Paper Citation Record · LEDGER

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge

As of 4 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2607.22746.

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

pith.paper-citation-record.v1
2607.22746 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T08:52:12.501657Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+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

36 of 36 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b9893dd7-efe4-474e-9aeb-ad171b139ebc · outbound

This paper cites Global trends in satellite-based emergency mapping,.

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge Global trends in satellite-based emergency mapping,

Reference 1

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Observation 0478ca47-9241-4027-8d63-f32bde4d21e5 · outbound

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

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge A comprehensive review of earthquake-induced building damage detection with remote sensing techniques,

Reference 2

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Observation 65133f51-6a6b-4dfa-a39a-52ff82459e7a · outbound

This paper cites Creating xBD: A dataset for assessing building damage from satellite imagery,.

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge Creating xBD: A dataset for assessing building damage from satellite imagery,

Reference 3

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Observation d9147f0b-1285-4fbb-853c-5b8d2b21bde9 · outbound

This paper cites Rapid damage assessment by means of multi-temporal SAR – a comprehensive review and outlook to Sentinel-1,.

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge Rapid damage assessment by means of multi-temporal SAR – a comprehensive review and outlook to Sentinel-1,

Reference 4

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Observation 46313228-8974-4161-b613-ff3a6ce07507 · outbound

This paper cites ETCI 2021 competition on flood detection,.

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge ETCI 2021 competition on flood detection,

Reference 5

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Observation ef2bf3e5-976c-4f35-b39d-968cfc7d56a2 · outbound

This paper cites SpaceNet 8 – the detection of flooded roads and buildings,.

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge SpaceNet 8 – the detection of flooded roads and buildings,

Reference 6

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Observation f7e78b2c-e335-4892-b2e7-f323e084d844 · outbound

This paper cites Land- slide4Sense: Reference benchmark data and deep learning models for landslide detection,.

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge Land- slide4Sense: Reference benchmark data and deep learning models for landslide detection,

Reference 7

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Observation 2799f85f-8648-4268-bb10-2777b7e2c007 · outbound

This paper cites The outcome of the 2022 Landslide4Sense competition: Advanced landslide detection from multisource satellite imagery,.

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge The outcome of the 2022 Landslide4Sense competition: Advanced landslide detection from multisource satellite imagery,

Reference 8

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Observation 24c3d399-4853-4acb-bb65-089bea917590 · outbound

This paper cites Large-scale fine-grained building classifica- tion and height estimation for semantic urban reconstruction: Outcome of the 2023 ieee grss data fusion contest,.

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge Large-scale fine-grained building classifica- tion and height estimation for semantic urban reconstruction: Outcome of the 2023 ieee grss data fusion contest,

Reference 9

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Observation c526313f-bd2c-4a9d-ab6e-ce52289da485 · outbound

This paper cites Artificial intelli- gence for earthquake response: Outcomes and insights from a global spaceborne rapid mapping challenge,.

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge Artificial intelli- gence for earthquake response: Outcomes and insights from a global spaceborne rapid mapping challenge,

Reference 10

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Observation 6e8d41b6-2985-465d-a96b-24629870b6a1 · outbound

This paper cites 2025 IEEE GRSS data fusion contest: All-weather land cover and building damage mapping,.

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge 2025 IEEE GRSS data fusion contest: All-weather land cover and building damage mapping,

Reference 11

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Observation d3e18af9-dafe-4895-be52-9524d8ed794e · outbound

This paper cites All-weather land cover and building damage mapping: Outcome of the 2025 IEEE GRSS data fusion contest,.

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge All-weather land cover and building damage mapping: Outcome of the 2025 IEEE GRSS data fusion contest,

Reference 12

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Observation 18a69b95-8b2b-436e-b58a-c22a48d5caf4 · outbound

This paper cites SpaceNet 6: Multi-sensor all weather mapping dataset,.

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge SpaceNet 6: Multi-sensor all weather mapping dataset,

Reference 13

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Observation ef2e7d56-7101-4aef-a99a-695d430385ab · outbound

This paper cites Spacenet 9—cross-sensor alignment of optical and sar imagery,.

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge Spacenet 9—cross-sensor alignment of optical and sar imagery,

Reference 14

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Observation a90ce57e-44ba-4887-8c00-692c827019a0 · outbound

This paper cites Report on the 2025 IEEE GRSS data fusion contest: All-weather land cover and building damage mapping,.

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge Report on the 2025 IEEE GRSS data fusion contest: All-weather land cover and building damage mapping,

Reference 15

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Observation f8e04999-5934-4b49-8b26-5f4df88f179e · outbound

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

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge Bright: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response,

Reference 16

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Observation 1eebea8d-3b11-4f58-9bfe-adf6fd741d96 · outbound

This paper cites Microsoft COCO: Common objects in context,.

