Skip to main content

Python bindings for geo-polygonize-core, a native Rust port of the JTS/GEOS polygonization algorithm.

Project description

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 library provides native Python bindings via PyO3, packaged as geo-polygonize.

import numpy as np
from geo_polygonize import polygonize

# 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 dictionary with 'flat_coords', 'ring_offsets', 'polygon_offsets', etc.
result_dict = polygonize(coords=coords, offsets=offsets)

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 (Bundlers / Browser): 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.

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 (Manual Loading): If you prefer to manage the Wasm binary yourself (e.g., to reduce bundle size or load from a CDN), import from geo-polygonize/slim.

import { initBest, polygonize } from "geo-polygonize/slim";

async function run() {
    // You must provide the compiled WebAssembly.Module or URL
    // You can choose to load the SIMD or Scalar version based on your own detection or availability
    const response = await fetch("geo_polygonize.wasm");
    const buffer = await response.arrayBuffer();
    const module = await WebAssembly.compile(buffer);

    // Helper to initialize the best available implementation
    // Pass the module to both arguments if you only have one version
    await initBest(module, module);

    // ... use polygonize
}

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

Project details


Download files

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

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

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

geo_polygonize_py-0.20.0-cp37-abi3-win_amd64.whl (840.2 kB view details)

Uploaded CPython 3.7+Windows x86-64

geo_polygonize_py-0.20.0-cp37-abi3-manylinux_2_35_x86_64.whl (971.3 kB view details)

Uploaded CPython 3.7+manylinux: glibc 2.35+ x86-64

geo_polygonize_py-0.20.0-cp37-abi3-macosx_11_0_arm64.whl (838.9 kB view details)

Uploaded CPython 3.7+macOS 11.0+ ARM64

File details

Details for the file geo_polygonize_py-0.20.0-cp37-abi3-win_amd64.whl.

File metadata

File hashes

Hashes for geo_polygonize_py-0.20.0-cp37-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 75df749eec98ca0333e0df155388c70d188e29191c14fcd64ea2973168411632
MD5 ef033824339a9929b2c461683e91cefa
BLAKE2b-256 8d01d298464764466afebd469814a31377f10c4d53788b920ddaf0a6d9f13ce3

See more details on using hashes here.

File details

Details for the file geo_polygonize_py-0.20.0-cp37-abi3-manylinux_2_35_x86_64.whl.

File metadata

File hashes

Hashes for geo_polygonize_py-0.20.0-cp37-abi3-manylinux_2_35_x86_64.whl
Algorithm Hash digest
SHA256 aea2fba290ae2de5f4a238419c9ba3ac91d83544c4c85e72491d87363cb7323b
MD5 1e6762e2726009ceb8e53d60521c6935
BLAKE2b-256 f17bf1738f116bfecd036ea1a4a8dec0eeac4fba05a29818e17284c0684e7e42

See more details on using hashes here.

File details

Details for the file geo_polygonize_py-0.20.0-cp37-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for geo_polygonize_py-0.20.0-cp37-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 cd2db2fef484e63659c0f4be45ece990cafbee3261c2521145563d8ba5ab05fd
MD5 ec1b9e20c074e83c2802b8a43887476d
BLAKE2b-256 2b067bdb832bb6e96e2c7cce7e03604da1b048252f0fae7cb5b00edb5b6afada

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page