gba-tiler 1.4.2

High-performance tool for downloading and tiling GlobalBuildingAtlas data

GlobalBuildingAtlas Downloader and Tiler

A high-performance Python tool for downloading and processing GlobalBuildingAtlas (GBA) building footprint data. Downloads large GeoJSON files via rsync and splits them into smaller, manageable tiles with configurable resolution.

GlobalBuildingAtlas

is is a dataset providing global and complete coverage of building polygons (GBA.Polygon), heights (GBA.Height) and Level of Detail 1 (LoD1) 3D building models (GBA.LoD1).

• The dataset is available under: doi:10.14459/2025mp1782307

• It can be viewed online in the GlobalBuildingAtlas web viewer

• Citation:
  Zhu, X. X., Chen, S., Zhang, F., Shi, Y., and Wang, Y., 2025: GlobalBuildingAtlas: an open global and complete dataset of building polygons, heights and LoD1 3D models, Earth Syst. Sci. Data, 17, 6647–6668, doi:10.5194/essd-17-6647-2025.

Features

• Flexible Area Selection: Define areas by bounding box, country name, or ISO codes
• Python API: Use programmatically from Python code with simple function calls
• High Performance: Streaming JSON parsing with spatial indexing (~70k features/sec)
• Memory Efficient: Processes multi-GB files without loading into RAM
• Space Optimized: Float precision limited to 1mm (3 decimal places)
• Standard GeoJSON: Outputs include CRS metadata and tile bounding boxes
• CLI Interface: Professional command-line interface with comprehensive help

Requirements

Required Dependencies

pip install ijson requests gdal

• Python 3.8+
• ijson: Streaming JSON parser
• requests: HTTP downloads for country boundaries
• GDAL/OGR: Shapefile processing (country boundaries)
• rsync: System command-line tool

Optional Dependencies

pip install tqdm

• tqdm: Progress bars (recommended)

System Requirements

• rsync must be installed and available in PATH
• Sufficient disk space for downloaded tiles and output
• Internet connection for downloading source data

Installation

Option 1: Install from PyPI (Recommended)

# Install the package
pip install gba-tiler

# With optional progress bars
pip install gba-tiler[progress]

After installation, the command is available globally:

gba-tiler --help
gba-tiler --version

For API usage in Python:

import gba_tiler
gba_tiler.main(country="Germany")

Option 2: Install from Source

1. Clone the Repository

git clone https://gitlab.rlp.net/druee/gba-tiler.git
cd gba-tiler

2. Install Python Dependencies

# Required
pip install ijson requests gdal

# Optional (for progress bars)
pip install tqdm

3. Install rsync (if not already installed)

Ubuntu/Debian:

sudo apt-get install rsync python3-gdal

macOS:

brew install rsync gdal
pip install gdal==$(gdal-config --version)

Fedora/RHEL:

sudo dnf install rsync python3-gdal

Usage

Basic Usage

Using Bounding Box

python gba_tiler.py --bbox <lon_min> <lat_min> <lon_max> <lat_max>

Example:

# Process area covering parts of Germany
python gba_tiler.py --bbox 5.0 45.0 15.0 55.0

Using Country Name

python gba_tiler.py --country <country_name>

Example:

# Process all of Germany
python gba_tiler.py --country Germany

# Process France
python gba_tiler.py --country "France"

Using ISO Country Codes

# Using ISO 2-letter code
python gba_tiler.py --iso2 <code>

# Using ISO 3-letter code
python gba_tiler.py --iso3 <code>

Example:

# Process Germany using ISO codes
python gba_tiler.py --iso2 DE
python gba_tiler.py --iso3 DEU

# Process France using ISO codes
python gba_tiler.py --iso2 FR
python gba_tiler.py --iso3 FRA

Version Information

# Show version
python gba_tiler.py --version

Logging Options

By default, the script runs silently (only shows errors). Use logging options for more output:

# Verbose output (shows progress)
python gba_tiler.py --country Germany --verbose

# Debug output (shows detailed information)
python gba_tiler.py --country Germany --debug

Advanced Options

python gba_tiler.py --country Germany \
    --delta 0.05 \              # Tile size: 0.05° (~5.5km at equator)
    --batch-size 2000 \         # Write 2000 features per batch
    --output-dir my_tiles \     # Custom output directory
    --temp-dir my_temp \        # Custom temp directory
    --verbose                   # Show progress

