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urban-change-detection

Analyze multi-temporal remote sensing images to detect urban changes.

Updated Jun 2, 2026
Search Tags:#image processing#land use#remote sensing#urban change detection#city monitoring
One-click install
npx skills add https://github.com/bettercallfan/deerflow --skill urban-change-detection
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: urban-change-detection
Source: https://github.com/bettercallfan/deerflow/tree/main/skills/custom/remote_sensing_image/urban-change-detection
Command: npx skills add https://github.com/bettercallfan/deerflow --skill urban-change-detection

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

The urban-change-detection Skill addresses the need for efficient urban change detection, pinpointing areas such as illegal construction, illicit dumping, land-use conversion, and urban renewal for better governance and monitoring.

Core Features & Use Cases

  • Multi-Temporal Analysis: Analyzes changes in urban areas using two or more sets of remote sensing imagery.
  • Illegal Construction Detection: Identifies new buildings that might be unauthorized constructions.
  • Use Case: Imagine you need to monitor the growth of a city over time and identify instances of unauthorised construction. This Skill can quickly compare multiple images to find significant changes.

Quick Start

Use the 'urban-change-detection' skill with inputs for the pre and post imagery, such as 'image_t1' and 'image_t2'.

Frequently Asked Questions about urban-change-detection

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

FAQPage Schema
How do I detect illegal construction and land-use changes using multi-temporal remote sensing imagery?▼

Multi-temporal remote sensing imagery detects illegal construction by comparing images from different time points to identify urban modifications. This Skill analyzes pre and post imagery inputs to localize and quantify unauthorized building patterns.

What is the best way to monitor urban area changes over time for city planning?▼

Urban area change monitoring is best achieved by comparing multiple sets of remote sensing imagery to pinpoint land-use conversion and urban renewal. This Skill quantifies change patterns to support environmental regulation and city planning.

Can I use rasterio and numpy for urban change detection with deep learning?▼

Rasterio and numpy support urban change detection by handling remote sensing image arrays and processing multi-temporal imagery. This Skill integrates these dependencies alongside scipy and deep learning methods to localize urban modifications.

How do I compare two remote sensing images to find illicit dumping sites?▼

Comparing two remote sensing images identifies illicit dumping sites by detecting significant pixel changes between time points. This Skill analyzes the pre and post imagery inputs to localize environmental violations and land-use conversions.

Does this urban change detection approach use CV and PCA algorithms?▼

This urban change detection approach uses CV, PCA, and deep learning algorithms to quantify and localize change patterns in remote sensing imagery. These methods process multi-temporal images to identify modifications like unauthorized construction.

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