urban-heat-assessment

Assess urban heat islands from thermal infrared remote sensing data.

Updated Jun 2, 2026

Search Tags:#remote sensing #LST #urban heat island #heat risk #cooling strategy

Ready to run

npx skills add https://github.com/bettercallfan/deerflow --skill urban-heat-assessment

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: urban-heat-assessment
Source: https://github.com/bettercallfan/deerflow/tree/main/skills/custom/remote_sensing_image/urban-heat-assessment
Command: npx skills add https://github.com/bettercallfan/deerflow --skill urban-heat-assessment

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires rasterio, numpy, scipy, shapely, and includes scripts (resource) and assets (resource) components.

What problem does it solve?

This Skill addresses the challenges of urban heat island assessment by quantifying UHI intensity and identifying high-temperature risk zones.

Core Features & Use Cases

UHI Intensity Quantification: Analyzes thermal infrared imagery to measure urban heat island intensity.
Risk Zone Identification: Grades heat risk using LST, NDVI, and building density.
Cooling Recommendations: Offers strategies to mitigate UHI based on assessment results.
Use Case: For urban planners and environmental managers looking to optimize urban cooling strategies and reduce health risks associated with high temperatures.

Quick Start

Run the urban-heat-assessment skill with the following input parameters: thermal_image, boundary, and optionally ndvi_image and building_density.

Frequently Asked Questions about urban-heat-assessment

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I quantify urban heat island intensity from thermal infrared remote sensing data?▼

To quantify urban heat island intensity, you process thermal infrared imagery to retrieve Land Surface Temperature (LST) and analyze spatial temperature variations. This Skill processes thermal imagery to measure UHI intensity and identify high-temperature risk zones.

What data do I need to identify heat risk zones using LST and NDVI?▼

Identifying heat risk zones requires thermal imagery and boundary definitions. You can optionally provide an NDVI image and building density data to enhance the risk grading based on Land Surface Temperature, vegetation index, and structural density.

Can I use rasterio and numpy for urban heat island assessment?▼

Yes, you can use rasterio and numpy for urban heat island assessment. This Skill leverages rasterio for thermal imagery processing and numpy for LST retrieval calculations, utilizing these dependencies to analyze thermal infrared data effectively.

How does LST retrieval work for urban cooling strategy planning?▼

LST retrieval for urban cooling strategy planning works by extracting Land Surface Temperature from thermal infrared remote sensing data. The process grades heat risk zones using LST, NDVI, and building density to offer targeted strategies to mitigate UHI.

What's the best way to grade heat risk zones using thermal imagery and building density?▼

The best way to grade heat risk zones is by combining thermal imagery for LST retrieval with building density and NDVI data. This approach grades heat risk comprehensively, helping urban planners optimize cooling strategies and reduce health risks.