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Format Support

Portolan converts data to cloud-native formats (GeoParquet, COG) for efficient cloud storage and querying.

Supported Formats

Input Format Output Format Notes
Shapefile GeoParquet Auto-converted
GeoJSON GeoParquet Auto-converted
GeoPackage GeoParquet Auto-converted
CSV (with geometry) GeoParquet Auto-converted
TIFF/GeoTIFF COG Auto-converted
JPEG2000 COG Auto-converted
GeoParquet GeoParquet Already cloud-native
COG COG Already cloud-native
FlatGeobuf — Accepted as-is (cloud-native)

ESRI File Geodatabase Rasters

Raster data stored in ESRI File Geodatabases (.gdb) cannot be converted by Portolan. The format was reverse-engineered exclusively for GDAL—no pure Python library can read it.

Workaround: Pre-convert to COG using GDAL before adding to your catalog:

# List rasters in the geodatabase
gdalinfo input.gdb

# Convert to COG
gdal_translate input.gdb/raster_name output.tif -of COG

Then add the resulting COG to your catalog as usual.

Vector GDB data is supported

This limitation applies only to raster data in geodatabases. Vector layers in .gdb files work normally.

Multi-Layer Formats

GeoPackage and FileGDB files can contain multiple vector layers. Portolan handles these specially:

Format Layer Detection Notes
GeoPackage ✅ geoparquet-io No external dependencies
FileGDB ✅ geoparquet-io No external GDAL required

API Functions

  • list_layers(path) — Returns list of layer names, or None for single-layer formats
  • is_multilayer(path) — Returns True if file has more than one layer
  • convert_multilayer_file(source, output_dir) — Converts each layer to a separate GeoParquet file

Output Naming

Each layer becomes a separate file: {source_stem}_{layer_name}.parquet

multilayer.gpkg (3 layers)
├── points
├── lines
└── polygons

→ multilayer_points.parquet
→ multilayer_lines.parquet
→ multilayer_polygons.parquet

COG Conversion Settings

COG (Cloud-Optimized GeoTIFF) conversion can be configured via config.yaml:

conversion:
  cog:
    compression: DEFLATE  # DEFLATE, LZW, ZSTD, JPEG, WEBP
    tile_size: 512        # 256, 512, 1024
    predictor: 2          # 1=none, 2=horizontal, 3=floating-point
    resampling: nearest   # nearest, bilinear, cubic, lanczos, average
    quality: 75           # JPEG/WEBP quality (1-100)

See get_cog_settings() and CogSettings for programmatic access.