Geo Polygonize

A native Rust port of the JTS/GEOS polygonization algorithm. This crate allows you to reconstruct valid polygons from a set of lines, including handling of complex topologies like holes, nested shells, and disconnected components.

Ask DeepWiki

Features

  • Robust Polygonization: Extracts polygons from unstructured linework.
  • Robust Noding: Implements Iterated Snap Rounding (ISR) to guarantee topological correctness on dirty inputs (self-intersections, overlaps).
  • Hardware Acceleration: Uses SIMD instructions (via wide crate) for critical geometric predicates like Point-in-Polygon checks.
  • Wasm Optimized: Tailored for WebAssembly with talc allocator and Zero-Copy data support (geoarrow).
  • Performance: Competitive with GEOS/Shapely (C++), outperforming it on random sparse inputs and scaling well on dense grids.
  • Geo Ecosystem: Fully integrated with geo-types and geo crates.
  • GeoArrow Support: Zero-copy data transfer via Arrow C Data Interface and Arrow IPC (Wasm).

Engineering Roadmap

For an ambitious, prioritized plan covering performance, security, API consistency, and maintainability, see docs/roadmap.md.

Usage

Library

use geo_polygonize_core::Polygonizer;
use geo_types::LineString;

fn main() {
    let mut poly = Polygonizer::new();

    // Enable robust noding if lines might intersect
    poly.node_input = true;
    // Optional: Configure snap grid (default 1e-10)
    poly.snap_grid_size = 1e-6;

    // Add lines (e.g., a square with diagonals)
    poly.add_geometry(LineString::from(vec![
        (0.0, 0.0), (10.0, 0.0), (10.0, 10.0), (0.0, 10.0), (0.0, 0.0)
    ]).into());
    poly.add_geometry(LineString::from(vec![
        (0.0, 0.0), (10.0, 10.0)
    ]).into());

    let polygons = poly.polygonize().expect("Polygonization failed");

    for p in polygons {
        println!("Found polygon with area: {}", p.unsigned_area());
    }
}

Choosing node_input and snap_grid_size

Polygonization quality is heavily influenced by input noding strategy.

  • node_input = false (default): Fastest path. Use this when your input linework is already noded (all intersections are explicit vertices).
  • node_input = true: Enables Iterated Snap Rounding (ISR). Use this for real-world datasets that may contain slight misalignments, overlaps, or self-intersections.
  • snap_grid_size controls how aggressively coordinates are snapped during robust noding:
    • Start with 1e-10 for high-precision projected data.
    • Increase to 1e-8 or 1e-6 when near-duplicate vertices prevent clean topology.
    • Avoid very large values unless your coordinate units are coarse; oversnapping can collapse narrow features.

Practical workflow:

  1. Run with node_input = false first on trusted data.
  2. If you observe missing polygons, sliver artifacts, or unresolved intersections, enable node_input.
  3. Tune snap_grid_size upward incrementally until topology stabilizes.

Output semantics

The polygonizer intentionally returns only valid polygonal areas that can be formed from closed cycles:

  • Dangles are removed: dead-end edges do not appear in output polygons.
  • Cut edges are excluded: edges that are connected but cannot bound a face are ignored.
  • Holes and nested shells are preserved when enough boundary information is present.

This behavior matches classical JTS/GEOS polygonization semantics and is useful for cleaning linework before area analysis.

GeoArrow Integration

The library supports ingesting data directly from Arrow arrays via the arrow_api module and ffi.

use geo_polygonize_core::arrow_api::{polygonize_arrow, PolygonizerOptions};
// ... create Arrow array ...
// let result = polygonize_arrow(&array, &field, options);

Python

The Python package is published as geo-polygonize-py and imported as geo_polygonize.

pip install geo-polygonize-py

import numpy as np
from geo_polygonize import polygonize, import_probe

# 1. Using Shapely LineStrings or coordinate lists directly
lines = [
    [(0, 0), (10, 0), (10, 10), (0, 10), (0, 0)],
    [(0, 0), (10, 10)]
]

# return_polygons=True returns a list of shapely.geometry.Polygon objects
polygons = polygonize(lines=lines, return_polygons=True)
for p in polygons:
    print(p.area)

# 2. Using High-Performance Flat Arrays
# Perfect for zero-copy integrations or massive datasets
coords = np.array([
    0.0, 0.0, 10.0, 0.0, 10.0, 10.0, 0.0, 10.0, 0.0, 0.0,
    0.0, 0.0, 10.0, 10.0
], dtype=np.float64)

# Start indices for each line segment.
# The final closing offset is computed implicitly.
offsets = np.array([0, 5], dtype=np.uint32)

