add model card

README.md

@@ -0,0 +1,106 @@
+---
+tags:
+- geospatial
+- geobase
+- wetland-segmentation
+---
+| <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 = wetland-segmentation`
+
+### 🛠 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/wetland-segmentation.mp4"></video>
+
+---
+### 🚀 Demo
+
+Explore the model in action with the interactive [Demo](https://docs.geobase.app/geoai-live/tasks/wetland-segmentation).
+
+### 📦 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: [
+      [
+        [-99.0983079371952, 46.60892272965549],
+        [-99.0983079371952, 46.5949877901148],
+        [-99.07778265091567, 46.5949877901148],
+        [-99.07778265091567, 46.60892272965549],
+        [-99.0983079371952, 46.60892272965549],
+      ],
+    ],
+    type: "Polygon",
+  },
+} as GeoJSON.Feature;
+
+// Initialize pipeline
+const pipeline = await geoai.pipeline(
+  [{ task: "wetland-segmentation" }],
+  providerParams
+);
+ 
+// Run detection
+const result = await pipeline.inference({
+  inputs: { polygon }
+});
+ 
+// Sample output format
+// {
+//     "detections": {
+//         "type": "FeatureCollection",
+//         "features": [
+//             {
+//                 "type": "Feature",
+//                 "properties": {
+//                 },
+//                 "geometry": {
+//                     "type": "Polygon",
+//                     "coordinates": [
+//                         [
+//                             [54.69479163045772, 24.766579711184693],
+//                             [54.69521093930892, 24.766579711184693],
+//                             [54.69521093930892, 24.766203991224682],
+//                             [54.69479163045772, 24.766203991224682],
+//                             [54.69479163045772, 24.766579711184693],
+//                         ]
+//                     ]
+//                 }
+//             },
+//             {"type": 'Feature', "properties": {…}, "geometry": {…}}, 
+//             {"type": 'Feature', "properties": {…}, "geometry": {…}},
+//         ]
+//     },
+//     "geoRawImage": 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/wetland-segmentation
+- GitHub Repo: https://github.com/decision-labs/geobase-ai.js