Street Sensing Urban Thermal Environment Assessment Using Street View Images /
Combining publicly available Google Street View (GSV) images with deep learning and radiative transfer models enables researchers to assess urban physical and thermal environments, especially in high-density areas. This book introduces this advanced approach, which provides large-scale, high-accurac...
Zapisane w:
| 1. autor: | |
|---|---|
| Korporacja: | |
| Format: | Elektroniczne E-book |
| Język: | angielski |
| Wydane: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2025.
|
| Wydanie: | 1st ed. 2025. |
| Seria: | SpringerBriefs in Geography,
|
| Hasła przedmiotowe: | |
| Dostęp online: | https://doi.org/10.1007/978-3-031-92005-9 |
| Etykiety: |
Dodaj etykietę
Nie ma etykietki, Dołącz pierwszą etykiete!
|
MARC
| LEADER | 00000nam a22000005i 4500 | ||
|---|---|---|---|
| 001 | 978-3-031-92005-9 | ||
| 003 | DE-He213 | ||
| 005 | 20260810160500.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 250530s2025 sz | o |||| 0|eng d | ||
| 020 | |a 9783031920059 |9 978-3-031-92005-9 | ||
| 024 | 7 | |a 10.1007/978-3-031-92005-9 |2 doi | |
| 050 | 4 | |a QH541.15.M64 | |
| 072 | 7 | |a TQD |2 bicssc | |
| 072 | 7 | |a SCI026000 |2 bisacsh | |
| 072 | 7 | |a TQD |2 thema | |
| 082 | 0 | 4 | |a 363.7063 |2 23 |
| 100 | 1 | |a Gong, Fang-Ying. |e author. |0 (orcid)0000-0001-5194-6435 |1 https://orcid.org/0000-0001-5194-6435 |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Street Sensing |h [electronic resource] : |b Urban Thermal Environment Assessment Using Street View Images / |c by Fang-Ying Gong. |
| 250 | |a 1st ed. 2025. | ||
| 264 | 1 | |a Cham : |b Springer Nature Switzerland : |b Imprint: Springer, |c 2025. | |
| 300 | |a 1 online resource (XXIX, 127 p. 48 illus.). |b 44 illus. in color. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 341 | 0 | |b Table of contents navigation |2 onix | |
| 341 | 0 | |b Single logical reading order |2 onix | |
| 341 | 0 | |b Short alternative textual descriptions |2 onix | |
| 341 | 0 | |b Use of color is not sole means of conveying information |2 onix | |
| 341 | 0 | |b Use of high contrast between text and background color |2 onix | |
| 341 | 0 | |b Next / Previous structural navigation |2 onix | |
| 341 | 0 | |b All non-decorative content supports reading without sight |2 onix | |
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a SpringerBriefs in Geography, |x 2211-4173 | |
| 505 | 0 | |a Chapter 1: Introduction -- Chapter 2: Theoretical background -- Chapter 3: Methodology -- Chapter 4: Spatial Patterns of Street Canyon View Factors -- Chapter 5: Spatiotemporal Patterns of Street Canyon Solar Radiation -- Chapter 6: Implementation of Urban Planning and Design at Street Level -- Chapter 7: Conclusions. | |
| 520 | |a Combining publicly available Google Street View (GSV) images with deep learning and radiative transfer models enables researchers to assess urban physical and thermal environments, especially in high-density areas. This book introduces this advanced approach, which provides large-scale, high-accuracy, and high-density measurements of street-level urban features—key to understanding urban radiation balance and thermal dynamics. By leveraging GSV images, this method directly characterizes urban streetscapes, including structural and geometric attributes, allowing for a comprehensive assessment. Moreover, the methods can be applied to any geographical location covered by GSV, making it a low-cost and effective tool for urban studies globally. This is particularly advantageous compared to traditional 3D-GIS models, which may not always be freely available or as extensive in coverage as GSV images. Lessons from Street Sensing provide data-driven insights for urban planning and governance. The accurate mapping of street view factors and solar irradiance helps identify areas with insufficient greenery, excessive or insufficient solar exposure, and other urban environment issues. These insights can help policymakers and urban planners mitigate negative environmental impacts and improve urban living conditions. | ||
| 532 | 8 | |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 588 | |a Print version record. | ||
| 650 | 0 | |a Environmental monitoring. | |
| 650 | 0 | |a Physical geography. | |
| 650 | 0 | |a Environmental geography. | |
| 650 | 1 | 4 | |a Environmental Monitoring. |
| 650 | 2 | 4 | |a Physical Geography. |
| 650 | 2 | 4 | |a Integrated Geography. |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783031920042 |
| 776 | 0 | 8 | |i Printed edition: |z 9783031920066 |
| 830 | 0 | |a SpringerBriefs in Geography, |x 2211-4173 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-3-031-92005-9 |
| 912 | |a ZDB-2-EES | ||
| 912 | |a ZDB-2-SXEE | ||
| 912 | |a ZDB-2-230 | ||
| 950 | |a Earth and Environmental Science (SpringerNature-11646) | ||
| 950 | |a Earth and Environmental Science (R0) (SpringerNature-43711) | ||
| 950 | |a Focus Bundle Environmental Sciences (SpringerNature-85400) | ||