drone-site-survey

What this skill does

# Drone Site Survey Processing

## Overview

This skill implements drone data processing for construction site monitoring. Process aerial imagery to generate maps, measure volumes, track progress, and compare with design models.

**Capabilities:**
- Orthomosaic generation
- Digital Elevation Model (DEM) creation
- Point cloud processing
- Volume calculations
- Progress monitoring
- BIM comparison
- Stockpile measurement

## Quick Start

```python
from dataclasses import dataclass
from typing import List, Dict, Tuple, Optional
from datetime import datetime
import numpy as np

@dataclass
class DroneImage:
    filename: str
    timestamp: datetime
    latitude: float
    longitude: float
    altitude: float
    heading: float
    pitch: float
    roll: float
    camera_model: str

@dataclass
class PointCloud:
    points: np.ndarray  # Nx3 array
    colors: Optional[np.ndarray] = None  # Nx3 RGB
    normals: Optional[np.ndarray] = None  # Nx3

@dataclass
class VolumeResult:
    volume_m3: float
    area_m2: float
    method: str
    reference_plane: str
    confidence: float

def calculate_volume_simple(point_cloud: PointCloud,
                           reference_z: float = None) -> VolumeResult:
    """Simple volume calculation from point cloud"""
    points = point_cloud.points

    if reference_z is None:
        reference_z = np.min(points[:, 2])

    # Grid-based volume calculation
    x_min, x_max = np.min(points[:, 0]), np.max(points[:, 0])
    y_min, y_max = np.min(points[:, 1]), np.max(points[:, 1])

    grid_size = 0.5  # 50cm grid
    x_bins = np.arange(x_min, x_max + grid_size, grid_size)
    y_bins = np.arange(y_min, y_max + grid_size, grid_size)

    volume = 0
    cell_area = grid_size ** 2

    for i in range(len(x_bins) - 1):
        for j in range(len(y_bins) - 1):
            mask = (
                (points[:, 0] >= x_bins[i]) & (points[:, 0] < x_bins[i + 1]) &
                (points[:, 1] >= y_bins[j]) & (points[:, 1] < y_bins[j + 1])
            )
            cell_points = points[mask]
            if len(cell_points) > 0:
                max_z = np.max(cell_points[:, 2])
                height = max_z - reference_z
                if height > 0:
                    volume += height * cell_area

    area = (x_max - x_min) * (y_max - y_min)

    return VolumeResult(
        volume_m3=volume,
        area_m2=area,
        method='grid_based',
        reference_plane=f'z={reference_z:.2f}',
        confidence=0.9
    )

# Example usage
sample_points = np.random.rand(10000, 3) * [100, 100, 10]  # 100x100m, 10m height
point_cloud = PointCloud(points=sample_points)
result = calculate_volume_simple(point_cloud)
print(f"Volume: {result.volume_m3:.2f} m³, Area: {result.area_m2:.2f} m²")
```

