Paper Citation Record · LEDGER

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning

As of 20 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2412.10756.

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

pith.paper-citation-record.v1
2412.10756 v1

Coverage vector

measured 37 of 37 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Source: cited_works

Reference resolution

37 of 37 outbound references displayed

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External citation measurements

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Outbound references

Observation c0424df8-31d7-4b51-b484-7c1f9e9a0fb1 · outbound

This paper cites WMO Atlas of mortal- ity and economic losses from weather, climate, and water extremes (1970–2019),.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning WMO Atlas of mortal- ity and economic losses from weather, climate, and water extremes (1970–2019),

Reference 1

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Observation 530a0abb-9521-4905-83a0-ef7d947a35ea · outbound

This paper cites GAR special report 2023: Mapping resilience for the sustainable development goals,.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning GAR special report 2023: Mapping resilience for the sustainable development goals,

Reference 2

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Observation 579fbc87-87b0-4a1c-8350-3ea246c3aa06 · outbound

This paper cites Solutions for sus- tainable and resilient communication infrastructure in disaster relief and management scenarios,.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning Solutions for sus- tainable and resilient communication infrastructure in disaster relief and management scenarios,

Reference 3

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Observation 3435a7ef-f989-4a7b-9baa-e64fe4b75e60 · outbound

This paper cites Onboard radar processor development for rapid response to natural hazards,.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning Onboard radar processor development for rapid response to natural hazards,

Reference 4

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Observation 8055df9b-04e5-4ec4-aa65-b363dea7c0a7 · outbound

This paper cites UA V computing-assisted search and rescue mission framework for disaster and harsh environment mitigation,.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning UA V computing-assisted search and rescue mission framework for disaster and harsh environment mitigation,

Reference 5

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Observation 11ccf8b3-7109-4546-9934-709a5b5f28c5 · outbound

This paper cites A uav-assisted edge framework for real-time disaster management,.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning A uav-assisted edge framework for real-time disaster management,

Reference 6

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Observation 2d67c5f9-0ba7-46e2-a281-6f2010f48d6b · outbound

This paper cites Ugen: Uav and gan-aided ensemble network for post-disaster survivor detection through oran,.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning Ugen: Uav and gan-aided ensemble network for post-disaster survivor detection through oran,

Reference 7

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Observation b59cb219-f950-4500-bbe5-720cf7bc26c7 · outbound

This paper cites Semantic communications: Overview, open issues, and future research directions,.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning Semantic communications: Overview, open issues, and future research directions,

Reference 8

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Observation 35187f8b-4cc8-4e0c-ae75-1319222d7e98 · outbound

This paper cites Wireless end-to-end image transmission system using semantic communications,.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning Wireless end-to-end image transmission system using semantic communications,

Reference 9

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Observation 4b12e636-92bb-4f62-8b00-cea568dc10b1 · outbound

This paper cites Semantic segmentation for high spatial resolution remote sensing images based on convolution neural network and pyramid pooling module,.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning Semantic segmentation for high spatial resolution remote sensing images based on convolution neural network and pyramid pooling module,

Reference 10

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Observation 1ab7450e-764d-42d8-b874-9be5f80ff2a2 · outbound

This paper cites A sim2real deep learning ap- proach for the transformation of images from multiple vehicle-mounted cameras to a semantically segmented image in bird’s eye view,.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning A sim2real deep learning ap- proach for the transformation of images from multiple vehicle-mounted cameras to a semantically segmented image in bird’s eye view,

Reference 11

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Observation d20c171f-53a5-4ea4-8a3d-3aca859eae67 · outbound

This paper cites Lightweight disaster semantic segmentation for uav on-device intelligence,.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning Lightweight disaster semantic segmentation for uav on-device intelligence,

Reference 12

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Observation b505cac2-6393-496a-980a-eff70ef6d6e6 · outbound

This paper cites Floodnet: A high resolution aerial imagery dataset for post flood scene understanding,.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning Floodnet: A high resolution aerial imagery dataset for post flood scene understanding,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:42:22.773536Z

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Observation b8b441d8-f10d-45e2-bbb3-e77e8fe18476 · outbound

This paper cites Rescuenet: A high resolution UA V semantic segmentation dataset for natural disaster damage assessment,.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning Rescuenet: A high resolution UA V semantic segmentation dataset for natural disaster damage assessment,

Reference 14

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Observation 548b6d53-bd00-468c-aeb6-ee29c1710b52 · outbound

This paper cites Lsar: Multi-uav col- laboration for search and rescue missions,.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning Lsar: Multi-uav col- laboration for search and rescue missions,

Reference 15

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Observation b169cf9a-17c0-4041-85c5-aef1f7b97aea · outbound

This paper cites Uav aerial imaging applications for post-disaster assessment, environmental management and infrastructure development,.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning Uav aerial imaging applications for post-disaster assessment, environmental management and infrastructure development,

Reference 16

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Observation 375228ea-ffdb-4a39-a7d5-c4a19f4060a7 · outbound

This paper cites Uav-based real-time survivor detection system in post-disaster search and rescue operations,.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning Uav-based real-time survivor detection system in post-disaster search and rescue operations,