Complete Command-Line Reference

usage: gba_tiler.py [-h] [--version]
                    (--bbox LON_MIN LAT_MIN LON_MAX LAT_MAX | --country NAME | --iso2 CODE | --iso3 CODE)
                    [-v | --debug] [--delta DEGREES] [--batch-size N]
                    [--output-dir DIR] [--temp-dir DIR]

GlobalBuildingAtlas Downloader and Tiler

optional arguments:
  -h, --help            show this help message and exit
  --version             show program's version number and exit
  
  Area specification (mutually exclusive - choose one):
  --bbox LON_MIN LAT_MIN LON_MAX LAT_MAX
                        Bounding box: min_lon min_lat max_lon max_lat (in degrees)
  --country NAME        Country name (e.g., "Germany", "France")
  --iso2 CODE           Country ISO 2-letter code (e.g., "DE", "FR")
  --iso3 CODE           Country ISO 3-letter code (e.g., "DEU", "FRA")
  
  Logging (mutually exclusive):
  -v, --verbose         Enable verbose output (INFO level)
  --debug               Enable debug output (DEBUG level)
  
  Optional parameters:
  --delta DEGREES       Tile size in degrees (default: 0.10)
  --batch-size N        Batch size for writing features (default: 1000)
  --output-dir DIR      Output directory (default: GBA_tiles)
  --temp-dir DIR        Temporary directory (default: GBA_temp)

Programmatic Usage (Python API)

The gba_tiler can also be used programmatically from Python code:

import logging
import gba_tiler

# Optional: Configure logging before importing
logging.basicConfig(level=logging.INFO)

# Process by country name
gba_tiler.main(country="Luxembourg")

# Process by ISO code with custom settings
gba_tiler.main(
    iso2="DE",
    delta=0.05,
    batch_size=2000,
    output_dir="germany_tiles"
)

# Process by bounding box
gba_tiler.main(
    bbox=(11.3, 48.0, 11.8, 48.3),
    delta=0.01,
    output_dir="munich_tiles"
)

Parameters:

• bbox: Tuple of (lon_min, lat_min, lon_max, lat_max) or None
• country: Country name string or None
• iso2: ISO 2-letter code or None
• iso3: ISO 3-letter code or None
• delta: Tile size in degrees (default: 0.10)
• batch_size: Features per batch (default: 1000)
• output_dir: Output directory (default: 'GBA_tiles')
• temp_dir: Temporary directory (default: 'GBA_temp')

Notes:

• Exactly one of bbox, country, iso2, or iso3 must be provided
• Logging level is inherited from the calling program
• If no logging is configured, WARNING level is used

Output Format

Directory Structure

GBA_tiles/
├── e00500_n4500_e00510_n4510_lod1.geojson
├── e00510_n4500_e00520_n4510_lod1.geojson
└── ...

GBA_temp/
├── e005_n50_e010_n45.geojson    # Downloaded source files
└── ...

Output File Naming

Format: <e|w><lon>_<n|s><lat>_<e|w><lon>_<n|s><lat>_lod1.geojson

• Longitude: 5 digits (centidegrees), e.g., e00500 = 5.00°E
• Latitude: 4 digits (centidegrees), e.g., n4500 = 45.00°N
• Example: e00567_n5621_e00578_n5637_lod1.geojson
  • Left: 5.67°E, Upper: 56.21°N
  • Right: 5.78°E, Lower: 56.37°N

GeoJSON Structure

{
  "type": "FeatureCollection",
  "name": "e00567_n5621_e00578_n5637_lod1",
  "bbox": [556597.0, 5621521.0, 567729.0, 5637278.0],
  "crs": {
    "type": "name",
    "properties": {
      "name": "urn:ogc:def:crs:EPSG::3857"
    }
  },
  "features": [...]
}

• name: Output tile name
• bbox: Tile bounding box in EPSG:3857 (Web Mercator meters)
• crs: Coordinate Reference System (Web Mercator)
• features: Building footprint features

Coordinate Systems

• Input CRS: EPSG:3857 (Web Mercator) - coordinates in meters
• Tile Grid: WGS84 (EPSG:4326) - tile boundaries in degrees
• Output CRS: EPSG:3857 (Web Mercator) - preserved from input