# Returns a stable dictionary with 'polygons', diagnostics, and provenance.
result_dict = polygonize(coords=coords, offsets=offsets)

# Native-extension probes are cheap and safe for optional integrations.
ok, error = import_probe()

CFB/autograder integrations should use the versioned production profile rather than assembling caller-side knobs or using legacy polygonize(..., node=True, snap=0.5) calls:

from geo_polygonize import cfb_robust_options, polygonize_with_options

result = polygonize_with_options(
    coords=coords,
    offsets=offsets,
    options=cfb_robust_options(),
)

The default return shape is a stable dictionary with polygons as SimplePolygon values. Use return_polygons=True only when you want Shapely Polygon objects.

For Shapely parity checks, compare report-mode outputs with the built-in mismatch helper:

from geo_polygonize import explain_mismatch, polygonize_with_options

options = cfb_robust_options()
result_a = polygonize_with_options(coords=coords_a, offsets=offsets_a, options=options)
result_b = polygonize_with_options(coords=coords_b, offsets=offsets_b, options=options)
result_a["options"] = options
result_b["options"] = options

mismatch = explain_mismatch(result_a, result_b)

For a minimal Shapely smoke comparison, use area signatures:

from shapely.ops import polygonize as shapely_polygonize

rust_polys = polygonize_with_options(lines=lines, options=cfb_robust_options(), return_polygons=True)
rust_areas = sorted(round(poly.area, 6) for poly in rust_polys)
shapely_areas = sorted(round(poly.area, 6) for poly in shapely_polygonize(lines))

WebAssembly (WASM)

This library supports WebAssembly with an ergonomic dual-build configuration that automatically utilizes SIMD instructions where available.

Installation:

npm install geo-polygonize

Standard Usage (Quick Demos): The default entry point automatically handles feature detection (SIMD) and lazy-loading of the Wasm binary. The Wasm is inlined as a Base64 Data URI, so no extra bundler configuration is needed. For app builds, prefer the slim entry point below so your bundler keeps the Wasm assets out of the JavaScript chunk.

import init, { polygonize, polygonize_geoarrow } from "geo-polygonize";

async function run() {
    await init();

    const geojson = {
        "type": "FeatureCollection",
        "features": [
            // ... your line features
        ]
    };

    // Returns a GeoJSON FeatureCollection string
    // Pass explicitly matching backend configuration if desired
    const result = polygonize(
        JSON.stringify(geojson),
        true, // node_input
        0.5   // snap_grid_size
    );
    console.log(JSON.parse(result));

    // Or use Arrow IPC bytes
    // const ipcBuffer = ...;
    // const arrowResult = polygonize_geoarrow(ipcBuffer, false, 1e-10, false);
}

Slim Usage (Apps / Manual Loading): For Vite and other app bundlers, import from geo-polygonize/slim and pass explicit Wasm asset URLs.

import { cfbRobustOptions, initBest } from "geo-polygonize/slim";
import scalarUrl from "geo-polygonize/geo_polygonize.wasm?url";
import simdUrl from "geo-polygonize/geo_polygonize_simd.wasm?url";

async function run() {
    const wasm = await initBest(
        { module_or_path: scalarUrl },
        { module_or_path: simdUrl },
    );

    const result = wasm.polygonizeWithOptions(
        JSON.stringify(geojson),
        cfbRobustOptions,
    );
}

Multithreaded Usage (Experimental): This library provides a multithreaded build powered by wasm-bindgen-rayon.

import init, { initThreadPool, polygonize } from "geo-polygonize/threads";

async function run() {
    await init();

    // Initialize thread pool (e.g., with navigator.hardwareConcurrency)
    await initThreadPool(navigator.hardwareConcurrency);

    // ... use polygonize as usual
}

Important: Multithreaded WebAssembly requires SharedArrayBuffer, which is only available in secure contexts. You must serve your page with the following headers:

Cross-Origin-Opener-Policy: same-origin
Cross-Origin-Embedder-Policy: require-corp

CLI Example

The repository includes a CLI tool to polygonize GeoJSON files.

# Build the example
cargo build -p geo-polygonize-core --example polygonize --release

# Run on input lines
cargo run -p geo-polygonize-core --release --example polygonize -- --input lines.geojson --output polygons.geojson --node

Visualization

You can visualize the results using the provided Python script (requires matplotlib and shapely).

python3 scripts/visualize.py --input lines.geojson --output polygons.geojson --save result.png

Examples

Below are some examples of what the polygonizer can do.

