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geospatial toolkit (whitebox_next_gen) compiled to WebAssembly (WASI) for GeoLibre's in-browser tool execution","maintainers":[{"name":"giswqs","email":"giswqs@gmail.com"}],"readme":"# geolibre-wasm\n\n[![npm version](https://img.shields.io/npm/v/geolibre-wasm.svg)](https://www.npmjs.com/package/geolibre-wasm)\n[![npm downloads](https://img.shields.io/npm/dm/geolibre-wasm.svg)](https://www.npmjs.com/package/geolibre-wasm)\n[![license](https://img.shields.io/npm/l/geolibre-wasm.svg)](https://github.com/opengeos/geolibre-rust#license)\n\nA pure-Rust geospatial toolkit compiled to WebAssembly, runnable entirely in the\nbrowser, Node, Deno, or any bundler. No server, no Python, no native install.\n\nIt is a **superset of [`whitebox-wasm`](https://www.npmjs.com/package/whitebox-wasm)**:\neverything that package offers, plus new\n[GeoLibre](https://github.com/opengeos/GeoLibre) tools. Built on\n[`opengeos/whitebox-wasm`](https://github.com/opengeos/whitebox-wasm), the\nWASM-ready fork of [`whitebox_next_gen`](https://github.com/jblindsay/whitebox_next_gen).\n\nSource repo: [opengeos/geolibre-rust](https://github.com/opengeos/geolibre-rust).\n\nTwo layers, two entry points:\n\n- **`geolibre-wasm`** (the `.` export) -- a `wasm-bindgen` browser library with\n  typed in-memory APIs for GeoTIFF/COG read+write, projections, vector, and LiDAR.\n- **`geolibre-wasm/tools`** (the `./tools` export) -- a WASI tool runner exposing\n  the whitebox tool suite plus GeoLibre's own tools.\n\n## Install\n\n```bash\nnpm install geolibre-wasm\n```\n\n`geolibre-wasm/tools` uses [`@bjorn3/browser_wasi_shim`](https://github.com/bjorn3/browser_wasi_shim)\n(a runtime dependency) to run the WASI binary over an in-memory filesystem.\n\n## Library usage (`.`)\n\n```js\nimport init, { GeoTiffReader, CogBuilder, geotiff_info, version } from \"geolibre-wasm\";\n\nawait init(); // load the wasm-bindgen module (browsers/bundlers)\n\nconst r = new GeoTiffReader(tiffBytes); // Uint8Array\nconsole.log(r.width, r.height, r.bands, r.epsg);\nconst band0 = r.read_band_f64(0);        // Float64Array\n\n// header-only metadata (works on multi-GB files)\nconst meta = JSON.parse(geotiff_info(tiffBytes));\n```\n\nClasses include `GeoTiffReader` (parse once, read many), `CogBuilder` (encode\nCloud Optimized GeoTIFFs), and `CogStream` (range-request tiled COG reading).\n\n### Vector data as binary, not GeoJSON\n\n`vector_to_geojson` returns a string, so the consumer pays for `JSON.parse` plus\na re-encode into typed arrays before anything reaches the GPU. Two binary paths\nskip both steps, reading the same formats through the same reader\n(`geojson`, `topojson`, `gml`, `gpx`, `kml`, `flatgeobuf`, `geopackage`,\n`geoparquet`, `kmz` -- see `vector_formats()`):\n\n```js\nimport init, { vector_to_binary, vector_to_arrow_ipc } from \"geolibre-wasm\";\nawait init();\n\n// deck.gl binary attributes: Float64Array positions + Uint32Array indices.\nconst b = vector_to_binary(bytes, \"geoparquet\");\nb.point_positions();          // Float64Array, position_size values per vertex\nb.numeric_column(0);          // Float64Array, one value per feature\nJSON.parse(b.schema_json);    // [{name, type}, ...]\nb.free();\n\n// GeoArrow record batch for apache-arrow, DuckDB-WASM, @geoarrow/deck.gl-layers.\nconst ipc = vector_to_arrow_ipc(bytes, \"flatgeobuf\"); // Uint8Array\n```\n\nReprojecting variants (`vector_to_binary_reproject`,\n`vector_to_arrow_ipc_reproject`) take `dst_epsg` and `src_epsg`, where `0`\nmeans \"use the layer's own CRS\".