Paper Citation Record · LEDGER

Can We Predict the Unpredictable? Leveraging DisasterNet-LLM for Multimodal Disaster Classification

As of 18 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2506.23462.

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

pith.paper-citation-record.v1
2506.23462 v1

Coverage vector

measured 23 of 23 reference resolution

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measured 23 of 23 standing notices

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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

23 of 23 outbound references displayed

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External citation measurements

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Outbound references

Observation a6a5950e-889c-4b49-821b-7b12f6491e5e · outbound

This paper cites Toward scalable damage assessment for rapid disaster re- sponse,.

Can We Predict the Unpredictable? Leveraging DisasterNet-LLM for Multimodal Disaster Classification Toward scalable damage assessment for rapid disaster re- sponse,

Reference 1

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Observation 016a87ae-93d9-4e58-ba64-fb5092a28b52 · outbound

This paper cites Uncertainty- aware 2d/3d change detection for natural disaster response,.

Can We Predict the Unpredictable? Leveraging DisasterNet-LLM for Multimodal Disaster Classification Uncertainty- aware 2d/3d change detection for natural disaster response,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:45:53.456486Z

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Observation f0e283d9-a772-4812-ab94-3b7627345da8 · outbound

This paper cites Social media for emergency rescue: An analysis of rescue requests on twitter during hurricane har- vey,.

Can We Predict the Unpredictable? Leveraging DisasterNet-LLM for Multimodal Disaster Classification Social media for emergency rescue: An analysis of rescue requests on twitter during hurricane har- vey,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:45:53.310144Z

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Observation 1adae68e-9233-489d-8e98-dc9409059b7a · outbound

This paper cites Detecting natural hazard-related disaster impacts with social media analyt- ics: the case of australian states and territories,.

Can We Predict the Unpredictable? Leveraging DisasterNet-LLM for Multimodal Disaster Classification Detecting natural hazard-related disaster impacts with social media analyt- ics: the case of australian states and territories,

Reference 4

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Observation 83ff6a7f-f31a-4ee0-9010-6c730ba53610 · outbound

This paper cites Venice was flooding... one tweet at a time,.

Can We Predict the Unpredictable? Leveraging DisasterNet-LLM for Multimodal Disaster Classification Venice was flooding... one tweet at a time,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:45:52.886710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e0d1e47c-c128-4b6c-b7fe-95fceab5d5f7 · outbound

This paper cites Damage assessment from social media imagery data during disasters,.

Can We Predict the Unpredictable? Leveraging DisasterNet-LLM for Multimodal Disaster Classification Damage assessment from social media imagery data during disasters,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:45:52.729314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 58010a18-4deb-41d3-b796-c06f83c72895 · outbound

This paper cites Enhancing disaster response with automated text information extraction from social media images,.

Can We Predict the Unpredictable? Leveraging DisasterNet-LLM for Multimodal Disaster Classification Enhancing disaster response with automated text information extraction from social media images,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:45:52.558504Z

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Observation 3e793172-55c4-414e-9073-e8eba043fde2 · outbound

This paper cites Crowd4ems: A crowdsourcing platform for gathering and geolocating social media content in disaster response,.

Can We Predict the Unpredictable? Leveraging DisasterNet-LLM for Multimodal Disaster Classification Crowd4ems: A crowdsourcing platform for gathering and geolocating social media content in disaster response,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:45:52.337406Z

Source-reported events for the cited work

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Observation fdb300f1-b27c-4619-9797-f27e1b75e96e · outbound

This paper cites Disaster early warning and damage as- sessment analysis using social media data and geo-location information,.

Can We Predict the Unpredictable? Leveraging DisasterNet-LLM for Multimodal Disaster Classification Disaster early warning and damage as- sessment analysis using social media data and geo-location information,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:45:52.149261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f4f28dbf-e60c-405b-80ae-fe8da0c35cde · outbound

This paper cites Domain knowledge- aware remote sensing foundation model for flood detection in multi-spectral imagery,.

Can We Predict the Unpredictable? Leveraging DisasterNet-LLM for Multimodal Disaster Classification Domain knowledge- aware remote sensing foundation model for flood detection in multi-spectral imagery,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:45:52.017414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 463a28ff-b972-4f14-867e-c17e2e74a1c3 · outbound

This paper cites Spacenet 8: Winning approaches to multi-class feature segmentation from satellite imagery for flood disasters,.

