First published 9/28/2026, 2:08:15 PM · Last updated 9/28/2026, 2:08:15 PM

Explainable machine learning for urban park cooling effect: Identifying dual-perspective drivers from satellite-derived metrics across extreme and normal weather

A peer-reviewed journal article (Elsevier) published 2026-08-01 reports use of explainable machine learning and satellite-derived metrics to analyze how urban parks cool surroundings under both extreme and normal weather and to identify drivers from two perspectives; the supplied excerpt contains no further methodological, geographic, or result details. Additional specifics beyond the title and publication metadata were not provided in the source data received by Planet Briefing.

Categories: environment-and-climate, science-and-space

Generated scores

Scores are based on the cited reporting and use a 1–10 scale. Read the methodology.

Confidence: 3/10How certain the assessment is based on the clarity and evidence in the cited reporting.
Geographic reach: 1/10How widely the event reaches, from a local area (1) to worldwide impact (10).
Global importance: 2/10The event's broader significance, from limited importance (1) to major global importance (10).
Impact magnitude: 3/10The scale or severity of the real-world impact, from minor (1) to extreme (10).
Positivity: 5/10How harmful or beneficial the event is, from strongly harmful (1) to strongly positive (10).
Urgency: 1/10How time-sensitive the event is, from low urgency (1) to immediate attention needed (10).

Why it matters

The study addresses drivers of urban park cooling across different weather conditions, a topic of relevance to urban climate research.

Sources

Explainable machine learning for urban park cooling effect: Identifying dual-perspective drivers from satellite-derived metrics across extreme and normal weather

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