drone-site-survey

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---
name: "drone-site-survey"
description: "Process drone survey data for construction sites. Generate orthomosaics, DEMs, point clouds, calculate volumes, track progress, and integrate with BIM models for comparison."
homepage: "https://datadrivenconstruction.io"
metadata: {"openclaw": {"emoji": "🚀", "os": ["darwin", "linux", "win32"], "homepage": "https://datadrivenconstruction.io", "requires": {"bins": ["python3"]}}}
---
# 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