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Machine learning

Leveraging Satellite Imagery and Machine Learning for Urban Green Space Assessment

Strategic Insights

— 2 min read

Researchers from King Saud University have developed an innovative solution to accurately assess the extent and quality of green spaces in urban areas. The team, led by Meshal Alfarhood, used live satellite imagery and advanced deep learning techniques to identify trees and measure green density in vegetated areas. Their methodology not only supports the objectives of the 'Green Riyadh' project but also sets a benchmark for green space evaluation that can be adopted by cities worldwide.

Key Takeaways:

  • The researchers proposed an innovative solution that leverages live satellite imagery and advanced deep learning techniques to address the challenges of accurately assessing green spaces.
  • The methodology uses two separate analytical pipelines to process high-resolution satellite imagery and identify trees and measure green density in vegetated areas.
  • The experimental results show significant improvements in accuracy and efficiency, with the YOLOv11 model achieving a mAP@50 of 95.4%, precision of 94.6%, and recall of 90.2%.
  • The proposed methodology supports the objectives of the 'Green Riyadh' project, which aims to improve air and water quality, increase tree and plant coverage, and promote environmental well-being for city residents.
  • The research sets a benchmark for green space evaluation that can be adopted by cities worldwide, enabling comprehensive progress evaluation and facilitating informed decision-making for urban planning.
  • The study was conducted in collaboration with the 'Green Riyadh' project, which represents a major initiative to enhance urban sustainability by expanding green spaces throughout Riyadh City.
  • The research was supported by King Saud University, Riyadh, Saudi Arabia.

Statistics:

  • The YOLOv11 model achieved a mAP@50 of 95.4%, precision of 94.6%, and recall of 90.2%.
  • The proposed methodology shows significant improvements in accuracy and efficiency compared to traditional methods for evaluating green areas and measuring tree density.
  • Riyadh City aims to expand green spaces by 20% within the next five years as part of the 'Green Riyadh' project.
  • The project targets to improve air and water quality, increase tree and plant coverage by 15%, and promote environmental well-being for city residents.
  • The study was published in the journal Sustainability and is available online.

Sources:

  • Leveraging Satellite Imagery and Machine Learning for Urban Green Space Assessment: a Case Study From Riyadh City. Sustainability, 2025;17(13):6118.
  • King Saud University. College of Computer and Information Sciences. Department of Computer Sciences.
  • Mdpi. St Alban-Anlage 66, Ch-4052 Basel, Switzerland.
  • Meshal Alfarhood. King Saud University. College of Computer and Information Sciences. Department of Computer Sciences.
  • Abdullah Alahmad. Abdalrahman Alalwan. Faisal Alkulaib. King Saud University. College of Computer and Information Sciences. Department of Computer Sciences.

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