-beb5-08586af139ef · outbound

This paper cites Global population profile of tropical cyclone exposure from 2002 to 2019.

Multi-Label Classification Framework for Hurricane Damage Assessment Global population profile of tropical cyclone exposure from 2002 to 2019

Reference 2024

Resolution: verified exact; doi, observed 2026-08-06T20:35:46.492619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ad459c6d-6ccb-4d68-9a58-0e4af013c3bc · outbound

This paper cites Balaguru, K., W.

Multi-Label Classification Framework for Hurricane Damage Assessment Balaguru, K., W

Reference 2344

Resolution: verified exact; doi, observed 2026-08-06T20:35:47.566174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

Observation 28f4a023-d702-4e15-b2fd-674748abe12c · inbound

FloodVision: Urban Flood Depth Estimation Using Foundation Vision-Language Models and Domain Knowledge Graph cites this paper.

FloodVision: Urban Flood Depth Estimation Using Foundation Vision-Language Models and Domain Knowledge Graph Multi-Label Classification Framework for Hurricane Damage Assessment

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-15T16:29:52.141825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.