Skip to content

Latest commit

 

History

History
127 lines (101 loc) · 3.76 KB

README.md

File metadata and controls

127 lines (101 loc) · 3.76 KB

FETCH: Framework for Environmental Type Classification Hub - LCZ Classifier

LCZ Classifier is a project that automates the classification of Local Climate Zones (LCZ) using geospatial data. The process begins with satellite data acquisition through the Google Solar API and continues with a series of QGIS processing steps for LCZ classification.

Features

  • Data Acquisition: Web interface for downloading DSM, RGB and MASK tiles via Google Solar API
  • Automated Processing: Series of Python scripts for QGIS that process data sequentially
  • LCZ Parameter Calculation: Analysis of:
    • Sky View Factor
    • Aspect Ratio
    • Building Surface Fraction
    • Impervious/Pervious Surface Fraction
    • Height of Roughness Elements
    • Terrain Roughness Class
    • Surface Admittance
    • Surface Albedo
    • Anthropogenic Heat Output

Prerequisites

  • QGIS 3.x
  • Python 3.x
  • Google API Key for Google Solar API
  • Python Libraries:
    • NumPy
    • Statsmodels
    • PyQt5

Installation

  1. Clone the repository
  2. Open index.html in browser for the data download interface
  3. Import Python scripts into QGIS

Usage

1. Data Acquisition

  • Open index.html
  • Draw an area on the map
  • Enter your Google API Key
  • Generate API links and download tiles

2. QGIS Processing

Execute the scripts in the following order:

  1. 01_merge_fetch_files.py: Merges downloaded files
  2. 02_import_raster_files.py: Imports raster files into the project
  3. 03_make_grid.py: Creates analysis grid
  4. 04_make_buildings.py: Extracts buildings
  5. 05_make_pervius_1.py: Pervious surfaces analysis (part 1)
  6. 06_make_pervius_2.py: Pervious surfaces analysis (part 2)
  7. 07_make_dtm.py: Generates digital terrain model
  8. 08_make_dist.py: Calculates distances
  9. 09_median_distance.py: Calculates median distances
  10. 10_mediana_altezze.py: Calculates median heights
  11. 11_make_h.py: Calculates height of roughness elements
  12. 12_aspect_ratio.py: Calculates aspect ratio
  13. 13_refine_albedo.py: Refines albedo values
  14. 14_rmsep.py: Calculates and classifies LCZs

Project Structure

  • index.html: Web interface for data download
  • scripts/: Directory containing numbered Python scripts
  • docs/: Additional documentation

Contributing

Contributions are welcome! Please:

  1. Fork the repository
  2. Create a branch for your changes
  3. Submit a pull request

License

This project is distributed under the MIT license. See the LICENSE file for details.

Acknowledgements

  • Google Solar API
  • QGIS Development Team
  • Leaflet.js and contributors

Data Acquisition Interface Details

Features

  • Interactive map interface using Leaflet.js
  • Automatic grid generation (195m x 195m tiles)
  • Batch download functionality for DSM, RGB, and mask files
  • Progress tracking for downloads
  • Automatic ZIP file creation
  • Built-in geocoding for location search

Technical Requirements

  • Modern web browser with JavaScript enabled
  • Active internet connection
  • Google Cloud Platform account with:
    • Solar API enabled
    • Valid API key with billing configured
    • Sufficient quota for Solar API requests

Download Process

  1. The interface automatically divides the selected area into 195m x 195m tiles
  2. For each tile, three files are downloaded:
    • DSM (Digital Surface Model)
    • RGB (Aerial imagery)
    • Mask (Building footprints)
  3. Files are named using the format:
    • dsm_[latitude]_[longitude].tif
    • rgb_[latitude]_[longitude].tif
    • mask_[latitude]_[longitude].tif
  4. All files are automatically compressed into a single tiles.zip file

Browser Compatibility

  • Chrome (recommended)
  • Firefox
  • Safari
  • Edge

Data Usage Notes

  • Each tile requires 3 API calls
  • Approximate file sizes:
    • DSM: ~2-3 MB per tile
    • RGB: ~1-2 MB per tile
    • Mask: ~0.5-1 MB per tile