GeoParquet Input Processing

GeoParquet Input Processing is the foundational ingestion layer for modern automated vector tile generation. As mapping platforms transition away from legacy Shapefiles and verbose GeoJSON, the columnar efficiency of Apache Parquet combined with standardized spatial metadata has become the industry baseline for high-throughput cartographic pipelines. This phase handles schema validation, coordinate reference system (CRS) enforcement, geometry sanitization, and attribute pruning before data reaches the tile encoder. When implemented correctly, it eliminates downstream bottlenecks, reduces memory overhead, and guarantees deterministic output across distributed build environments.

For engineering teams operating within Automated Generation Pipelines with Tippecanoe, establishing a robust ingestion routine is non-negotiable. Raw spatial datasets rarely arrive in a tile-ready state — they contain mixed geometries, inconsistent typing, null spatial references, and unoptimized attribute payloads. GeoParquet Input Processing transforms these raw inputs into a predictable, partitioned, and spatially indexed format that downstream tile builders can consume without runtime failures or silent data degradation.

Prerequisites & Environment Setup

Before implementing the ingestion workflow, ensure your execution environment meets the following baseline requirements:

Python 3.10+ with virtual environment isolation (venv or uv)
Core Libraries: geopandas>=1.0, pyarrow>=14.0, duckdb>=0.9, shapely>=2.0, pandas>=2.0
GDAL/OGR compiled with Parquet and FlatGeobuf drivers (recommended for legacy fallback and advanced projection grids)
Hardware: Minimum 16 GB RAM for datasets under 5 GB; 32 GB+ recommended for continental-scale inputs
Storage: NVMe SSD or high-IOPS network volume for chunked read/write operations
Spatial Reference Knowledge: Familiarity with EPSG:4326 (WGS84) requirements for web mapping tile grids

The official GeoParquet specification mandates that spatial columns include explicit CRS metadata and bounding box statistics directly in the Parquet schema. Leveraging this metadata during ingestion prevents costly reprojection operations later in the pipeline and ensures interoperability across different GIS engines.

Deterministic Ingestion Workflow

GeoParquet Input Processing follows a strict, sequential pipeline designed to maximize throughput while maintaining spatial integrity.

1. Schema Inspection & Metadata Validation

Read the Parquet metadata without loading geometries into memory. Using pyarrow.parquet.read_metadata(), verify that the geometry column exists, matches the expected type (POINT, LINESTRING, POLYGON, or MULTI*), and contains valid CRS definitions. Reject files with missing spatial metadata or ambiguous column naming. Early schema validation prevents OOM crashes and ensures consistent column ordering across distributed workers.

2. CRS Enforcement & Reprojection

Web tile engines universally require EPSG:4326 (WGS84) longitude/latitude coordinates. If the source CRS differs, apply a deterministic transformation using pyproj or geopandas.to_crs(). Always validate the transformation bounds and handle edge cases near the antimeridian or polar regions. For high-performance environments, DuckDB’s spatial extension can execute CRS transformations in parallel across row groups.

3. Geometry Sanitization & Topology Repair

Raw spatial data frequently contains self-intersections, ring orientation violations, or mixed geometry collections. Apply shapely.make_valid() to repair invalid polygons, and explicitly explode MULTI* geometries into single features if your downstream encoder requires homogeneous geometry types. This stage is also where you should integrate Geometry Simplification Algorithms to reduce vertex density before tiling.

4. Attribute Pruning & Type Normalization

Vector tile encoders compress attributes, but unnecessary columns still inflate memory usage and increase build times. Drop unused fields, cast numeric columns to the smallest viable data type (e.g., int32 instead of int64), and standardize string encodings to UTF-8. Handle NaN and None values explicitly by filling defaults or dropping rows, depending on your data contract.

5. Spatial Partitioning & Chunking

Large GeoParquet files must be partitioned to enable parallel processing and efficient random access. Sort the dataset by a spatial index (e.g., H3 hexagons or S2 cell IDs) and write row groups aligned to geographic boundaries. Aim for row group sizes between 128 MB and 256 MB to balance I/O throughput with memory constraints.

Reliable Implementation Patterns

Memory management and fault tolerance are the primary concerns when processing multi-gigabyte spatial datasets. The following Python implementation demonstrates a production-ready ingestion pattern using chunked reading, explicit error handling, and schema preservation.

Note: GeoDataFrame.from_arrow() requires geopandas>=1.0. For older versions, convert via WKB: gpd.GeoDataFrame(geometry=gpd.GeoSeries.from_wkb(batch.column("geometry").to_pylist())).

python

import geopandas as gpd
import pyarrow.parquet as pq
import pandas as pd
from pathlib import Path
import logging

logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")

def process_geoparquet(input_path: Path, output_path: Path, target_crs: str = "EPSG:4326"):
    # 1. Validate metadata without loading data
    metadata = pq.read_metadata(str(input_path))
    schema_meta = metadata.schema.to_arrow_schema().metadata
    if b"geo" not in (schema_meta or {}):
        raise ValueError("Missing 'geo' extension in Parquet metadata")

    logging.info("Schema validated. Reading in chunks...")

