Datasets:

NoraResearchLab
/

glim-lithology-api-data

@@ -1,176 +1,15 @@
----
-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/Fqs9qCJD/Chat-GPT-Image-Aug-13-2026-12-25-50-AM.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>

+# GLiM Lithology API Data
+This repository contains optimized spatial data derived from the Global Lithological Map (GLiM) database, prepared for Nora-Research-Lab.
 ## Data Hosting
+Due to file size limits, the primary data files are hosted on Hugging Face Datasets:
+[View Dataset on Hugging Face](https://huggingface.co/datasets/NoraResearchLab/glim-lithology-api-data)
+## Dataset Structure
+1. **glim_spatial_index.parquet**: Geometry-only file for fast spatial indexing.
+2. **glim_metadata.csv**: Attribute table containing lithological details.
+## Source Information
+Original Data: Global Lithological Map (GLiM) v1.0
+Citation: Hartmann, J., and Moosdorf, N. (2012), The new global lithological map database GLiM: A representation of rock properties at the Earth surface, Geochem. Geophys. Geosyst., 13, Q12004.