Datasets:
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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>
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