ArcheoVLM
Phase 2: Automated Triage & Prioritization
Spatially categorize all LiDAR tiles to isolate and prioritize high-potential, unexplored areas
Goal
Spatially categorize all LiDAR tiles to isolate and prioritize high-potential, unexplored areas using computational analysis and GIS data layers.
Data Sources
GIS layers for spatial analysis
Archaeological Data
AmazonGeoArchDB
Known archaeological site shapefiles for reference analysis
Development Data
OpenStreetMap
Modern development vectors for exclusion zones
Environmental Data
PRODES
Deforestation layers for landscape analysis
LiDAR Metadata
Tile Footprints
Spatial boundaries and coverage information
Spatial Analysis Process
Automated categorization workflow
Processing Steps:
- • Data Loading: Load LiDAR tile footprints and GIS layers into GeoDataFrames
- • Spatial Joins: Cross-reference tiles with known sites, development, and deforestation
- • Categorization: Flag and sort tiles based on proximity analysis
- • File Organization: Copy raw .laz files to appropriate subdirectories
Discovered Archaeology
Tiles near known sites
Reference
Modern Development
Tiles with recent activity
Excluded
High Potential Raw
Unexplored areas for analysis
Priority
Execution Checklist
Phase 2 tasks and deliverables
Acquire and pre-process all necessary GIS data layers
Store GIS data in /01_inventory_and_gis_data/
Develop triage_tiles.py script for automated spatial cross-referencing
Implement automated prioritization algorithms
Run final triage_tiles.py script to categorize all tiles
Verify that raw.laz files are correctly copied to /02_filtered_tiles/ subdirectories
Expected Outputs
Prioritized Tile List
High-potential LiDAR tiles identified for processing
Organized File Structure
Raw .laz files sorted into appropriate categories