| --- |
| license: cc-by-3.0 |
| pretty_name: GLiM API Backend Data |
| size_categories: |
| - 1M<n<10M |
| tags: |
| - geospatial |
| - geology |
| - lithology |
| - earth-science |
| - gis |
| - pandas |
| --- |
| |
| <p align="center"> |
| <img src="https://i.ibb.co/YTTkV4W5/file-0000000001748243a5fdcd08844666f0.png" alt="GLiM API Backend banner" width="100%"> |
| </p> |
|
|
| <h1 align="center">GLiM API Backend</h1> |
| <p align="center"><i>A lightweight, production-ready API for global lithological data — built for AI training, geospatial analysis, and low-memory deployment.</i></p> |
|
|
| <p align="center"> |
| <img src="https://img.shields.io/badge/Python-3.10%2B-blue?logo=python&logoColor=white" alt="Python"> |
| <img src="https://img.shields.io/badge/GeoPandas-Spatial%20Engine-green?logo=geopandas&logoColor=white" alt="GeoPandas"> |
| <img src="https://img.shields.io/badge/Data-GLiM%20v1.0-orange" alt="GLiM"> |
| <img src="https://img.shields.io/badge/License-CC--BY--3.0-lightgrey" alt="License"> |
| <img src="https://img.shields.io/badge/Deploy-Render-46E3B7?logo=render&logoColor=white" alt="Render"> |
| </p> |
|
|
| --- |
|
|
| ## Overview |
|
|
| The **GLiM API Backend** serves lithological (rock type) data for any point on Earth's land surface, sourced from the [Global Lithological Map (GLiM)](https://doi.org/10.1029/2012GC004370) database (Hartmann & Moosdorf, 2012). It exposes fast point and batch lookups without requiring the ~2.7 GB raw geodatabase to be loaded in full — making it practical to run on constrained environments like Render's free tier. |
|
|
| This is a sibling project to [**AGDFS**](https://agdfs.onrender.com) (Automated Geological Data Fetching System), following the same design philosophy: unify large, awkward-to-host geoscience datasets behind a simple API, so downstream AI training and analysis pipelines never need to touch the raw source files directly. |
|
|
| --- |
|
|
| ## Data Source |
|
|
| | | | |
| |---|---| |
| | **Dataset** | Global Lithological Map (GLiM) Geodatabase | |
| | **Authors** | Hartmann, J. & Moosdorf, N. (2012) | |
| | **Citation** | Hartmann, J., Moosdorf, N. (2012). *The new global lithological map database GLiM: A representation of rock properties at the Earth surface.* Geochemistry, Geophysics, Geosystems, 13, Q12004. https://doi.org/10.1029/2012GC004370 | |
| | **License** | CC-BY-3.0 — attribution required in any derived product | |
| | **Raw size** | ~1.1 GB compressed / ~2.7 GB uncompressed (File Geodatabase, ~1.24M polygons) | |
|
|
| > **Note:** Always retain the citation above wherever this API's data is used downstream — it's a condition of the CC-BY-3.0 license. |
|
|
| --- |
|
|
| ## Production Files |
|
|
| Rather than serving the raw geodatabase, the pipeline splits GLiM into two purpose-built files that keep the API's memory footprint low: |
|
|
| | File | Contents | Purpose | |
| |---|---|---| |
| | `glim_spatial_index.parquet` | `polygon_id`, `geometry` only | Loaded at API startup for fast spatial lookups via GeoPandas' spatial index. Stripped of attribute columns to minimize RAM. | |
| | `glim_metadata.csv` | All lithological attributes (age, rock type, lithology class, etc.), indexed by `polygon_id` | Queried **only after** a spatial match is found — attributes are never loaded per-polygon during the spatial search itself. | |
|
|
| This split is the core of the memory optimization: the expensive part of a lookup (spatial search) runs against a geometry-only file, and the cheap part (attribute retrieval) runs against a plain indexed table. |
|
|
| --- |
|
|
| ## How It Works |
|
|
| ``` |
| Incoming request (lat, lon) |
| │ |
| ▼ |
| Spatial index query — geopandas .sindex.query(point, predicate="intersects") |
| │ |
| ├── Match found ──────────────► polygon_id |
| │ |
