Real-Time Green View Index Assessment: AI-Driven Analysis of Urban Vegetation Using Dashcam Videos
<p dir="ltr">The Green View Index (GVI) is a valuable metric for assessing visible greenery in urban areas and improving urban planning. However, current GVI calculation methods, which predominantly rely on manual assessments through on-site visits or Google Street View images, often...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | |
| منشور في: |
2025
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513531983953920 |
|---|---|
| author | Karam Masad (22827149) |
| author2 | Fethi Filali (12646471) |
| author2_role | author |
| author_facet | Karam Masad (22827149) Fethi Filali (12646471) |
| author_role | author |
| dc.creator.none.fl_str_mv | Karam Masad (22827149) Fethi Filali (12646471) |
| dc.date.none.fl_str_mv | 2025-06-24T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/jstars.2025.3582867 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Real-Time_Green_View_Index_Assessment_AI-Driven_Analysis_of_Urban_Vegetation_Using_Dashcam_Videos/30858842 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Built environment and design Urban and regional planning Environmental sciences Ecological applications Environmental management Dashcam video analysis green view index (GVI) real-time urban greenery monitoring vegetation health YOLOv11 semantic segmentation Green products Vegetation mapping Real-time systems Urban planning Normalized difference vegetation index Videos Indexes Artificial intelligence Semantic segmentation Biological system modeling |
| dc.title.none.fl_str_mv | Real-Time Green View Index Assessment: AI-Driven Analysis of Urban Vegetation Using Dashcam Videos |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The Green View Index (GVI) is a valuable metric for assessing visible greenery in urban areas and improving urban planning. However, current GVI calculation methods, which predominantly rely on manual assessments through on-site visits or Google Street View images, often provide outdated data and fail to account for the quality and health of vegetation. This article proposes an AI-driven approach to improve the accuracy and timeliness of GVI measurements using dashcam videos for real-time vegetation analysis. A deep learning model based on YOLOv11 is used for semantic segmentation of vegetation and terrain. The GVI calculation is further enhanced by incorporating vegetation health indicators derived from pixel saturation and hue values. Greenery is categorized into two distinct classes: “vegetation,” which represents vertical greenery (e.g., trees), and “terrain,” which represents horizontal greenery (e.g., grass). This classification allows us to prioritize vertical greenery, by giving it greater weighting, which generally offers greater ecological and aesthetic benefits in urban environments. In addition, GVI heat maps are generated to visually represent the distribution of greenery across Doha, Qatar. This approach overcomes the critical limitations of static GVI models, offering a more dynamic and accurate tool for assessing urban green spaces by enabling real-time, health monitoring, and vertically prioritized greenery assessments. This allows for making data-driven decisions when addressing sustainable urban planning. Adaptive policy making, improved ecological designs, and consistent audits for urban greenery could all become possible due to this proposed method. This is particularly valuable in rapidly growing regions like Qatar and regions with limited street-view coverage, where timely vegetation data is essential for sustainable urban planning.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/jstars.2025.3582867" target="_blank">https://dx.doi.org/10.1109/jstars.2025.3582867</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_fa3ffa00cb52f50d8caa265b36a3e39d |
| identifier_str_mv | 10.1109/jstars.2025.3582867 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30858842 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Real-Time Green View Index Assessment: AI-Driven Analysis of Urban Vegetation Using Dashcam VideosKaram Masad (22827149)Fethi Filali (12646471)Built environment and designUrban and regional planningEnvironmental sciencesEcological applicationsEnvironmental managementDashcam video analysisgreen view index (GVI)real-time urban greenery monitoringvegetation healthYOLOv11 semantic segmentationGreen productsVegetation mappingReal-time systemsUrban planningNormalized difference vegetation indexVideosIndexesArtificial intelligenceSemantic segmentationBiological system modeling<p dir="ltr">The Green View Index (GVI) is a valuable metric for assessing visible greenery in urban areas and improving urban planning. However, current GVI calculation methods, which predominantly rely on manual assessments through on-site visits or Google Street View images, often provide outdated data and fail to account for the quality and health of vegetation. This article proposes an AI-driven approach to improve the accuracy and timeliness of GVI measurements using dashcam videos for real-time vegetation analysis. A deep learning model based on YOLOv11 is used for semantic segmentation of vegetation and terrain. The GVI calculation is further enhanced by incorporating vegetation health indicators derived from pixel saturation and hue values. Greenery is categorized into two distinct classes: “vegetation,” which represents vertical greenery (e.g., trees), and “terrain,” which represents horizontal greenery (e.g., grass). This classification allows us to prioritize vertical greenery, by giving it greater weighting, which generally offers greater ecological and aesthetic benefits in urban environments. In addition, GVI heat maps are generated to visually represent the distribution of greenery across Doha, Qatar. This approach overcomes the critical limitations of static GVI models, offering a more dynamic and accurate tool for assessing urban green spaces by enabling real-time, health monitoring, and vertically prioritized greenery assessments. This allows for making data-driven decisions when addressing sustainable urban planning. Adaptive policy making, improved ecological designs, and consistent audits for urban greenery could all become possible due to this proposed method. This is particularly valuable in rapidly growing regions like Qatar and regions with limited street-view coverage, where timely vegetation data is essential for sustainable urban planning.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/jstars.2025.3582867" target="_blank">https://dx.doi.org/10.1109/jstars.2025.3582867</a></p>2025-06-24T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/jstars.2025.3582867https://figshare.com/articles/journal_contribution/Real-Time_Green_View_Index_Assessment_AI-Driven_Analysis_of_Urban_Vegetation_Using_Dashcam_Videos/30858842CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/308588422025-06-24T12:00:00Z |
| spellingShingle | Real-Time Green View Index Assessment: AI-Driven Analysis of Urban Vegetation Using Dashcam Videos Karam Masad (22827149) Built environment and design Urban and regional planning Environmental sciences Ecological applications Environmental management Dashcam video analysis green view index (GVI) real-time urban greenery monitoring vegetation health YOLOv11 semantic segmentation Green products Vegetation mapping Real-time systems Urban planning Normalized difference vegetation index Videos Indexes Artificial intelligence Semantic segmentation Biological system modeling |
| status_str | publishedVersion |
| title | Real-Time Green View Index Assessment: AI-Driven Analysis of Urban Vegetation Using Dashcam Videos |
| title_full | Real-Time Green View Index Assessment: AI-Driven Analysis of Urban Vegetation Using Dashcam Videos |
| title_fullStr | Real-Time Green View Index Assessment: AI-Driven Analysis of Urban Vegetation Using Dashcam Videos |
| title_full_unstemmed | Real-Time Green View Index Assessment: AI-Driven Analysis of Urban Vegetation Using Dashcam Videos |
| title_short | Real-Time Green View Index Assessment: AI-Driven Analysis of Urban Vegetation Using Dashcam Videos |
| title_sort | Real-Time Green View Index Assessment: AI-Driven Analysis of Urban Vegetation Using Dashcam Videos |
| topic | Built environment and design Urban and regional planning Environmental sciences Ecological applications Environmental management Dashcam video analysis green view index (GVI) real-time urban greenery monitoring vegetation health YOLOv11 semantic segmentation Green products Vegetation mapping Real-time systems Urban planning Normalized difference vegetation index Videos Indexes Artificial intelligence Semantic segmentation Biological system modeling |