Nested Holes and Islands

The algorithm correctly identifies nested structures (Island inside a Hole inside a Shell).

Nested Holes

Incomplete Grid / Dangles

The algorithm prunes dangles (dead-end lines) and extracts only closed cycles.

Incomplete Grid

Touching Polygons (Shared Edges)

Using robust noding (--node), it can reconstruct adjacent polygons that share boundaries, even if the input lines are not perfectly noded.

Touching Polygons

Self-Intersecting Geometry (Bowtie)

Self-intersecting lines are split at intersection points, and valid cycles are extracted.

Bowtie

Complex Geometries

The polygonizer can handle complex, curved inputs (approximated by LineStrings) such as overlapping circles and shapes with multiple holes.

Overlapping Circles: Note how the intersection regions are correctly identified as separate polygons.

Overlapping Circles

Curved Holes: A complex polygon with multiple circular holes.

Curved Holes

Benchmarks

This library includes a "severe" comparison suite against shapely (GEOS).

See BENCHMARKS.md for detailed results and instructions on how to run them.

Architecture

This implementation moves away from the pointer-based graph structures of JTS/GEOS to a Rust-idiomatic Index Graph (Arena) approach.

See ARCHITECTURE.md for a deep dive into the optimization strategies.

Key optimizations include:

  1. Robust Noding: Iterated Snap Rounding (ISR) using rstar for intersection detection and grid snapping.
  2. Vectorization: SIMD-accelerated Ray Casting for efficient Hole Assignment.
  3. Memory Layout: Structure of Arrays (SoA) for graph nodes and talc allocator for Wasm.

License

MIT/Apache-2.0

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (1.1.0)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for graydonpleasants from gravatar.com graydonpleasants

License

MIT OR Apache-2.0

Requires

Python >=3.8

Classifiers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

geo_polygonize_py-0.37.5.tar.gz (134.8 kB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

geo_polygonize_py-0.37.5-cp38-abi3-win_amd64.whl (851.4 kB view details)

Uploaded CPython 3.8+Windows x86-64

geo_polygonize_py-0.37.5-cp38-abi3-manylinux_2_35_x86_64.whl (984.4 kB view details)

Uploaded CPython 3.8+manylinux: glibc 2.35+ x86-64

geo_polygonize_py-0.37.5-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (936.7 kB view details)

Uploaded CPython 3.8+manylinux: glibc 2.17+ ARM64

geo_polygonize_py-0.37.5-cp38-abi3-macosx_11_0_arm64.whl (851.5 kB view details)

Uploaded CPython 3.8+macOS 11.0+ ARM64

File details

Details for the file geo_polygonize_py-0.37.5.tar.gz.

File metadata

  • Download URL: geo_polygonize_py-0.37.5.tar.gz
  • Upload date:
  • Size: 134.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for geo_polygonize_py-0.37.5.tar.gz
Algorithm Hash digest
SHA256 e66865eb84472e5cf23b2dff1d8c25e3bade4336920331a67cdb71c282b3005e
MD5 b09163cb8f53708b5ad8588907a7b4c4
BLAKE2b-256 42641e2881b2c3e61dd3a83e38012e39998b3ee60be9d7deee5352e351c6b497

See more details on using hashes here.

Provenance

The following attestation bundles were made for geo_polygonize_py-0.37.5.tar.gz:

Publisher: publish-python.yml on graydonpleasants/geo-polygonize

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file geo_polygonize_py-0.37.5-cp38-abi3-win_amd64.whl.

File metadata

File hashes

Hashes for geo_polygonize_py-0.37.5-cp38-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 4fc0606fcaa78a50decfab2894a8127dff34f468ac405ee1597063b6ddc687f2
MD5 b2093ac8ade76b9c53ec56c18fd7bc33
BLAKE2b-256 98eca8647df156d8a798d262d908ea09938d1a0acb8178d5e2633bd758351dbe

See more details on using hashes here.

Provenance

The following attestation bundles were made for geo_polygonize_py-0.37.5-cp38-abi3-win_amd64.whl:

Publisher: publish-python.yml on graydonpleasants/geo-polygonize

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file geo_polygonize_py-0.37.5-cp38-abi3-manylinux_2_35_x86_64.whl.

File metadata

File hashes

Hashes for geo_polygonize_py-0.37.5-cp38-abi3-manylinux_2_35_x86_64.whl
Algorithm Hash digest
SHA256 9532f82ad3e731d6f17ae185ed85f768aac31f13164a8da205a976ed6a49403a
MD5 8b51a9db515c29c297fa2ecd1dafd7e5
BLAKE2b-256 2b17271dac73a877b3884ec0ac221c43b3454ade9572b09688f951be284e69e1

See more details on using hashes here.

