Mapping Building Construction Year from Landsat in Data-Scarce, Cloud-Prone Regions: A Parsimonious Spatial Triage Tool for Physical Vulnerability Screening.
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| Title: | Mapping Building Construction Year from Landsat in Data-Scarce, Cloud-Prone Regions: A Parsimonious Spatial Triage Tool for Physical Vulnerability Screening. |
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| Authors: | Liu, Yang1 (AUTHOR), Zhang, Xuan1,2 (AUTHOR), Mo, Zewen1 (AUTHOR), Wang, Zhipang2 (AUTHOR), Zhang, Qingling1 (AUTHOR) zhangqling@mail.sysu.edu.cn |
| Source: | Remote Sensing. Jul2026, Vol. 18 Issue 13, p2135. 28p. |
| Subjects: | Normalized difference vegetation index, Template matching (Digital image processing), Earthquake hazard analysis, Landsat satellites, Geographical positions, Sustainable urban development |
| Geographic Terms: | Vietnam, Shenzhen (Guangdong Sheng, China : East), Hanoi (Vietnam), China |
| Abstract: | Highlights: What are the main findings? A parsimonious framework is developed for building construction year mapping in cloud-prone regions, leveraging annual Landsat NDVI and open footprints via TTM algorithm. Validated across two heavily cloud-contaminated metros (Shenzhen, Hanoi), it delivers high accuracy and nearly 1.8-fold improvement over monthly LandTrendr in Shenzhen. What is the implication of the main finding? Coarser but consistent annual composites outperform finer-grained alternatives for built-up change detection under persistent cloud cover. This scalable spatial triage tool supports physical vulnerability screening, seismic risk modeling, and resilient planning in resource-constrained, fast-growing regions. Assessing urban resilience requires accurate data on building age as a temporal proxy associated with structural vulnerability, yet persistent cloud cover and rapid development constrain data availability in tropical and subtropical regions. We propose a computationally efficient framework that prioritizes annual data integrity over monthly granularity to map building construction years. By combining annual cloud-free Landsat NDVI (Normalized Difference Vegetation Index) composites with open-source building footprints, the framework utilizes a Temporal Template Matching (TTM) algorithm to detect the distinct "vegetation-to-built" transition signal. Evaluated across two dynamic and heavily cloud-contaminated metropolitan areas—Shenzhen, China, and Hanoi, Vietnam—this approach achieves a high producer's accuracy; furthermore, in Shenzhen, where a monthly comparative analysis was conducted, it outperforms a noise-sensitive monthly LandTrendr-based baseline by a factor of nearly 1.8. Our findings demonstrate that under persistent cloud contamination, a coarser but consistent annual composite provides a more reliable signal than finer-grained alternatives. This scalable methodology generates critical building-age datasets, offering foundational structural intelligence for potential inputs into seismic risk modeling and resilient urban planning in rapid-growth and resource-constrained regions. [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.) | |
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| Header | DbId: egs DbLabel: Engineering Source An: 195440479 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Mapping Building Construction Year from Landsat in Data-Scarce, Cloud-Prone Regions: A Parsimonious Spatial Triage Tool for Physical Vulnerability Screening. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liu%2C+Yang%22">Liu, Yang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Xuan%22">Zhang, Xuan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mo%2C+Zewen%22">Mo, Zewen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Zhipang%22">Wang, Zhipang</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Qingling%22">Zhang, Qingling</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zhangqling@mail.sysu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jul2026, Vol. 18 Issue 13, p2135. 28p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Normalized+difference+vegetation+index%22">Normalized difference vegetation index</searchLink><br /><searchLink fieldCode="DE" term="%22Template+matching+%28Digital+image+processing%29%22">Template matching (Digital image processing)</searchLink><br /><searchLink fieldCode="DE" term="%22Earthquake+hazard+analysis%22">Earthquake hazard analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Landsat+satellites%22">Landsat satellites</searchLink><br /><searchLink fieldCode="DE" term="%22Geographical+positions%22">Geographical positions</searchLink><br /><searchLink fieldCode="DE" term="%22Sustainable+urban+development%22">Sustainable urban development</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Vietnam%22">Vietnam</searchLink><br /><searchLink fieldCode="DE" term="%22Shenzhen+%28Guangdong+Sheng%2C+China+%3A+East%29%22">Shenzhen (Guangdong Sheng, China : East)</searchLink><br /><searchLink fieldCode="DE" term="%22Hanoi+%28Vietnam%29%22">Hanoi (Vietnam)</searchLink><br /><searchLink fieldCode="DE" term="%22China%22">China</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? A parsimonious framework is developed for building construction year mapping in cloud-prone regions, leveraging annual Landsat NDVI and open footprints via TTM algorithm. Validated across two heavily cloud-contaminated metros (Shenzhen, Hanoi), it delivers high accuracy and nearly 1.8-fold improvement over monthly LandTrendr in Shenzhen. What is the implication of the main finding? Coarser but consistent annual composites outperform finer-grained alternatives for built-up change detection under persistent cloud cover. This scalable spatial triage tool supports physical vulnerability screening, seismic risk modeling, and resilient planning in resource-constrained, fast-growing regions. Assessing urban resilience requires accurate data on building age as a temporal proxy associated with structural vulnerability, yet persistent cloud cover and rapid development constrain data availability in tropical and subtropical regions. We propose a computationally efficient framework that prioritizes annual data integrity over monthly granularity to map building construction years. By combining annual cloud-free Landsat NDVI (Normalized Difference Vegetation Index) composites with open-source building footprints, the framework utilizes a Temporal Template Matching (TTM) algorithm to detect the distinct "vegetation-to-built" transition signal. Evaluated across two dynamic and heavily cloud-contaminated metropolitan areas—Shenzhen, China, and Hanoi, Vietnam—this approach achieves a high producer's accuracy; furthermore, in Shenzhen, where a monthly comparative analysis was conducted, it outperforms a noise-sensitive monthly LandTrendr-based baseline by a factor of nearly 1.8. Our findings demonstrate that under persistent cloud contamination, a coarser but consistent annual composite provides a more reliable signal than finer-grained alternatives. This scalable methodology generates critical building-age datasets, offering foundational structural intelligence for potential inputs into seismic risk modeling and resilient urban planning in rapid-growth and resource-constrained regions. [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/rs18132135 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 28 StartPage: 2135 Subjects: – SubjectFull: Normalized difference vegetation index Type: general – SubjectFull: Template matching (Digital image processing) Type: general – SubjectFull: Earthquake hazard analysis Type: general – SubjectFull: Landsat satellites Type: general – SubjectFull: Geographical positions Type: general – SubjectFull: Sustainable urban development Type: general – SubjectFull: Vietnam Type: general – SubjectFull: Shenzhen (Guangdong Sheng, China : East) Type: general – SubjectFull: Hanoi (Vietnam) Type: general – SubjectFull: China Type: general Titles: – TitleFull: Mapping Building Construction Year from Landsat in Data-Scarce, Cloud-Prone Regions: A Parsimonious Spatial Triage Tool for Physical Vulnerability Screening. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liu, Yang – PersonEntity: Name: NameFull: Zhang, Xuan – PersonEntity: Name: NameFull: Mo, Zewen – PersonEntity: Name: NameFull: Wang, Zhipang – PersonEntity: Name: NameFull: Zhang, Qingling IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 13 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
