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A Review on Recent Deep Learning-Based Semantic Segmentation for Urban Greenness Measurement.

Doo Hong Lee¹, Hye Yeon Park², Joonwhoan Lee³

¹Landscape Architecture and Environmental Planning, College of Agriculture and Applied Sciences, Utah State University, Logan, UT 84322, USA.

Sensors (Basel, Switzerland)

|April 13, 2024

Summary

Deep learning semantic segmentation advances urban green space (UGS) measurement for landscape analysis. Accurate segmentation is vital for greenness quantification and planning, even with deep learning model explainability challenges.

Keywords:

deep learning (DL)-based semantic segmentation greenness measures landscape analysis and planning urban green space (UGS)

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Area of Science:

Environmental science
Computer science
Urban planning

Background:

Accurate measurement of urban green space (UGS) is essential for effective landscape analysis and urban planning.
Traditional methods for UGS measurement face limitations in accuracy and efficiency.
Recent advancements in deep learning (DL) offer potential solutions for enhanced UGS analysis.

Purpose of the Study:

To review recent technological breakthroughs in deep learning (DL)-based semantic segmentation for urban green space measurement.
To explore quantitative greenness measures and their integration with semantic segmentation techniques.
To illuminate the evolution of UGS measurement and segmentation tasks for advanced landscape analysis.

Main Methods:

Review of deep learning (DL) models for semantic segmentation applied to UGS.
Categorization of quantitative greenness measures into plan view- and perspective view-based methods (e.g., Land Class Classification (LCC), Green View Index (GVI)).
Exploration of performance metrics and public datasets for UGS measure construction.
Discussion of unsupervised domain adaptation (UDA) for aerial imagery.

Main Results:

Accurate semantic segmentation is crucial for fine-grained greenness measures and qualitative landscape evaluation.
Deep learning models significantly improve the efficiency and accuracy of UGS analysis.
Unsupervised domain adaptation (UDA) helps address challenges like scale changes and limited labeled data in aerial images.

Conclusions:

Deep learning-based semantic segmentation is indispensable for accurate urban green space measurement and landscape analysis.
Further research into DL model explainability and UDA techniques is needed for robust UGS quantification.
This review provides insights into DL advancements for researchers in urban green space studies.