Paper Citation Record · LEDGER

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake

As of 21 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2506.22338.

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pith.paper-citation-record.v1
2506.22338 v1

Coverage vector

measured 35 of 35 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-06T22:11:36.010881Z

measured 35 of 35 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 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

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 18d7e220-9fc4-4270-9c3e-6a7370435608 · outbound

This paper cites Remote Sensing and Earthquake Damage Assessment: Experiences, Limits, and Perspectives,.

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake Remote Sensing and Earthquake Damage Assessment: Experiences, Limits, and Perspectives,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:43.202137Z

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 36005f77-b498-4909-8b27-0f236ec26f36 · outbound

This paper cites A comprehensive review of earthquake-induced building damage detection with remote sensing techniques,.

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake A comprehensive review of earthquake-induced building damage detection with remote sensing techniques,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:42.873100Z

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 036d29cc-ebcd-42ba-9674-675c13f9f43d · outbound

This paper cites Earthquake Damage As- sessment of Buildings Using VHR Optical and SAR Imagery,.

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake Earthquake Damage As- sessment of Buildings Using VHR Optical and SAR Imagery,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:42.586251Z

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 50a2bde7-1b68-41d8-8aca-9ef86b7b3f50 · outbound

This paper cites Assessment of Seismic Building Vulnerability from Space,.

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake Assessment of Seismic Building Vulnerability from Space,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:42.334464Z

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 36ebfc19-755d-4ec7-9613-94235302c499 · outbound

This paper cites A Comparative Study of Texture and Convolutional Neural Network Features for Detecting Collapsed Buildings After Earthquakes Using Pre- and Post-Event Satellite Imagery,.

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake A Comparative Study of Texture and Convolutional Neural Network Features for Detecting Collapsed Buildings After Earthquakes Using Pre- and Post-Event Satellite Imagery,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:42.098257Z

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 c1ffe788-a5e0-4af6-8f48-d26b8835e95f · outbound

This paper cites xBD: A Dataset for Assessing Building Damage from Satellite Imagery.

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake xBD: A Dataset for Assessing Building Damage from Satellite Imagery

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e94f079-37d0-4e78-be38-6aa3ed3c55dd · outbound

This paper cites Multi-Hazard and Spatial Transferability of a CNN for Automated Building Damage Assessment,.

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake Multi-Hazard and Spatial Transferability of a CNN for Automated Building Damage Assessment,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:41.850978Z

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 1e9d8cde-10dc-4c6b-9eda-25103368bb9b · outbound

This paper cites Large-scale building damage assessment using a novel hierarchical transformer ar- chitecture on satellite images,.

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake Large-scale building damage assessment using a novel hierarchical transformer ar- chitecture on satellite images,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:41.432144Z

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 57bf0a17-d09a-43b9-8ca8-6fd342b9f7a0 · outbound

This paper cites Automated detection of damaged buildings in post-disaster scenarios: a case study of Kahramanmaras ¸ (T¨urkiye) earthquakes on February 6, 2023,.

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake Automated detection of damaged buildings in post-disaster scenarios: a case study of Kahramanmaras ¸ (T¨urkiye) earthquakes on February 6, 2023,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:41.077319Z

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 e509d20e-e841-4424-8d49-6eebeb5cbaed · outbound

This paper cites Deep Learning for Building Damage Assessment of the 2023 Turkey Earthquakes,.

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake Deep Learning for Building Damage Assessment of the 2023 Turkey Earthquakes,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:40.743197Z

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 f748dcd3-61ac-4334-8c14-020229e708c1 · outbound

This paper cites Evaluating Deep Learning Based Building Damage Assessment Methods in Densely Built-up Urban Areas: The Case of Kahramanmaras ¸,.

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake Evaluating Deep Learning Based Building Damage Assessment Methods in Densely Built-up Urban Areas: The Case of Kahramanmaras ¸,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:40.687195Z

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 e09a4ae2-b928-49b1-b137-bb163de88b61 · outbound

This paper cites Deep En- semble Learning for Rapid Large-Scale Postearthquake Damage As- sessment: Application to Satellite Images from the 2023 T ¨urkiye Earth- quakes,.

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake Deep En- semble Learning for Rapid Large-Scale Postearthquake Damage As- sessment: Application to Satellite Images from the 2023 T ¨urkiye Earth- quakes,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:40.489525Z

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 1c94fcab-824b-48f2-b9d0-1d14e0b601be · outbound

This paper cites Evaluation of Deep Learning Models for Building Damage Mapping in Emergency Response Settings,.

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake Evaluation of Deep Learning Models for Building Damage Mapping in Emergency Response Settings,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:40.270625Z

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 cc6516e6-207f-40f8-8b75-ebb2dc275281 · outbound

This paper cites Evaluating Urban Building Damage of 2023 Kahramanmaras, Turkey Earthquake Sequence Using SAR Change Detection,.

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake Evaluating Urban Building Damage of 2023 Kahramanmaras, Turkey Earthquake Sequence Using SAR Change Detection,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:40.077943Z

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 301c8776-3fe5-4547-8e55-bac5b0ac3406 · outbound

This paper cites The EEFIT Remote Sensing Reconnaissance Mission for the February 2023 Turkey Earthquakes,.

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake The EEFIT Remote Sensing Reconnaissance Mission for the February 2023 Turkey Earthquakes,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:39.916175Z

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 00a4ee98-ca0e-457f-8110-eaded6e62fc6 · outbound

This paper cites Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives,.

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:39.805321Z

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 6d0f62f6-fbc2-4f40-be10-ba720daf06c5 · outbound

This paper cites Change Detection in Heterogeneous Optical and SAR Remote Sensing Images Via Deep Homogeneous Feature Fusion,.