Provenance

The following attestation bundles were made for geo_polygonize_py-0.37.5-cp38-abi3-manylinux_2_35_x86_64.whl:

Publisher: publish-python.yml on graydonpleasants/geo-polygonize

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file geo_polygonize_py-0.37.5-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for geo_polygonize_py-0.37.5-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 f91d41df49c49a2fd5328c721cf58bd943a562c3186ab0ebb3e0ae7aba03789f
MD5 e52d690a50fec8211fa791430b5b91d8
BLAKE2b-256 38b7a7087788c57d9b4290ddffd5768c0a10246be72a092e951956eb51d8406a

See more details on using hashes here.

Provenance

The following attestation bundles were made for geo_polygonize_py-0.37.5-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl:

Publisher: publish-python.yml on graydonpleasants/geo-polygonize

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file geo_polygonize_py-0.37.5-cp38-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for geo_polygonize_py-0.37.5-cp38-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 27457a0f15acac869a198f4244e3ba5d763c7f69d6853f4095b152d8867a7629
MD5 e4a5d38302152747dfb204002dbbbfe6
BLAKE2b-256 2a8eea69abb61e2b587bac3ffd36e4f103f45f33b0e5eb9263f7471c7123930c

See more details on using hashes here.

Provenance

The following attestation bundles were made for geo_polygonize_py-0.37.5-cp38-abi3-macosx_11_0_arm64.whl:

Publisher: publish-python.yml on graydonpleasants/geo-polygonize

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

1.1.0

5 files

1.0.0

5 files

0.76.2

5 files

0.76.1

5 files

0.76.0

5 files

0.75.0

5 files

0.74.0

5 files

0.73.0

5 files

0.72.0

5 files

0.71.0

5 files

0.70.0

5 files

0.69.0

5 files

0.68.0

5 files

0.67.0

5 files

0.66.0

5 files

0.65.0

5 files

0.64.0

5 files

0.63.0

5 files

0.62.0

5 files

0.61.0

5 files

0.60.0

5 files

0.59.0

5 files

0.58.1

5 files

0.58.0

5 files

0.57.0

5 files

0.56.0

5 files

0.55.0

5 files

0.54.0

5 files

0.53.0

5 files

0.52.0

5 files

0.51.2

5 files

0.51.1

5 files

0.51.0

5 files

0.50.0

5 files

0.49.0

5 files

0.48.0

5 files

0.47.1

5 files

0.47.0

5 files

0.46.2

5 files

0.46.1

5 files

0.46.0

5 files

0.45.1

5 files

0.45.0

5 files

0.44.0

5 files

0.43.0

5 files

0.42.0

5 files

0.41.0

5 files

0.40.1

5 files

0.40.0

5 files

0.39.13

5 files

0.39.12

5 files

0.39.11

5 files

0.39.10

5 files

0.39.9

5 files

0.39.8

5 files

0.39.7

5 files

0.39.6

5 files

0.39.5

5 files

0.39.4

5 files

0.39.3

5 files

0.39.2

5 files

0.39.1

5 files

0.39.0

5 files

0.38.1

5 files

0.38.0

5 files

0.37.8

5 files

0.37.7

5 files

0.37.6

5 files

This release

0.37.5 This release

5 files

0.37.4

5 files

0.37.3

5 files

0.37.2

5 files

0.37.1

5 files

0.37.0

5 files

0.36.2

5 files

0.36.1

5 files

0.36.0

5 files

0.35.2

5 files

0.35.1

4 files

0.35.0

4 files

0.34.0

3 files

0.33.1

3 files

0.33.0

3 files

0.23.1

3 files

0.23.0

3 files

0.22.1

3 files

0.22.0

3 files

0.21.0

3 files

0.20.0

3 files

0.19.0

3 files

0.18.1

3 files

0.18.0

3 files

0.17.4

3 files

0.17.3

3 files

0.17.2

3 files

0.17.1

3 files

0.17.0

3 files

0.16.0

3 files

0.15.0

3 files

0.14.1

3 files

0.14.0

3 files

0.13.0

3 files

0.12.1

3 files

0.12.0

3 files

0.11.0

3 files

0.10.0

3 files

0.9.0

3 files

0.8.1

3 files

0.8.0

3 files

0.7.0

3 files

0.6.3

3 files

0.6.2

3 files

0.6.1

3 files

0.6.0

3 files

0.5.0

3 files

0.4.2

3 files

0.4.1

3 files

Yanked

0.1.0

5 files

Yanked reason: wrong version number

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page