add model card

README.md

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+---
+tags:
+- geospatial
+- geobase
+- land-cover-classification
+---
+| <img src="https://upload.wikimedia.org/wikipedia/commons/6/6a/JavaScript-logo.png" width="28" height="28"> | [@geobase-js/geoai](https://www.npmjs.com/package/@geobase-js/geoai) |
+|---|---|
+
+
+
+> `task = land-cover-classification`
+
+### 🛠 Model Purpose
+This model is part of the **[@geobase-js/geoai](https://github.com/geobase-ai/geoai)** javascript library.
+
+**GeoAi** enables geospatial AI inference **directly in the browser or Node.js** without requiring a heavy backend.
+
+**GeoAi** pipeline accepts **geospatial polygons** as input (in GeoJSON format) and outputs results as a **GeoJSON FeatureCollection**, ready for use with libraries like **Leaflet** and **Mapbox GL**.
+
+<video controls autoplay loop width="1024" height="720" src="https://geobase-docs.s3.amazonaws.com/geobase-ai-assets/land-cover-classification.mp4"></video>
+
+---
+### 🚀 Demo
+
+Explore the model in action with the interactive [Demo](https://docs.geobase.app/geoai-live/tasks/land-cover-classification).
+
+### 📦 Model Information
+- **Architecture**: MaskRCNN
+- **Source Model**: https://opengeoai.org/examples/wetland_mapping/
+- **Quantization**: Yes
+---
+
+> **Note:** This model only works if the input imagery contains both RGB and NIR bands.
+
+
+### 💡 Example Usage
+
+```javascript
+import { geoai } from "@geobase-js/geoai";
+
+// Example polygon (GeoJSON)
+const polygon = {
+  type: "Feature",
+  properties: {},
+  geometry: {
+    coordinates: [
+      [
+        [-99.98170823334205, 50.64318249608178],
+        [-99.9806018185983, 50.6431476272638],
+        [-99.9807117895183, 50.642403469489295],
+        [-99.98256386074388, 50.64247150726101],
+        [-99.98170823334205, 50.64318249608178]
+      ],
+    ],
+    type: "Polygon",
+  },
+} as GeoJSON.Feature;
+
+// Initialize pipeline
+const pipeline = await geoai.pipeline(
+  [{ task: "land-cover-classification" }],
+  providerParams
+);
+ 
+// Run detection
+const result = await pipeline.inference({
+  inputs: { polygon }
+});
+ 
+// Sample output format
+// {
+//     "detections": [
+//      {type: 'FeatureCollection', features: Array(6)},
+//      {type: 'FeatureCollection', features: Array(43)},
+//      {type: 'FeatureCollection', features: Array(6)},
+//      {type: 'FeatureCollection', features: Array(3)},
+//      {type: 'FeatureCollection', features: Array(11)},
+//      {type: 'FeatureCollection', features: Array(36)},
+//      {type: 'FeatureCollection', features: Array(0)},
+//      {type: 'FeatureCollection', features: Array(9)}
+//    ]
+//     "outputimage": GeoRawImage {data: Uint8ClampedArray(1048576), width: 512, height: 512, channels: 4, bounds: {…}, …}
+// }
+
+```
+### 📖 Documentation & Demo
+
+- GeoBase Docs: https://docs.geobase.app/geoai
+- NPM Package: https://www.npmjs.com/package/@geobase-js/geoai
+- Demo Playground: https://docs.geobase.app/geoai-live/tasks/land-cover-classification
+- GitHub Repo: https://github.com/decision-labs/geobase-ai.js