Efficient matrix algebra encoding for urban solar irradiation simulation: fine-grid ground-level estimation with vector data.
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| Title: | Efficient matrix algebra encoding for urban solar irradiation simulation: fine-grid ground-level estimation with vector data. |
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| Authors: | Cui, Ziang1 (AUTHOR), Leduc, Thomas1 (AUTHOR) thomas.leduc@crenau.archi.fr |
| Source: | International Journal of Geographical Information Science. Mar2025, Vol. 39 Issue 3, p577-599. 23p. |
| Subjects: | Separation of variables, Vector data, Weather, Open spaces, Machine learning |
| Abstract: | Conducting a detailed assessment of solar irradiation at the pedestrian scale on all ground surfaces of a city can assist in identifying cooler routes for pedestrian navigation or preparing the city for potential overheating issues by pinpointing overexposed areas. This article proposes an effective method for conducting such an assessment within a GIS with metric resolution across territories exceeding 100 km². It is based on standard datasets and implements an efficient strategy that relies on separation of variables, domain decomposition, and dimensionality reduction. This strategy involves creating a synthetic representation of the facades of the surrounding buildings (spatial dimension) which accelerates the calculation of shadows based on the sun's position (temporal dimension). To demonstrate the effectiveness of this method, we applied it to a French city, generating fourteen maps illustrating the solar irradiation of the area for different months of the year or for two given dates with specific weather conditions. The proposed strategy, along with the synthetic representation of building facades, opens up a wide range of possibilities. In addition to synthesizing machine learning labeled datasets, we can also consider calculating solar irradiation with time steps of a few minutes to update weather conditions throughout a journey. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Geographical Information Science is the property of Taylor & Francis Ltd 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 |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 183128383 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Efficient matrix algebra encoding for urban solar irradiation simulation: fine-grid ground-level estimation with vector data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Cui%2C+Ziang%22">Cui, Ziang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Leduc%2C+Thomas%22">Leduc, Thomas</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> thomas.leduc@crenau.archi.fr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Geographical+Information+Science%22">International Journal of Geographical Information Science</searchLink>. Mar2025, Vol. 39 Issue 3, p577-599. 23p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Separation+of+variables%22">Separation of variables</searchLink><br /><searchLink fieldCode="DE" term="%22Vector+data%22">Vector data</searchLink><br /><searchLink fieldCode="DE" term="%22Weather%22">Weather</searchLink><br /><searchLink fieldCode="DE" term="%22Open+spaces%22">Open spaces</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Conducting a detailed assessment of solar irradiation at the pedestrian scale on all ground surfaces of a city can assist in identifying cooler routes for pedestrian navigation or preparing the city for potential overheating issues by pinpointing overexposed areas. This article proposes an effective method for conducting such an assessment within a GIS with metric resolution across territories exceeding 100 km². It is based on standard datasets and implements an efficient strategy that relies on separation of variables, domain decomposition, and dimensionality reduction. This strategy involves creating a synthetic representation of the facades of the surrounding buildings (spatial dimension) which accelerates the calculation of shadows based on the sun's position (temporal dimension). To demonstrate the effectiveness of this method, we applied it to a French city, generating fourteen maps illustrating the solar irradiation of the area for different months of the year or for two given dates with specific weather conditions. The proposed strategy, along with the synthetic representation of building facades, opens up a wide range of possibilities. In addition to synthesizing machine learning labeled datasets, we can also consider calculating solar irradiation with time steps of a few minutes to update weather conditions throughout a journey. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Geographical Information Science is the property of Taylor & Francis Ltd 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: 10.1080/13658816.2024.2425339 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 577 Subjects: – SubjectFull: Separation of variables Type: general – SubjectFull: Vector data Type: general – SubjectFull: Weather Type: general – SubjectFull: Open spaces Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: Efficient matrix algebra encoding for urban solar irradiation simulation: fine-grid ground-level estimation with vector data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Cui, Ziang – PersonEntity: Name: NameFull: Leduc, Thomas IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 13658816 Numbering: – Type: volume Value: 39 – Type: issue Value: 3 Titles: – TitleFull: International Journal of Geographical Information Science Type: main |
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