Reference 18

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Observation 8052abb5-805c-4bf4-bf82-d80aafb67a7a · outbound

This paper cites Drones4good: Supporting disaster relief through remote sens- ing and ai,.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning Drones4good: Supporting disaster relief through remote sens- ing and ai,

Reference 19

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Observation d2026c30-ded3-463b-a689-0e06c995e1b3 · outbound

This paper cites Deepdamagenet: A two-step deep-learning model for multi-disaster building damage segmentation and classification using satellite imagery,.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning Deepdamagenet: A two-step deep-learning model for multi-disaster building damage segmentation and classification using satellite imagery,

Reference 20

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Observation 38933a72-5a6e-4f88-a58a-d7f488506d87 · outbound

This paper cites Transferring cnn with adaptive learning for remote sensing scene classification,.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning Transferring cnn with adaptive learning for remote sensing scene classification,

Reference 21

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Observation a3fc2446-2750-42f9-915d-629ee413ea44 · outbound

This paper cites Deep-learning-based aerial image classification for emergency response applications using unmanned aerial vehicles,.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning Deep-learning-based aerial image classification for emergency response applications using unmanned aerial vehicles,

Reference 22

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Observation 184f6270-ec1b-4871-952e-4661eefe667c · outbound

This paper cites TinyVQA: Compact Multimodal Deep Neural Network for Visual Question Answering on Resource-Constrained Devices.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning TinyVQA: Compact Multimodal Deep Neural Network for Visual Question Answering on Resource-Constrained Devices

Reference 23

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Observation 5a650ed3-9f51-4322-81d6-87f58f3b6544 · outbound

This paper cites Emergencynet: Efficient aerial image classification for drone-based emergency monitoring using atrous con- volutional feature fusion,.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning Emergencynet: Efficient aerial image classification for drone-based emergency monitoring using atrous con- volutional feature fusion,

Reference 24

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Observation 3d26a2a3-e584-428c-9574-4c0b180df575 · outbound

This paper cites Visual AI for satellite imagery perspective: A visual question answering frame- work in the geospatial domain,.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning Visual AI for satellite imagery perspective: A visual question answering frame- work in the geospatial domain,

Reference 25

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Observation 64b3141b-4792-4e03-800b-50b99c9c8075 · outbound

This paper cites SAM-VQA: Supervised attention-based visual question answering model for post-disaster damage assessment on remote sensing imagery,.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning SAM-VQA: Supervised attention-based visual question answering model for post-disaster damage assessment on remote sensing imagery,

Reference 26

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Observation d0dcef4d-8947-4044-b4f1-69aff9fe32b8 · outbound

This paper cites Unsupervised learning of image segmentation based on differentiable feature clustering,.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning Unsupervised learning of image segmentation based on differentiable feature clustering,

Reference 27

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This paper cites Learning enriched features for fast image restoration and enhancement,.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning Learning enriched features for fast image restoration and enhancement,

Reference 28

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This paper cites Semantic communication systems for speech transmission,.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning Semantic communication systems for speech transmission,

Reference 29

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Observation cd1a7a0e-1eff-4e63-afde-6b9845b236bb · outbound

This paper cites Context-based semantic communication via dynamic programming,.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning Context-based semantic communication via dynamic programming,

Reference 30

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Observation c0cce823-86a1-4298-bfd4-82b9a0ffeff0 · outbound

This paper cites UA V image high fidelity compression algorithm based on generative adversarial networks under complex disaster conditions,.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning UA V image high fidelity compression algorithm based on generative adversarial networks under complex disaster conditions,

Reference 31

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Observation 8184fae6-04d9-4372-b108-3cc548670c35 · outbound

This paper cites Blessemflood21: Advancing flood analysis with a high-resolution georeferenced dataset for humanitarian aid support,.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning Blessemflood21: Advancing flood analysis with a high-resolution georeferenced dataset for humanitarian aid support,

Reference 32

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Observation 174b113e-764e-48ea-ac83-16d4f65a6791 · outbound

This paper cites Pyramid scene parsing network,.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning Pyramid scene parsing network,

Reference 33

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Observation 33c5657c-dc5e-4c83-a2d4-1ec1933cdb96 · outbound

This paper cites Deep residual learning for image recognition,.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning Deep residual learning for image recognition,

Reference 34

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Observation 7d1647bc-da45-43ca-82a3-f5460f787bf3 · outbound

This paper cites An Efficient Modern Baseline for FloodNet VQA.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning An Efficient Modern Baseline for FloodNet VQA

Reference 35

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Observation 42c7be6b-1291-4f8e-a923-0cd8fb9df34d · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 36

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Observation 936103dc-91cd-46be-b515-ba55ec099572 · outbound

This paper cites Minimizing maximum latency of task offloading for multi-UA V-assisted maritime search and rescue,.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning Minimizing maximum latency of task offloading for multi-UA V-assisted maritime search and rescue,

Reference 37

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Observation 09e95471-99bc-4268-bd2d-aa8f5ffe8c8b · outbound

This paper cites Efficient uavs deployment and resource allocation in uav-relay assisted public safety networks for video transmission,.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning Efficient uavs deployment and resource allocation in uav-relay assisted public safety networks for video transmission,

Reference 38

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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