Upload README.md

README.md

@@ -1,15 +1,176 @@
-# GLiM Lithology API Data
+---
+license: cc-by-3.0
+pretty_name: GLiM API Backend Data
+size_categories:
+- 1M<n<10M
+tags:
+- geospatial
+- geology
+- lithology
+- earth-science
+- gis
+- pandas
+---
 
-This repository contains optimized spatial data derived from the Global Lithological Map (GLiM) database, prepared for Nora-Research-Lab.
+<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
-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.
+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.
+
+---
 
-## 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.
+<p align="center"><i>Part of the <a href="https://github.com/Nora-Research-Lab">NORA Research Lab</a> geoscience API suite.</i></p>