Urban morphology impacts on urban microclimate using artificial intelligence – a review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Urban morphology, defined by the characteristics and spatial arrangement of urban structures, significantly affects urban microclimate in terms of thermal environments, wind dynamics, energy use, and outdoor air quality. Despite extensive research in this field, these effects are intensified by climate change and rapid urbanization, posing challenges to urban sustainability, such as poor air quality, increased energy demands, and pedestrian discomfort. While artificial intelligence (AI) and machine learning (ML) offer promising solutions for addressing these challenges, the field lacks standardized approaches for implementing these technologies. By leveraging urban morphology indicators such as sky view factor, building density, and green space ratio, AI models can analyze complex interactions across various spatiotemporal scales. However, significant variability in methodologies, indicators, and datasets limits the generalizability and applicability of these techniques. By synthesizing 111 studies over the last decade utilizing urban morphology and AI models to predict urban microclimate, this review aims to bridge these gaps and highlight AI’s unique potential to contribute to the field. Analyzed studies reported that key urban morphology indicators, particularly building density and height, explain up to 75% of land surface temperature variance across seasons, while sky view factor accounts for over 67% of heat exposure variations in urban environments, with these findings emerging from multiple independent investigations across diverse urban contexts. Random Forest emerges as the most widely adopted AI technique, demonstrating robust performance across different applications. Emerging trends, such as hybrid approaches combining AI with physics-based models, are highlighted as promising avenues for advancing the field. Our review identifies the need for standardized frameworks and datasets to enhance model applicability. The study presents actionable insights for climate-responsive urban planning and lays the groundwork for interdisciplinary studies, enabling the development of resilient, sustainable urban environments amid the growing challenges of urbanization and climate change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it