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake Change Detection in Heterogeneous Optical and SAR Remote Sensing Images Via Deep Homogeneous Feature Fusion,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:39.679048Z

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 adf9db05-9ff1-4341-9deb-fd8cedbc7ec1 · outbound

This paper cites CD- TransUNet: A Hybrid Transformer Network for the Change Detection of Urban Buildings Using L-Band SAR Images,.

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake CD- TransUNet: A Hybrid Transformer Network for the Change Detection of Urban Buildings Using L-Band SAR Images,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:39.536079Z

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 2fd7af22-03c1-4e22-a1f9-c3753da981d1 · outbound

This paper cites An open-source tool for mapping war destruction at scale in Ukraine using Sentinel-1 time series,.

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake An open-source tool for mapping war destruction at scale in Ukraine using Sentinel-1 time series,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:39.375010Z

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 e70a2860-ebf8-4d59-b736-544e052a287f · outbound

This paper cites Detection of Earthquake-Induced Building Damages Using Polarimetric SAR Data,.

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake Detection of Earthquake-Induced Building Damages Using Polarimetric SAR Data,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:39.184658Z

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 8d0a2120-2336-4900-9523-b483de1c7079 · outbound

This paper cites Detection of Damaged Buildings Using Temporal SAR Data with Different Observation Modes,.

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake Detection of Damaged Buildings Using Temporal SAR Data with Different Observation Modes,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:38.844005Z

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 65434e74-e28a-4c13-b29a-41828dbd8a77 · outbound

This paper cites Optical-to-SAR Translation Based on CDA-GAN for High-Quality Training Sample Generation for Ship Detection in SAR Amplitude Images,.

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake Optical-to-SAR Translation Based on CDA-GAN for High-Quality Training Sample Generation for Ship Detection in SAR Amplitude Images,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:38.641269Z

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 c0219ad3-8ed6-42b1-a8a3-2acf9d3fbffe · outbound

This paper cites TSGAN: An Optical-to-SAR Dual Conditional GAN for Optical based SAR Temporal Shifting.

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake TSGAN: An Optical-to-SAR Dual Conditional GAN for Optical based SAR Temporal Shifting

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:11:36.926209Z

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 13c79d2c-7748-4970-8467-9e6baa997eb2 · outbound

This paper cites Unsupervised Domain Adaptation Based on Progressive Transfer for Ship Detection: From Optical to SAR Images,.

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake Unsupervised Domain Adaptation Based on Progressive Transfer for Ship Detection: From Optical to SAR Images,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:38.527652Z

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 88ffa4ed-f489-405c-8b5e-0efb048408ae · outbound

This paper cites BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response,.

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response,

Reference 25

Resolution: verified exact; raw_fallback, observed 2026-08-06T22:11:36.640797Z

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 df1e7ac3-8339-4e54-b1c8-27ffa4af6873 · outbound

This paper cites Global building exposure model for earthquake risk assessment,.

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake Global building exposure model for earthquake risk assessment,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:38.351194Z

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 48b5c1e8-1721-4743-ba44-fa6edc29db3a · outbound

This paper cites QuickQuakeBuildings: Post- Earthquake SAR-Optical Dataset for Quick Damaged-Building Detec- tion,.

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake QuickQuakeBuildings: Post- Earthquake SAR-Optical Dataset for Quick Damaged-Building Detec- tion,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:38.230756Z

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 a955ea32-8681-4d6b-9d63-d3c1d0923c00 · outbound

This paper cites Earthquake building damage detection based on synthetic-aperture-radar imagery and machine learning,.

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake Earthquake building damage detection based on synthetic-aperture-radar imagery and machine learning,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:38.063052Z

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 484b4bc5-b647-481c-9f25-d8a1df462096 · outbound

This paper cites COSMO-SkyMed an existing opportunity for observing the Earth,.

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake COSMO-SkyMed an existing opportunity for observing the Earth,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:37.900928Z

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 68564ae8-401a-48d6-9eff-450d9174b6c7 · outbound

This paper cites COSMO-SkyMed Mission and Products Description,.

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake COSMO-SkyMed Mission and Products Description,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:37.744191Z

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 28eb30b1-fe47-48cf-a577-66aa65a51e47 · outbound

This paper cites Macroseismic and mechanical models for the vulnerability and damage assessment of current build- ings,.

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake Macroseismic and mechanical models for the vulnerability and damage assessment of current build- ings,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:37.527997Z

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 2735c027-58cd-44aa-af94-25d6d11d12f2 · outbound

This paper cites CG-Net: Conditional GIS-aware Network for Individual Building Segmentation in VHR SAR Images,.

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake CG-Net: Conditional GIS-aware Network for Individual Building Segmentation in VHR SAR Images,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:37.331654Z

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 4c9107be-0d64-4cfb-a2e5-7c6495db0b16 · outbound

This paper cites Member of Academic Senate and PhD Professors’ Board.

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake Member of Academic Senate and PhD Professors’ Board

Reference 1992

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:37.113664Z

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 e3a9090b-a8ab-4483-ba17-c3c6094f690e · outbound

This paper cites Available: https://doi.org/10.1007/s10518-006-9024-z.

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake Available: https://doi.org/10.1007/s10518-006-9024-z

Reference 2006

Resolution: verified exact; doi, observed 2026-08-06T22:11:36.300161Z

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 8c09daf2-445c-4677-92b0-3241ed1ee27e · outbound

This paper cites Available: https://www.mdpi.com/2072-4292/12/1/137.

A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake Available: https://www.mdpi.com/2072-4292/12/1/137

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:11:39.016475Z

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

No inbound Pith citation observations are available.