Performance

Benchmarks

• Processing Speed: ~70,000 features/second
• Spatial Index: Checks ~1-10 tiles per feature (vs 10,000 without index)
• Memory Usage: O(BATCH_SIZE × num_tiles) - typically <1GB RAM
• Example: 23 million features processed in ~10 hours

Optimization Tips

1. Increase batch size for faster I/O (use powers of 2: 1000, 2000, 4000)
2. Larger tiles (higher DELTA) = fewer files, faster processing
3. SSD storage significantly improves performance
4. Disable tqdm if running in background scripts

Performance Characteristics

Area Size	Tiles (0.1°)	Est. Time	Memory
1° × 1°	100	~1 hour	<500MB
5° × 5°	2,500	~5 hours	<1GB
10° × 10°	10,000	~10 hours	<2GB

Technical Details

Algorithm Overview

1. Download: Fetch 5°×5° source tiles via rsync from GBA server
2. Stream Parse: Use ijson to process features one-by-one (no full file load)
3. Spatial Index: 100km grid cells to quickly find candidate tiles
4. Bbox Center: Calculate bbox center for each building
5. Tile Assignment: Assign building to tile containing its bbox center
6. Batch Write: Write features in batches for optimal I/O

Coordinate Handling

Buildings are assigned to tiles based on their bounding box center:

center_x = (min_x + max_x) / 2.0
center_y = (min_y + max_y) / 2.0

This ensures:

• ✅ Each building appears in exactly one tile
• ✅ No duplicate buildings across tiles
• ✅ Deterministic assignment (repeatable)

Memory Management

The script uses streaming to handle large files:

• Input files are never loaded fully into memory
• Features are processed one at a time with ijson
• Output is written in batches (default: 1000 features)
• Batch buffers are flushed regularly to disk

Floating Point Precision

All coordinates are rounded to 3 decimal places (1mm in Web Mercator):

• Reduces file size by ~50%
• 0.001m precision is more than sufficient for buildings
• Maintains visual quality while saving disk space

Example Scripts

Basic API Usage

The repository includes example_api.py demonstrating programmatic usage:

#!/usr/bin/env python3
"""Example: Using gba_tiler programmatically"""
import logging
import gba_tiler

# Configure logging before using gba_tiler
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s'
)

# Example 1: Process by country name with defaults
gba_tiler.main(country="Luxembourg")

# Example 2: Process by ISO code with custom settings
gba_tiler.main(
    iso2="DE",
    delta=0.05,  # Smaller tiles
    batch_size=2000,  # Larger batches
    output_dir="germany_tiles"
)

# Example 3: Process by bounding box
gba_tiler.main(
    bbox=(11.3, 48.0, 11.8, 48.3),
    delta=0.01,  # Very small tiles for detail
    output_dir="munich_tiles"
)

Run the example:

python example_api.py

See example_api.py for complete examples including different logging levels.

Batch Processing Multiple Countries

The repository includes example_EU_countries.py for batch processing:

#!/usr/bin/env python3
"""Example: Batch processing EU countries"""
import csv
import lzma
import tarfile
from pathlib import Path
import gba_tiler

# Process multiple countries from CSV
with open('countries_EU.csv', newline='') as file:
    reader = csv.DictReader(file)
    for row in reader:
        country = row['Country']
        iso2 = row['ISO-Code']
        
        print(f"Processing: {country} ({iso2})")
        
        # Create tiles
        outdir = Path(f"GBA_tiles/{iso2.lower()}")
        gba_tiler.main(iso2=iso2, output_dir=str(outdir))
        
        # Compress to tar.xz
        with lzma.open(f"GBA_tiles.{iso2.lower()}.tar.xz", 'wb') as xz:
            with tarfile.open(fileobj=xz, mode='w') as tar:
                tar.add(outdir, arcname=iso2.lower())

This example processes all EU member states plus UK, Norway, Switzerland, and former Yugoslavia countries.

Run the batch processor:

python example_EU_countries.py

The script reads from countries_EU.csv and creates compressed archives for each country.

