Predicting Local Climate Zones using Urban Morphometrics and Satellite Imagery

2026-02-23

AI Summary

This study investigates the predictive capacity of urban morphometric metrics for Local Climate Zone (LCZ) classification and their potential integration with remote sensing imagery. Leveraging building footprints and street networks, the authors construct 321 two-dimensional morphometric indicators and evaluate LCZ classification performance across multiple spatial scales. For the first time, the effectiveness of a purely morphometric approach is systematically assessed, alongside the added value of fusing remote sensing data. Results reveal substantial regional variation in the predictive performance of morphometric features alone. While the integration of remote sensing imagery yields modest accuracy improvements in certain areas, the overall gain is limited and inconsistent, highlighting inherent constraints in the relationship between observable urban form and LCZ typology.

Technology Category

• Natural Language Processing: Lexical Semantics and Morphology
• Computer Vision: Remote Sensing / Geospatial AI
• Search and Optimization: Local Search

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Security and Privacy: Large-scale security measurements

Abstract

The Local Climate Zone (LCZ) framework is commonly employed to represent urban form in morphological analyses despite its mapping predominantly relies on satellite imagery. Urban morphometrics, describing urban form via numerical measures of physical aspects and spatial relationships of its elements, offers another avenue. This study evaluates the ability of morphometric assessment to predict LCZs using a) a morphometric-based LCZ prediction, and b) a fusion-based LCZ prediction combining morphometrics with satellite imagery. We calculate 321 2D morphometric attributes from building footprints and street networks, covering their various properties at multiple spatial scales. Subsequently, we develop four classification schemes: morphometric-based prediction, baseline image-based prediction, and two techniques fusing morphometrics with imagery. We evaluate them across five sites. Results from the morphometric-based prediction indicate that the correspondence between 2D urban morphometrics and urban LCZ types is selective and inconsistent, rendering the efficacy of this method site-dependent. Nevertheless, it demonstrated that a much broader range of urban form properties is relevant for distinguishing LCZ types compared to standard parameters. Relative to the image-based baseline, the fusion yielded relatively distinct accuracy improvements for urban LCZ types at two sites; however, gains at the remaining sites were negligible or even slightly negative, suggesting that the benefits of fusion are modest and inconsistent. Collectively, these results indicate that the relationship between the LCZs and the measurable, visible aspects of urban form is tenuous, thus the LCZ framework should be used with caution in morphological studies.

Problem

• Local Climate Zones
• Urban Morphometrics
• Satellite Imagery
• Urban Form
• LCZ Prediction

Innovation

• urban morphometrics
• Local Climate Zones
• satellite imagery fusion
• LCZ prediction
• morphometric attributes