## Comprehensive Drone Survey System

### Image Processing Pipeline

```python
from dataclasses import dataclass, field
from typing import List, Dict, Tuple, Optional
from datetime import datetime
import numpy as np
from pathlib import Path
import json

@dataclass
class CameraParameters:
    focal_length_mm: float
    sensor_width_mm: float
    sensor_height_mm: float
    image_width_px: int
    image_height_px: int

@dataclass
class GeoReference:
    crs: str  # Coordinate Reference System (e.g., "EPSG:4326")
    origin: Tuple[float, float, float]  # lat, lon, alt
    rotation: Tuple[float, float, float]  # heading, pitch, roll

@dataclass
class SurveyFlight:
    flight_id: str
    date: datetime
    site_name: str
    images: List[DroneImage]
    camera: CameraParameters
    geo_reference: GeoReference
    flight_altitude: float
    overlap_forward: float = 0.8
    overlap_side: float = 0.7
    gsd: float = 0  # Ground Sample Distance (cm/pixel)

    def __post_init__(self):
        if self.gsd == 0 and self.camera:
            # Calculate GSD
            sensor_width = self.camera.sensor_width_mm
            focal_length = self.camera.focal_length_mm
            image_width = self.camera.image_width_px
            altitude = self.flight_altitude

            self.gsd = (altitude * sensor_width) / (focal_length * image_width) * 100  # cm

@dataclass
class ProcessingResult:
    orthomosaic_path: Optional[str] = None
    dem_path: Optional[str] = None
    dsm_path: Optional[str] = None
    point_cloud_path: Optional[str] = None
    report_path: Optional[str] = None
    statistics: Dict = field(default_factory=dict)

class DroneDataProcessor:
    """Process drone survey data"""

    def __init__(self, output_dir: str):
        self.output_dir = Path(output_dir)
        self.output_dir.mkdir(parents=True, exist_ok=True)

    def process_survey(self, flight: SurveyFlight,
                      generate_ortho: bool = True,
                      generate_dem: bool = True,
                      generate_pointcloud: bool = True) -> ProcessingResult:
        """Process drone survey data"""
        result = ProcessingResult()
        result.statistics['flight_id'] = flight.flight_id
        result.statistics['image_count'] = len(flight.images)
        result.statistics['gsd_cm'] = flight.gsd
        result.statistics['flight_date'] = flight.date.isoformat()

        # In production, these would call actual photogrammetry libraries
        # like OpenDroneMap, Pix4D API, or custom SfM pipeline

        if generate_ortho:
            result.orthomosaic_path = str(self.output_dir / f"{flight.flight_id}_ortho.tif")
            result.statistics['ortho_resolution'] = flight.gsd

        if generate_dem:
            result.dem_path = str(self.output_dir / f"{flight.flight_id}_dem.tif")
            result.dsm_path = str(self.output_dir / f"{flight.flight_id}_dsm.tif")

        if generate_pointcloud:
            result.point_cloud_path = str(self.output_dir / f"{flight.flight_id}_pointcloud.las")

        # Generate report
        result.report_path = str(self.output_dir / f"{flight.flight_id}_report.json")
        with open(result.report_path, 'w') as f:
            json.dump(result.statistics, f, indent=2)

        return result

    def extract_point_cloud(self, las_path: str) -> PointCloud:
        """Extract point cloud from LAS file"""
        # In production, use laspy or similar
        # Simulated point cloud for demonstration
        n_points = 100000
        points = np.random.rand(n_points, 3) * [100, 100, 20]
        colors = np.random.randint(0, 255, (n_points, 3), dtype=np.uint8)

        return PointCloud(points=points, colors=colors)

    def compare_surveys(self, survey1: ProcessingResult,
                       survey2: ProcessingResult) -> Dict:
        """Compare two surveys for change detection"""
        # Load point clouds
        pc1 = self.extract_point_cloud(survey1.point_cloud_path)
        pc2 = self.extract_point_cloud(survey2.point_cloud_path)

        # Calculate elevation differences
        # In production, use proper point cloud registration and comparison

        comparison = {
            'survey1_date': survey1.statistics.get('flight_date'),
            'survey2_date': survey2.statistics.get('flight_date'),
            'point_count_diff': len(pc2.points) - len(pc1.points),
            'changes_detected': []
        }

        return comparison
```

### Volume Calculation Engine

```python
from scipy.spatial import Delaunay
from scipy.interpolate import griddata
import numpy as np

class VolumeCalculator:
    """Advanced volume calculations from drone data"""

    def __init__(self, point_cloud: PointCloud):
        self.points = point_cloud.points
        self.colors = point_cloud.colors

    def calculate_cut_fill(self, design_surface: np.ndarray,
                          grid_size: float = 0.5) -> Dict:
        """Calculate cut and fill volumes compared to design surface"""
        # Create grid
        x_min, x_max = np.min(self.points[:, 0]), np.max(self.points[:, 0])
        y_min, y_max = np.min(self.points[:, 1]), np.max(self.points[:, 1])

        x_grid = np.arange(x_min, x_max, grid_size)
        y_grid = np.arange(y_