\n\n## Tools usage (`./tools`)\n\n```js\nimport { runTool, listTools, listManifests } from \"geolibre-wasm/tools\";\n\n// every available tool id\nconst tools = await listTools();\n\n// run a tool: inputs go into an in-memory /work dir; new files come back out\nconst { exitCode, stdout, files } = await runTool(\"slope\", {\n  args: [\"--input=/work/dem.tif\", \"--output=/work/slope.tif\", \"--units=degrees\"],\n  input: { \"dem.tif\": demBytes }, // Uint8Array\n});\nconst slopeCog = files[\"slope.tif\"]; // Uint8Array (Cloud Optimized GeoTIFF)\n```\n\nCOG subsets can be extracted from a local file or an HTTP source. HTTP sources\nuse byte-range requests and do not download the full object:\n\n```js\nconst { files } = await runTool(\"extract_cog_subset\", {\n  args: [\n    \"--url=https://example.com/scene.cog.tif\",\n    \"--bbox=-122.55,37.70,-122.35,37.84\",\n    \"--bbox_crs=4326\",\n    \"--output_crs=3857\",\n    \"--resolution=10\",\n    \"--nodata=-9999\",\n    \"--output=/work/subset.tif\",\n  ],\n});\nconst subsetCog = files[\"subset.tif\"];\n```\n\nWMS subsets can be requested from a GetMap endpoint that supports GeoTIFF\nresponses:\n\n```js\nconst { files } = await runTool(\"extract_wms_subset\", {\n  args: [\n    \"--url=https://example.com/geoserver/wms\",\n    \"--layers=workspace:layer\",\n    \"--bbox=-122.55,37.70,-122.35,37.84\",\n    \"--bbox_crs=4326\",\n    \"--resolution=0.0001\",\n    \"--format=image/geotiff\",\n    \"--output=/work/wms_subset.tif\",\n  ],\n});\n```\n\nXYZ tile subsets can be mosaicked from raster tiles and written as an RGB COG:\n\n```js\nconst { files } = await runTool(\"extract_xyz_tile_subset\", {\n  args: [\n    \"--url=https://tile.openstreetmap.org/{z}/{x}/{y}.png\",\n    \"--zoom=10\",\n    \"--bbox=-122.55,37.70,-122.35,37.84\",\n    \"--bbox_crs=4326\",\n    \"--width=512\",\n    \"--height=512\",\n    \"--output=/work/xyz_subset.tif\",\n  ],\n});\n```\n\nFor a local COG, upload/provide bytes through `input` and point `--input` at\nthe `/work` path:\n\n```js\nconst { files } = await runTool(\"extract_cog_subset\", {\n  args: [\n    \"--input=/work/local.cog.tif\",\n    \"--bbox=-122.55,37.70,-122.35,37.84\",\n    \"--bbox_crs=4326\",\n    \"--output=/work/subset.tif\",\n  ],\n  input: { \"local.cog.tif\": localCogBytes },\n});\n```\n\nInputs are placed under `/work` (keyed by filename); any file a tool writes is\nreturned in `files`. Raster outputs are Cloud Optimized GeoTIFFs; vector outputs\nare GeoJSON.\n\nWhen `output_crs` is set, `resolution` is interpreted in output CRS units and\nthe subset is reprojected with nearest-neighbor resampling. Without\n`output_crs`, the output usually stays on the source COG grid. Sources with\nuser-defined projection strings that cannot be written as EPSG metadata, such as\nthe NLCD sample, default to `bbox_crs` output so GIS viewers can place the\nresult. Outputs are Deflate-compressed by default. `nodata` overrides the output\nnodata metadata and is used to fill pixels introduced during reprojection.\nThe subset writer preserves the source sample type for supported COG types\n(`uint8`, `float32`, and `float64`); palette-indexed `uint8` rasters retain\ntheir source ColorMap.\n\n## API\n\n| Export | Description |\n|---|---|\n| `initTools(source?)` | Compile the WASI runner once. Omit `source` in browsers/bundlers; pass wasm bytes or a URL/Response in Node. |\n| `listTools(): Promise<string[]>` | Every available tool id. |\n| `listManifests(): Promise<ToolManifest[]>` | All tool manifests (parameter schemas), for building UIs offline. |\n| `runTool(tool, { args?, input? }): Promise<ToolResult>` | Run one tool over the in-memory filesystem. |\n| `extractCogSubset(source, opts): Promise<Uint8Array>` | Extract a local or HTTP COG subset directly. |\n| `extractWmsSubset(url, opts): Promise<Uint8Array>` | Request a WMS GeoTIFF subset and encode it as a COG. |\n| `extractXyzTileSubset(url, opts): Promise<Uint8Array>` | Mosaic XYZ raster tiles and encode them as an RGB COG. |\n\n`ToolResult` is `{ exitCode: number, stdout: string[], files: Record<string, Uint8Array> }`.\n\n## Notes\n\n- Bundler users (Vite, etc.): exclude this package from dependency pre-bundling\n  so the `new URL(\"./geolibre-cli.wasm\", import.meta.url)` reference is preserved\n  (e.g. Vite's `optimizeDeps.exclude`).\n- Bounded by WebAssembly's ~4 GiB memory and single-threaded execution; use a\n  server-side path for very large data.\n\n## License\n\nMIT\n","readmeFilename":"README.md"}