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge Microsoft COCO: Common objects in context,

Reference 17

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Observation 6bf965b1-6895-465b-9259-6914ae3bd9e6 · outbound

This paper cites Mask r-cnn,.

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge Mask r-cnn,

Reference 18

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Observation 480730bc-96e6-44c2-9375-f82dbcc32140 · outbound

This paper cites Codabench: Flexible, easy-to-use, and reproducible meta- benchmark platform,.

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge Codabench: Flexible, easy-to-use, and reproducible meta- benchmark platform,

Reference 19

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Observation 867f77e1-a4fe-4206-83c2-244f9be178bf · outbound

This paper cites Building-guided pseudo-label learning for cross-modal building damage mapping,.

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge Building-guided pseudo-label learning for cross-modal building damage mapping,

Reference 20

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Observation 9e1a9a24-939b-4bac-a0cf-1c9145f0fad8 · outbound

This paper cites Foreground-aware relation network for geospatial object segmentation in high spatial resolution remote sensing imagery,.

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge Foreground-aware relation network for geospatial object segmentation in high spatial resolution remote sensing imagery,

Reference 21

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Observation 084d1384-c108-4a7d-b469-7a5ee73cf9c7 · outbound

This paper cites Toward complex backgrounds: A unified difference-aware decoder for binary segmentation,.

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge Toward complex backgrounds: A unified difference-aware decoder for binary segmentation,

Reference 22

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Observation ea6a6f02-2c92-4e4f-a1c8-68c88f2910c4 · outbound

This paper cites Progressive uncertainty- guided network for binary segmentation in high-resolution remote sens- ing imagery,.

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge Progressive uncertainty- guided network for binary segmentation in high-resolution remote sens- ing imagery,

Reference 23

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Observation d17a115f-d56e-495e-9f54-d7ff301ded0a · outbound

This paper cites Overcoming the uncertainty challenges in detecting building changes from remote sensing images,.

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge Overcoming the uncertainty challenges in detecting building changes from remote sensing images,

Reference 24

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Observation 51749799-2ec0-4887-bd78-ef292872b7ee · outbound

This paper cites Masked-attention mask transformer for universal image segmentation,.

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge Masked-attention mask transformer for universal image segmentation,

Reference 25

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Observation b2d87231-fb44-4587-9598-a71d7f4ddb19 · outbound

This paper cites Solov2: Dynamic and fast instance segmentation,.

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge Solov2: Dynamic and fast instance segmentation,

Reference 26

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Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge Yolov10: Real-time end-to-end object detection,

Reference 27

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This paper cites Yolo26: An analysis of nms-free end to end framework for real-time object detection,.

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge Yolo26: An analysis of nms-free end to end framework for real-time object detection,

Reference 28

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Observation 1356d68e-df93-4e78-b02f-2f08e0341822 · outbound

This paper cites Deep residual learning for image recognition,.

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge Deep residual learning for image recognition,

Reference 29

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Observation 00c9c0e7-0e24-4b49-93dd-7c4faee5acb5 · outbound

This paper cites Feature pyramid networks for object detection,.

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge Feature pyramid networks for object detection,

Reference 30

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Observation 3e4b2ee9-bd52-4c32-b53d-f0701ee7e4e1 · outbound

This paper cites Faster R-CNN: Towards real-time object detection with region proposal networks,.

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge Faster R-CNN: Towards real-time object detection with region proposal networks,

Reference 31

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Observation 355d0f15-ec93-438c-8821-d23ece9116a8 · outbound

This paper cites In search of lost domain generalization,.

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge In search of lost domain generalization,

Reference 32

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Observation 58f57fee-0269-431c-8eff-39191d1a2827 · outbound

This paper cites Earth observation for disaster mapping: Benchmarks, methods, challenges and future perspectives,.

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge Earth observation for disaster mapping: Benchmarks, methods, challenges and future perspectives,

Reference 33

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Observation 3ffef893-e82f-43aa-ae25-ff78b9d98793 · outbound

This paper cites Tent: Fully test-time adaptation by entropy minimization,.

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge Tent: Fully test-time adaptation by entropy minimization,

Reference 34

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This paper cites Pseudo-labeling and confirmation bias in deep semi-supervised learning,.

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge Pseudo-labeling and confirmation bias in deep semi-supervised learning,

Reference 35

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Observation dcdfbbfa-01ea-4432-9585-0028d5e67f63 · outbound

This paper cites Ultra-high- resolution sar and optical image registration: From global benchmark dataset to frequency-guided registration method,.

Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge Ultra-high- resolution sar and optical image registration: From global benchmark dataset to frequency-guided registration method,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-01T08:52:12.501657Z

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