drone-site-survey

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

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")