Visual Prompt Learning of Foundation Models for Post-Disaster Damage Evaluation.
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| Title: | Visual Prompt Learning of Foundation Models for Post-Disaster Damage Evaluation. |
|---|---|
| Authors: | Zhao, Fei1 (AUTHOR), Zhang, Chengcui1,2 (AUTHOR) czhang02@uab.edu, Zhang, Runlin1,2 (AUTHOR), Wang, Tianyang1,2 (AUTHOR) |
| Source: | Remote Sensing. May2025, Vol. 17 Issue 10, p1664. 20p. |
| Subjects: | Visual learning, Remote-sensing images, Deep learning, Emergency management, Remote sensing |
| Abstract: | In response to the urgent need for rapid and precise post-disaster damage evaluation, this study introduces the Visual Prompt Damage Evaluation (ViPDE) framework, a novel contrastive learning-based approach that leverages the embedded knowledge within the Segment Anything Model (SAM) and pairs of remote sensing images to enhance building damage assessment. In this framework, we propose a learnable cascaded Visual Prompt Generator (VPG) that provides semantic visual prompts, guiding SAM to effectively analyze pre- and post-disaster image pairs and construct a nuanced representation of the affected areas at different stages. By keeping the foundation model's parameters frozen, ViPDE significantly enhances training efficiency compared with traditional full-model fine-tuning methods. This parameter-efficient approach reduces computational costs and accelerates deployment in emergency scenarios. Moreover, our model demonstrates robustness across diverse disaster types and geographic locations. Beyond mere binary assessments, our model distinguishes damage levels with a finer granularity, categorizing them on a scale from 1 (no damage) to 4 (destroyed). Extensive experiments validate the effectiveness of ViPDE, showcasing its superior performance over existing methods. Comparative evaluations demonstrate that ViPDE achieves an F1 score of 0.7014. This foundation model-based approach sets a new benchmark in disaster management. It also pioneers a new practical architectural paradigm for foundation model-based contrastive learning focused on specific objects of interest. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 185473491 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Visual Prompt Learning of Foundation Models for Post-Disaster Damage Evaluation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhao%2C+Fei%22">Zhao, Fei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Chengcui%22">Zhang, Chengcui</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> czhang02@uab.edu</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Runlin%22">Zhang, Runlin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Tianyang%22">Wang, Tianyang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. May2025, Vol. 17 Issue 10, p1664. 20p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Visual+learning%22">Visual learning</searchLink><br /><searchLink fieldCode="DE" term="%22Remote-sensing+images%22">Remote-sensing images</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Emergency+management%22">Emergency management</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In response to the urgent need for rapid and precise post-disaster damage evaluation, this study introduces the Visual Prompt Damage Evaluation (ViPDE) framework, a novel contrastive learning-based approach that leverages the embedded knowledge within the Segment Anything Model (SAM) and pairs of remote sensing images to enhance building damage assessment. In this framework, we propose a learnable cascaded Visual Prompt Generator (VPG) that provides semantic visual prompts, guiding SAM to effectively analyze pre- and post-disaster image pairs and construct a nuanced representation of the affected areas at different stages. By keeping the foundation model's parameters frozen, ViPDE significantly enhances training efficiency compared with traditional full-model fine-tuning methods. This parameter-efficient approach reduces computational costs and accelerates deployment in emergency scenarios. Moreover, our model demonstrates robustness across diverse disaster types and geographic locations. Beyond mere binary assessments, our model distinguishes damage levels with a finer granularity, categorizing them on a scale from 1 (no damage) to 4 (destroyed). Extensive experiments validate the effectiveness of ViPDE, showcasing its superior performance over existing methods. Comparative evaluations demonstrate that ViPDE achieves an F1 score of 0.7014. This foundation model-based approach sets a new benchmark in disaster management. It also pioneers a new practical architectural paradigm for foundation model-based contrastive learning focused on specific objects of interest. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs17101664 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 1664 Subjects: – SubjectFull: Visual learning Type: general – SubjectFull: Remote-sensing images Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Emergency management Type: general – SubjectFull: Remote sensing Type: general Titles: – TitleFull: Visual Prompt Learning of Foundation Models for Post-Disaster Damage Evaluation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhao, Fei – PersonEntity: Name: NameFull: Zhang, Chengcui – PersonEntity: Name: NameFull: Zhang, Runlin – PersonEntity: Name: NameFull: Wang, Tianyang IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 05 Text: May2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 17 – Type: issue Value: 10 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
