A sophisticated AI vision skill that extracts structured data, safety metrics, and progress measurements from construction photos and drawings.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install image-to-data
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install image-to-data using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Image To Data?
The Image To Data skill is a specialized computer vision and OCR engine designed specifically for the construction industry, following the Data-Driven Construction (DDC) methodology. It enables developers to transform visual assets—such as site photos, scanned blueprints, and floor plans—into machine-readable data structures. By integrating these Openclaw Skills, teams can automate the extraction of critical information that previously required manual entry.
This tool leverages multiple specialized engines to handle different visual tasks, including object detection for equipment and safety gear, and structured text extraction for drawing title blocks. Whether you are managing a digital twin or auditing site safety, these Openclaw Skills provide the technical foundation for high-fidelity data extraction from complex construction imagery.
Image To Data Use Cases
- Automating the digitization of scanned floor plans and architectural drawings by extracting title block data.
- Performing automated safety audits by detecting the presence of PPE like hard hats and safety vests in site photos.
- Tracking project progress by calculating completion percentages of structural elements from progress photos.
- Identifying and logging heavy equipment and materials on-site for inventory and logistics management.
- Converting tabular data found in physical construction documents into structured JSON or CSV formats.
How Image To Data Works
- The user provides raw image data and specifies the image category, such as a site photo or elevation drawing.
- The OCREngine processes the image to identify text regions and applies template matching to structure technical metadata.
- The ObjectDetector utilizes pre-trained construction models to locate specific objects like excavators, workers, or scaffolding.
- The TableExtractor identifies and parses grid-based data into headers and row-based records.
- The ProgressAnalyzer evaluates the visual state of building elements to estimate completion percentages.
- The ConstructionImageAnalyzer aggregates all findings into a comprehensive ImageAnalysisResult for export or further processing.
Image To Data Setup
To integrate this functionality into your environment, ensure you have the necessary computer vision libraries installed. These Openclaw Skills are compatible with standard Python vision stacks.
# Install core dependencies for OCR and Object Detection
pip install pytesseract ultralytics opencv-python
Initialize the analyzer within your application:
from image_to_data import ConstructionImageAnalyzer
analyzer = ConstructionImageAnalyzer()
Image To Data Data Schema & Taxonomy
The skill produces a detailed metadata taxonomy that allows for seamless integration into larger data-driven construction workflows.
| Component | Data Output | Description |
|---|---|---|
TextRegion |
text, bbox, confidence |
OCR results with spatial coordinates. |
DetectedObject |
label, attributes, safety_zone |
Identified equipment, PPE, or structural elements. |
ExtractedTable |
headers, rows, records |
Tabular data converted into a structured list of dictionaries. |
ProgressMeasurement |
percent_complete, area_sqft |
Quantified construction progress metrics. |
SafetyResult |
compliance_score, violations |
Audit results based on detected safety gear. |
Image To Data Advanced Features
- Real-time safety compliance checking with specific violation types and counts.
- Multi-language support for OCR, enabling analysis of international technical drawings.
- Batch analysis capabilities to process large volumes of site imagery in a single workflow.
- Site-to-plan comparison logic to identify deviations from architectural designs.
- Support for custom construction classes, allowing for the detection of niche equipment or materials.
SKILL.md
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