arcgis-nb 0.1.3

A tool for extracting and processing LiDAR data for roofing projects in New Brunswick

ArcGIS NB - New Brunswick GIS and LiDAR Processing Tool

A Python package for extracting and processing geospatial data from New Brunswick's GeoNB services. This tool streamlines the workflow for roofing professionals by providing easy access to satellite imagery, building footprints, and LiDAR point clouds for accurate roof measurements and analysis.

🌟 Features

• Address Geocoding: Convert New Brunswick addresses to precise coordinates using GeoNB's ArcGIS REST API
• High-Resolution Imagery: Retrieve aerial imagery for visual inspection and reference
• Building Footprints: Extract building outlines from GeoNB's building database
• Automatic Building Detection: Identify the building closest to a specified point
• LiDAR Processing: Discover, download, and process LiDAR point cloud data
• Precise Clipping: Extract only the LiDAR points that fall within a building footprint
• 3D Visualization: View the processed LiDAR data in an interactive 3D environment
• Command-Line Interface: Easy-to-use CLI for integration into workflows
• Python API: Flexible Python interface for custom applications

📋 Prerequisites

• Python 3.7 or higher
• pip package manager
• Internet connection for accessing GeoNB services
• Sufficient disk space for LiDAR data (typically 50-200MB per tile)

🔧 Installation

From PyPI (Recommended)

pip install arcgis-nb

From Source

git clone https://github.com/artisanroofing/utils/arcgis_nb.git
cd arcgis_nb
pip install -e .

Development Installation

git clone https://github.com/artisanroofing/utils/arcgis_nb.git
cd arcgis_nb
pip install -e ".[dev]"

🚀 Quick Start

# Create directories for data and output
mkdir -p data/lidar_tiles output

# Process an address with default settings
arcgis-nb --address "71 ANCHORAGE AVENUE, SAINT JOHN, NB E2K5R3"

# Process with manual point selection and 3D visualization
arcgis-nb --address "71 ANCHORAGE AVENUE, SAINT JOHN, NB E2K5R3" --point_correction --show_3d

📖 Usage

Command Line Interface

The package provides a command-line tool for processing addresses:

arcgis-nb --address "71 ANCHORAGE AVENUE, SAINT JOHN, NB E2K5R3" --output_dir ./my_project

Command Line Options

Option	Description	Default
--address	New Brunswick address (required)	None
--output_dir	Directory where output files will be saved	./output
--show_3d	Enable 3D visualization of LiDAR data	False
--no_show_3d	Disable 3D visualization	-
--point_correction	Enable manual point selection on satellite image	False
--no_point_correction	Disable manual point selection	-
--verbose	Enable detailed output messages	False
--quiet	Disable detailed output messages	-

Python API

You can also use the package as a Python library for more flexibility:

from arcgis_nb import arcgis

# Get coordinates from an address
X, Y, wgs84_coords = arcgis.get_arcgis_data("71 ANCHORAGE AVENUE, SAINT JOHN, NB E2K5R3")
print(f"Coordinates (EPSG:2036): {X}, {Y}")
print(f"WGS84 coordinates: {wgs84_coords}")  # For use with Google Maps, etc.

# Get satellite imagery
img = arcgis.get_geonb_imagery(Y, X)
img.save("satellite.png")

# Get building footprints
buildings = arcgis.get_buildings(Y, X)

# Highlight the closest building to the center
highlighted_img, geo_points = arcgis.highlight_closest_polygon_to_center(buildings, X, Y)

# Get LiDAR data information
lidar_info = arcgis.get_lidar_download_info(X, Y)
print(f"LiDAR file: {lidar_info['filename']}")
print(f"Year: {lidar_info['year']}")
print(f"Point density: {lidar_info['point_density']} points/m²")
print(f"Download URL: {lidar_info['download_url']}")

# Clip LiDAR data to building footprint
clipped_file = arcgis.clip_lidar_to_contour(
    "path/to/lidar.laz",
    "path/to/output.laz",
    geo_points
)

# Visualize the clipped LiDAR data
arcgis.visualize_laz(clipped_file, show=True)

📁 Output Files

The tool generates several output files in the specified output directory:

• satellite_image.png: Aerial imagery of the location
• building_footprint.png: Building footprints at the location
• building_footprint_highlighted.png: Building footprints with the closest building highlighted
• *_clipped.laz: LiDAR point cloud clipped to the building footprint

📊 LiDAR Data

The tool will check if the required LiDAR tile exists in the data/lidar_tiles directory. If not, it will:

1. Provide a download URL from GeoNB's LiDAR repository
2. Prompt you to download the file manually (due to authentication requirements)
3. Wait for you to confirm the download is complete
4. Proceed with processing once the file is available

LiDAR File Naming Convention

LiDAR files follow the naming convention: nb_YYYY_XXXXXXX_YYYYYYY.laz where:

• YYYY: Year of data collection
• XXXXXXX_YYYYYYY: Coordinates of the tile's lower-left corner in NB Stereographic projection

📂 Directory Structure

project/
├── output/                  # Output files
│   ├── satellite_image.png
│   ├── building_footprint.png
│   ├── building_footprint_highlighted.png
│   └── *_clipped.laz
└── data/
    └── lidar_tiles/         # Downloaded LiDAR tiles

🔍 Advanced Usage

Coordinate Systems

The tool works with two coordinate systems:

• NB Stereographic (EPSG:2036): Used by GeoNB services and LiDAR data
• WGS84 (EPSG:4326): Standard latitude/longitude, provided for reference

Manual Point Selection

When using the --point_correction flag, the tool will:

1. Display the satellite image
2. Allow you to click on the exact building of interest
3. Use the selected point for subsequent processing

This is useful when:

• The geocoded address doesn't precisely match the building location
• You want to select a specific building in a multi-building property
• The building footprint data needs manual correction

Working with Large LiDAR Files

LiDAR files can be large (50-200MB). To optimize processing:

1. Create a dedicated directory for LiDAR tiles
2. Keep downloaded tiles for future use
3. Use the clipped output for analysis (much smaller file size)

📦 Dependencies

• requests: For making HTTP requests to GeoNB services
• pyproj: For coordinate transformations between different systems
• PIL/Pillow: For image processing and manipulation
• matplotlib: For visualization and interactive point selection
• numpy: For numerical operations and array handling
• opencv-python: For contour detection and image processing
• laspy: For reading, manipulating and writing LiDAR LAS/LAZ files
• geopandas: For geospatial data handling and analysis
• shapely: For geometric operations and spatial analysis

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

This tool uses data from GeoNB (Service New Brunswick), including:

• GeoNB Basemap Imagery
• GeoNB SNB Buildings
• GeoNB SNB LiDAR Index

We acknowledge the valuable geospatial data provided by the Province of New Brunswick.

🤝 Contributing

Contributions are welcome! Here's how you can help:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📞 Support

For questions, issues, or feature requests, please:

• Open an issue on GitHub
• Contact the author: [e-mail]