ArcheoVLM

Technical Architecture

Comprehensive technical stack and implementation details

2.1 Core Technologies

Platform & Language

Python 3.10+
Google Cloud Platform
GKE Autopilot

Primary Data Source

"LiDAR Surveys over Selected Forest Research Sites, Brazilian Amazon, 2008-2018" (ORNL DAAC)

2.2 Key Libraries & APIs
Comprehensive technology stack organized by domain

Geospatial Processing

• GeoPandas, Shapely
• Rasterio, Xarray
• PDAL, Laspy
• rvt-py (Relief Visualization)

Machine Learning

• PyTorch, Transformers
• Scikit-learn
• Ultralytics (YOLO)
• OpenAI GPT-4V API

Data & Cloud

• Pandas, NumPy
• google-cloud-storage
• Google Earth Engine API
• ALOS-PALSAR & NISAR

NLP & Geoparsing

• spaCy, GeospaCy
• GeoNorm
• Historical text processing

Visualization

• Folium
• Streamlit
• Interactive dashboards

Standards

• PEP 8 compliance
• Type hinting
• Comprehensive logging
• config.yaml management
2.3 Project Standards
Code quality and configuration management

Code Quality

  • • PEP 8 compliance
  • • Comprehensive logging
  • • Type hinting
  • • Robust error handling

Configuration

  • • Central config.yaml
  • • File path management
  • • Model parameters
  • • API key handling

Modularity

  • • Distinct modules per phase
  • • Well-documented classes
  • • Reusable components
  • • Clear interfaces
Data Processing Pipeline
End-to-end workflow architecture

Input

Raw LiDAR (.laz files)

~3,154 files

Processing

GKE Autopilot batch jobs

Scalable

Output

Verified site database

Georeferenced

Data flows through collaborative triage → LiDAR processing → AI detection → multi-modal verification → expert review