| └── No match (near-coast / boundary) |
| │ |
| ▼ |
| Fallback: .nearest() neighbor search → closest land polygon |
| │ |
| ▼ |
| polygon_id |
| │ |
| ▼ |
| Attribute lookup — glim_metadata.csv indexed by polygon_id |
| │ |
| ▼ |
| Response: { lithology, rock_class, age, ..., polygon_id } |
| ``` |
|
|
| 1. **Spatial query** — the incoming point is tested against `glim_spatial_index.parquet`'s in-memory R-tree via `sindex.query(point, predicate="intersects")`. |
| 2. **Fallback logic** — if the point falls just offshore or on a polygon boundary and returns no direct match, a `.nearest()` neighbor search finds the closest land polygon instead of returning empty. |
| 3. **Attribute fetch** — once a `polygon_id` is resolved, the corresponding row is pulled from `glim_metadata.csv` (or a database equivalent) and merged into the response. |
|
|
| --- |
|
|
| ## API Endpoints |
|
|
| > Endpoint paths below reflect the intended workflow described above — adjust to match your actual route implementation. |
|
|
| | Method | Path | Description | |
| |---|---|---| |
| | `GET` | `/lithology/point?lat={lat}&lon={lon}` | Single coordinate lookup — returns lithology attributes for the containing (or nearest) polygon. | |
| | `POST` | `/lithology/batch` | Batch lookup — accepts a list of `{lat, lon}` points, returns results for all in one call. | |
| | `GET` | `/health` | Service health check. | |
| | `GET` | `/mcp` | MCP layer for agent/tool integration (if enabled, matching the AGDFS pattern). | |
|
|
| --- |
|
|
| ## Tech Stack |
|
|
| - **[GeoPandas](https://geopandas.org/)** + **[Pyogrio](https://pyogrio.readthedocs.io/)** — fast geodatabase and Parquet I/O |
| - **[Shapely](https://shapely.readthedocs.io/)** — geometric operations and point-in-polygon tests |
| - **[PyArrow](https://arrow.apache.org/docs/python/)** — high-performance Parquet storage engine |
| - **FastAPI** — API layer (recommended, consistent with AGDFS) |
|
|
| --- |
|
|
| ## Project Structure |
|
|
| ``` |
| glim-api-backend/ |
| ├── data/ |
| │ ├── glim_spatial_index.parquet # geometry + polygon_id only |
| │ └── glim_metadata.csv # attributes indexed by polygon_id |
| ├── app/ |
| │ ├── main.py # FastAPI app + endpoints |
| │ ├── lookup.py # spatial query + fallback logic |
| │ └── config.py |
| ├── requirements.txt |
| └── README.md |
| ``` |
|
|
| --- |
|
|
| ## Getting Started |
|
|
| ```bash |
| git clone https://github.com/Nora-Research-Lab/<repo-name>.git |
| cd <repo-name> |
| pip install -r requirements.txt |
| uvicorn app.main:app --reload |
| ``` |
|
|
| --- |
|
|
| ## Data Hosting |
|
|
| Given the size of the production files, large data assets are hosted externally rather than committed to the repo — following the same pattern as [AGDFS's Hugging Face dataset](https://huggingface.co/datasets/Adedoyinjames/AGDFS-DATA): |
|
|
| ``` |
| https://huggingface.co/datasets/<your-org-or-username>/GLiM-DATA |
| ``` |
|
|
| The API downloads/caches `glim_spatial_index.parquet` and `glim_metadata.csv` from this location at startup or build time. *(Replace with your actual dataset repo once published.)* |
|
|
| --- |
|
|
| ## Deployment Notes |
|
|
| Built with Render's free tier (and similarly memory-constrained hosts) in mind: |
| - Only the stripped-down spatial index is loaded into memory — never the full 2.7 GB geodatabase. |
| - The spatial index (`sindex`) is built **once** at startup and reused across all requests. |
| - Attribute data stays out-of-memory-critical-path, queried lazily per match. |
|
|
| --- |
|
|
| ## License |
|
|
| Code: add your preferred license here. |
| Data: GLiM is licensed **CC-BY-3.0** — see citation above. |
|
|
| --- |
|
|
| <p align="center"><i>Part of the <a href="https://github.com/Nora-Research-Lab">NORA Research Lab</a> geoscience API suite.</i></p> |
|
|