Can We Predict the Unpredictable? Leveraging DisasterNet-LLM for Multimodal Disaster Classification Spacenet 8: Winning approaches to multi-class feature segmentation from satellite imagery for flood disasters,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:45:51.895693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cebfd772-36bd-4936-9876-fa06b209d454 · outbound

This paper cites Large language model applications for evaluation: Opportunities and ethical implications,.

Can We Predict the Unpredictable? Leveraging DisasterNet-LLM for Multimodal Disaster Classification Large language model applications for evaluation: Opportunities and ethical implications,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:45:51.714117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1c188c1a-e018-4b26-bcbc-79d5af1d2921 · outbound

This paper cites Parameter-efficient fine-tuning of large-scale pre-trained language models,.

Can We Predict the Unpredictable? Leveraging DisasterNet-LLM for Multimodal Disaster Classification Parameter-efficient fine-tuning of large-scale pre-trained language models,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:45:51.567272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 226dcef1-c19a-4c8e-ae20-fce00d72522b · outbound

This paper cites Knowledge injection to counter large language model (llm) hallucination,.

Can We Predict the Unpredictable? Leveraging DisasterNet-LLM for Multimodal Disaster Classification Knowledge injection to counter large language model (llm) hallucination,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:45:51.416013Z

Source-reported events for the cited work

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Observation e2abc24b-0a09-4e23-ad8e-5ec3c1a3ebc8 · outbound

This paper cites Health system-scale language models are all-purpose predic- tion engines,.

Can We Predict the Unpredictable? Leveraging DisasterNet-LLM for Multimodal Disaster Classification Health system-scale language models are all-purpose predic- tion engines,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:45:51.299784Z

Source-reported events for the cited work

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Observation 422b9e3b-50ce-4928-ab3a-5a1b139046c1 · outbound

This paper cites Fine-tuning gpt-3 for legal rule classification,.

Can We Predict the Unpredictable? Leveraging DisasterNet-LLM for Multimodal Disaster Classification Fine-tuning gpt-3 for legal rule classification,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:45:51.114244Z

Source-reported events for the cited work

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Observation c7f3a0a1-d2e7-4232-9af9-36defe4980be · outbound

This paper cites Dsqa-llm: domain- specific intelligent question answering based on large language model,.

Can We Predict the Unpredictable? Leveraging DisasterNet-LLM for Multimodal Disaster Classification Dsqa-llm: domain- specific intelligent question answering based on large language model,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:45:50.991671Z

Source-reported events for the cited work

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Observation 80f66ef7-3b12-447c-92d2-5deb5b040394 · outbound

This paper cites Enhancing emergency decision-making with knowledge graphs and large language models,.

Can We Predict the Unpredictable? Leveraging DisasterNet-LLM for Multimodal Disaster Classification Enhancing emergency decision-making with knowledge graphs and large language models,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:45:50.819596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 79f03428-6d00-4029-9fb1-4e7182fea599 · outbound

This paper cites Neural networks for geospatial data,.

Can We Predict the Unpredictable? Leveraging DisasterNet-LLM for Multimodal Disaster Classification Neural networks for geospatial data,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:45:50.645435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 157b84ca-68d6-4d4c-b860-ea28be996e53 · outbound

This paper cites A novel disaster image data-set and characteristics analysis using attention model,.

Can We Predict the Unpredictable? Leveraging DisasterNet-LLM for Multimodal Disaster Classification A novel disaster image data-set and characteristics analysis using attention model,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:45:50.479797Z

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Observation b53bb8d1-b29d-4995-bbbb-f0399a7b7660 · outbound

This paper cites Medic: a multi-task learning dataset for disaster image classification,.

Can We Predict the Unpredictable? Leveraging DisasterNet-LLM for Multimodal Disaster Classification Medic: a multi-task learning dataset for disaster image classification,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:45:50.340509Z

Source-reported events for the cited work

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Observation c2ad4f11-0c2d-4365-8ba0-0d1b68805e2a · outbound

This paper cites The era5 global reanalysis,.

Can We Predict the Unpredictable? Leveraging DisasterNet-LLM for Multimodal Disaster Classification The era5 global reanalysis,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:45:50.215358Z

Source-reported events for the cited work

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Observation 8afe6ece-2124-4a94-8ae0-e42b4282a400 · outbound

This paper cites CLLMate: A Multimodal Benchmark for Weather and Climate Events Forecasting.

Can We Predict the Unpredictable? Leveraging DisasterNet-LLM for Multimodal Disaster Classification CLLMate: A Multimodal Benchmark for Weather and Climate Events Forecasting

Reference 23

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Source-reported events for the cited work

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Pith citing papers

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