youness-yach/uhi-business-challenge

Satellite-driven Urban Heat Island classification across Rio, Santiago, and Sierra Leone (Hult Business Challenge II, team project). # Predicting Urban Heat Islands — A Machine Learning Approach Across Three Cities Satellite-driven classification of Urban Heat Island (UHI) intensity in Rio de Janeiro and Santiago, with a combined model transferred to predict UHI risk in Freetown, Sierra Leone. **Team project** — Hult International Business School, Business Challenge II. Team: Carolina Trovisco, Filippo Beni, João Ponte, Mickias Ambaye, **Youness Yachruti**, Yousra Sajjad. My focus on this team was model research: testing and tuning multiple tree-based and gradient-boosting models across all three locations to improve prediction accuracy. See a teammate's complementary extraction-pipeline repo: Mickias-Ambaye/uhi-pipe. ## Overview Urban Heat Islands are urban areas that run warmer than their surroundings — sometimes by 10°C+ locally — driven by dense building layouts, impervious surface heat absorption, and waste heat from industry and transport. The challenge: build a model that predicts UHI intensity from satellite data alone, and test whether a model trained on two cities can transfer to predict a third city it has never seen. We extracted Sentinel-2 spectral indices and Landsat-8 thermal/elevation data for Rio de Janeiro and Santiago (50,150 combined data points), engineered interaction features (e.g. LST × NDVI, elevation × LST), trained per-city classifiers, then tested whether a model combining both cities' signal could predict UHI intensity in Freetown, Sierra Leone — a city with no training labels of its own. ## Key results All figures below are held-out classification performance (F1 / precision / recall), taken directly from the team's final report — not backtested or live figures, this is a classification task, not a trading strategy. - **Rio de Janeiro:** F1 = 0.959 (Precision 0.960, Recall 0.959) — best model: XGBoost. Rio's heat signal is dominated by raw thermal response and building morphology; intense year-round solar exposure combined with high shares of concrete/asphalt …