# Optimizing Urban Critical Green Space Development Using Machine Learning

arXiv: https://arxiv.org/abs/2505.09175 · HTML: https://mlsanity.com/paper/2505.09175/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 157 references that are in the map.
- First posted on arXiv 14 May 2025.

## Builds on (most cited first, 5 of 5)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Machine Learning Framework for High-Resolution Air Temperature Downscaling Using LiDAR-Derived Urban Morphological Features](https://mlsanity.com/paper/2409.02120/) (2024)
- [A machine learning-based framework for high resolution mapping of PM2.5 in Tehran, Iran, using MAIAC AOD data](https://mlsanity.com/paper/2204.02093/) (2022)
- [Data level and decision level fusion of satellite multi-sensor AOD retrievals for improving PM2.5 estimations, a study on Tehran](https://mlsanity.com/paper/2302.10278/) (2023)
- [Using Deep Ensemble Forest for High Resolution Mapping of PM2.5 from MODIS MAIAC AOD in Tehran, Iran](https://mlsanity.com/paper/2402.02139/) (2024)

## Abstract (arXiv)

This paper presents a novel framework for prioritizing urban green space development in Tehran using diverse socio-economic, environmental, and sensitivity indices. The indices were derived from various sources including Google Earth Engine, air pollution measurements, municipal reports and the Weather Research & Forecasting (WRF) model. The WRF model was used to estimate the air temperature at a 1 km resolution due to insufficient meteorological stations, yielding RMSE and MAE values of 0.96degC and 0.92degC, respectively. After data preparation, several machine learning models were used for binary vegetation cover classification including XGBoost, LightGBM, Random Forest (RF) and Extra Trees. RF achieved the highest performance, exceeding 94% in Overall Accuracy, Recall, and F1-score. Then, the probability of areas lacking vegetation cover was assessed using socio-economic, environmental and sensitivity indices. This resulted in the RF generating an urban green space development prioritization map. Feature Importance Analysis revealed that the most significant indices were nightly land surface temperature (LST) and sensitive population. Finally, the framework performance was validated through microclimate simulation to assess the critical areas after and before the green space development by green roofs. The simulation demonstrated reducing air temperature by up to 0.67degC after utilizing the green roof technology in critical areas. As a result, this framework provides a valuable tool for urban planners to develop green spaces.