arcgis-nb 0.2.6
A tool for extracting and processing LiDAR data for roofing projects in New Brunswick
Requires Python: >=3.7

# ArcGIS NB - New Brunswick GIS and LiDAR Processing Tool

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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
- **2D LiDAR Visualization**: View LiDAR data as a 2D terrain map with customizable colormaps
- **Google Maps Integration**: Optional satellite imagery from Google Maps (requires API key)
- **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)
- Google Maps API key (optional, for Google satellite imagery)

## 🔧 Installation

### From PyPI (Recommended)

```bash
pip install arcgis-nb
```

### From Source

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

### Development Installation

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

## 🚀 Quick Start

```bash
# 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:

```bash
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:

```python
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)

### Google Maps Integration

To use Google Maps satellite imagery:
1. Set the `GOOGLE_API_KEY` environment variable with your API key
2. The tool will automatically fetch Google Maps imagery when available
3. This provides an alternative view to GeoNB's imagery

### 2D LiDAR Visualization

The tool now supports 2D visualization of LiDAR data:
```python
from arcgis_nb import arcgis

# Visualize LiDAR data as 2D terrain map
arcgis.visualize_laz_2d(
    "path/to/lidar.laz",
    point_size=0.5,
    cmap="terrain",
    alpha=0.6,
    show=True
)
```

## 📦 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
- **lazrs**: For efficient LiDAR LAZ file compression and decompression

## 📄 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: amad.gakkhar@adept-techsolutions.com