Configuration

Default Values

DELTA = 0.10          # Tile size in degrees (~11km at equator)
BATCH_SIZE = 1000     # Features per write batch
LAT_MIN = 45.0        # Minimum latitude (if using bbox)
LAT_MAX = 55.0        # Maximum latitude
LON_MIN = 5.0         # Minimum longitude
LON_MAX = 15.0        # Maximum longitude
OUTPUT_DIR = "GBA_tiles"
TEMP_DIR = "GBA_temp"

Tile Size Guidelines

DELTA	Tile Size (equator)	Files (10°×10°)	Use Case
0.25°	~28km × 28km	1,600	Country-level analysis
0.10°	~11km × 11km	10,000	Default - balanced
0.05°	~5.5km × 5.5km	40,000	City-level detail
0.01°	~1.1km × 1.1km	1,000,000	Neighborhood detail

Data Source

GlobalBuildingAtlas

• Source: Technische Universität München (TUM)

• Coverage: Global building footprints

• Resolution: LoD1 (Level of Detail 1)

• Format: GeoJSON with EPSG:3857 coordinates

• Access: see https://mediatum.ub.tum.de/1782307

• rsync Server This program loads the data from the rsync server at the TUM library (Universitätsbibliothek der Technischen Universität München) using the (publicly available) credentials:

  rsync://m1782307:[e-mail]/m1782307/LoD1/europe/
  

  Note: Update credentials in the script if needed.

Country Boundaries

• Source: Natural Earth Data (public domain)
• URL: https://naciscdn.org/naturalearth/
• Resolutions: 110m (simplified), 10m (detailed)
• Format: Shapefile (auto-converted to GeoJSON)

Troubleshooting

Common Issues

1. "rsync command not found"

Install rsync:

# Ubuntu/Debian
sudo apt-get install rsync

# macOS
brew install rsync

2. "GDAL/OGR module is required"

Install GDAL:

# Ubuntu/Debian
sudo apt-get install python3-gdal

# macOS
brew install gdal
pip install gdal==$(gdal-config --version)

# pip (may require compilation)
pip install gdal

3. "Country 'XYZ' not found"

• Check spelling (case-insensitive search)
• Try alternative names: "United States", "USA", "America"
• Use --debug to see available countries
• Use --bbox as fallback

4. Memory Issues

• Increase system swap space
• Reduce --batch-size (default: 1000)
• Process smaller areas
• Use larger --delta (fewer tiles)

5. No Output Files Created

• Check if input area has any buildings
• Verify rsync credentials
• Use --debug to see detailed processing info
• Check that tile boundaries overlap with data

Performance Issues

Slow Processing:

• Increase --batch-size (try 2000-5000)
• Use SSD storage
• Disable antivirus scanning on work directories
• Increase --delta for fewer tiles

High Memory Usage:

• Decrease --batch-size
• Process smaller areas
• Close other applications

Examples

Example 1: Process Germany

# Using country name
python gba_tiler.py --country Germany --verbose

# Using ISO-2 code
python gba_tiler.py --iso2 DE --verbose

# Using ISO-3 code
python gba_tiler.py --iso3 DEU --verbose

Example 2: Custom Tile Size

# Larger tiles for overview
python gba_tiler.py --country France --delta 0.25 --verbose

# Smaller tiles for detail
python gba_tiler.py --bbox 8.5 50.0 8.8 50.2 --delta 0.05 --debug

Example 3: High Performance Configuration

python gba_tiler.py --country Germany \
    --batch-size 5000 \
    --verbose

Example 4: Custom Directories

python gba_tiler.py --country Germany \
    --output-dir ~/data/germany_tiles \
    --temp-dir ~/data/temp \
    --verbose

Example 5: Specific Region

# Berlin area
python gba_tiler.py --bbox 13.0 52.3 13.8 52.7 --verbose

# Munich area
python gba_tiler.py --bbox 11.3 48.0 11.8 48.3 --verbose

Example 6: Version Information

# Check version
python gba_tiler.py --version

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Make your changes (follow PEP 8)
4. Add tests if applicable
5. Submit a pull request

License

Copyright (C) 2025 Clemens Drüe, Universität Trier

This project is licensed under the European Union Public License v1.2 (EUPL-1.2).

See the LICENSE file for details.

Citation

If you use this tool in research, please cite:

[Add citation information]

Contact

• Email: [e-mail]

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Last Updated: 07 Jan 2026