    # 2. Chunked processing for memory safety
    parquet_file = pq.ParquetFile(str(input_path))
    processed_chunks = []

    for batch_idx, batch in enumerate(parquet_file.iter_batches(batch_size=500_000)):
        # GeoDataFrame.from_arrow requires geopandas>=1.0
        gdf = gpd.GeoDataFrame.from_arrow(batch)

        # 3. CRS enforcement
        if gdf.crs is None or gdf.crs.to_string() != target_crs:
            gdf = gdf.to_crs(target_crs)

        # 4. Geometry repair & validation
        gdf["geometry"] = gdf["geometry"].make_valid()
        gdf = gdf[gdf["geometry"].is_valid].copy()

        # 5. Attribute pruning (example: keep only essential fields)
        keep_cols = ["id", "name", "type", "geometry"]
        gdf = gdf[[c for c in keep_cols if c in gdf.columns]]

        processed_chunks.append(gdf)
        logging.info(f"Processed chunk {batch_idx + 1}")

    # Concatenate and write optimized output
    final_gdf = gpd.GeoDataFrame(pd.concat(processed_chunks, ignore_index=True))
    final_gdf.to_parquet(str(output_path), compression="snappy", engine="pyarrow")
    logging.info(f"Successfully wrote {len(final_gdf)} features to {output_path}")

import geopandas as gpd
import pyarrow.parquet as pq
import pandas as pd
from pathlib import Path
import logging

logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")

def process_geoparquet(input_path: Path, output_path: Path, target_crs: str = "EPSG:4326"):
    # 1. Validate metadata without loading data
    metadata = pq.read_metadata(str(input_path))
    schema_meta = metadata.schema.to_arrow_schema().metadata
    if b"geo" not in (schema_meta or {}):
        raise ValueError("Missing 'geo' extension in Parquet metadata")

    logging.info("Schema validated. Reading in chunks...")

    # 2. Chunked processing for memory safety
    parquet_file = pq.ParquetFile(str(input_path))
    processed_chunks = []

    for batch_idx, batch in enumerate(parquet_file.iter_batches(batch_size=500_000)):
        # GeoDataFrame.from_arrow requires geopandas>=1.0
        gdf = gpd.GeoDataFrame.from_arrow(batch)

        # 3. CRS enforcement
        if gdf.crs is None or gdf.crs.to_string() != target_crs:
            gdf = gdf.to_crs(target_crs)

        # 4. Geometry repair & validation
        gdf["geometry"] = gdf["geometry"].make_valid()
        gdf = gdf[gdf["geometry"].is_valid].copy()

        # 5. Attribute pruning (example: keep only essential fields)
        keep_cols = ["id", "name", "type", "geometry"]
        gdf = gdf[[c for c in keep_cols if c in gdf.columns]]

        processed_chunks.append(gdf)
        logging.info(f"Processed chunk {batch_idx + 1}")

    # Concatenate and write optimized output
    final_gdf = gpd.GeoDataFrame(pd.concat(processed_chunks, ignore_index=True))
    final_gdf.to_parquet(str(output_path), compression="snappy", engine="pyarrow")
    logging.info(f"Successfully wrote {len(final_gdf)} features to {output_path}")

This pattern avoids full dataset materialization in RAM, enforces strict geometry validity, and guarantees that the output Parquet file adheres to the GeoParquet standard. For teams managing terabyte-scale inputs, replacing the geopandas concatenation step with a DuckDB INSERT INTO pipeline or a direct pyarrow.dataset write will further reduce memory pressure and improve write throughput.

Downstream Integration & Validation

Once the ingestion layer produces a clean GeoParquet artifact, validate it before entering the tile generation queue. Automated validation should verify feature counts, bounding box alignment, and attribute schema consistency against a baseline manifest.

The cleaned output feeds directly into Tippecanoe CLI Fundamentals, where the encoder consumes the pre-sorted, CRS-normalized data. Because GeoParquet Input Processing handles heavy lifting upfront, Tippecanoe can operate with minimal flags, relying on the input’s spatial ordering for efficient tile boundary clipping.

Performance & Scale Considerations

At scale, ingestion performance is dictated by I/O patterns, columnar compression, and parallel execution strategies. Use snappy or zstd compression for a balance of speed and file size reduction. Avoid gzip on geometry columns, as it significantly increases CPU overhead during read operations. When deploying to cloud environments, leverage object storage lifecycle rules to archive raw inputs after successful ingestion, and store processed GeoParquet files in a dedicated staging bucket.

For teams managing continuous map updates, scheduling incremental ingestion jobs ensures that only modified regions are reprocessed. This delta-based approach minimizes compute costs and keeps tile caches fresh. For detailed guidance on handling massive datasets that exceed single-node memory limits, refer to the companion guide on Converting Large GeoParquet Files to Vector Tiles.

Next reading Converting Large GeoParquet Files to Vector Tiles