Geobase (models)

.gitattributes

@@ -42,3 +42,17 @@ datasets/geoai-cogs/ship-detection.tif filter=lfs diff=lfs merge=lfs -text
 datasets/geoai-cogs/solar-panel-detection.tif filter=lfs diff=lfs merge=lfs -text
 datasets/geoai-cogs/wetland-segmentation.tif filter=lfs diff=lfs merge=lfs -text
 datasets/geoai-cogs/zero-shot-object-detection.tif filter=lfs diff=lfs merge=lfs -text
+models/building-detection/building-detection.mp4 filter=lfs diff=lfs merge=lfs -text
+models/building-footprint-segmentation/building-footprint-segmentation.mp4 filter=lfs diff=lfs merge=lfs -text
+models/car-detection/car-detection-model.mp4 filter=lfs diff=lfs merge=lfs -text
+models/dinov3-vits16-pretrain-lvd1689m-ONNX/onnx/model_q4.onnx_data filter=lfs diff=lfs merge=lfs -text
+models/dinov3-vits16-pretrain-lvd1689m-ONNX/onnx/model_quantized.onnx_data filter=lfs diff=lfs merge=lfs -text
+models/dinov3-vits16-pretrain-lvd1689m-ONNX/onnx/model.onnx_data filter=lfs diff=lfs merge=lfs -text
+models/gghl-oriented-object-detection/A[[:space:]]General[[:space:]]Gaussian[[:space:]]Heatmap[[:space:]]Label[[:space:]]Assignment[[:space:]]for[[:space:]]Arbitrary-Oriented[[:space:]]Object[[:space:]]Detection.pdf filter=lfs diff=lfs merge=lfs -text
+models/gghl-oriented-object-detection/oriented-object-detection.mp4 filter=lfs diff=lfs merge=lfs -text
+models/oil-storage-tank-detection/oil-storage-detection-on-airbus-imagery-with-yolox.ipynb filter=lfs diff=lfs merge=lfs -text
+models/oil-storage-tank-detection/oil-storage-tank-detection.mp4 filter=lfs diff=lfs merge=lfs -text
+models/ship-detection/ship-detection.mp4 filter=lfs diff=lfs merge=lfs -text
+models/solar-panel-detection/solar-panel-detection.mp4 filter=lfs diff=lfs merge=lfs -text
+models/sparsemask/land-cover-classification.mp4 filter=lfs diff=lfs merge=lfs -text
+models/wetland-segmentation/wetland-segmentation.mp4 filter=lfs diff=lfs merge=lfs -text

models/WALDO30-yolov8m-640x640/.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
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+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

models/WALDO30-yolov8m-640x640/README.md

@@ -0,0 +1,10 @@
+---
+base_model:
+- StephanST/WALDO30
+tags:
+- onnx
+- yolov8
+- transformer.js
+- '@geobase-js/geoai'
+---
+This ONNX model is a converted version of the original model available at: [StephanST/WALDO30](https://huggingface.co/StephanST/WALDO30).

models/WALDO30-yolov8m-640x640/config.json

@@ -0,0 +1,31 @@
+{
+    "model_type": "yolos",
+    "id2label": {
+      "0": "LightVehicle",
+      "1": "Person",
+      "2": "Building",
+      "3": "UPole",
+      "4": "Boat",
+      "5": "Bike",
+      "6": "Container",
+      "7": "Truck",
+      "8": "Gastank",
+      "9": "Digger",
+      "10": "SolarPanels",
+      "11": "Bus"
+    },
+    "label2id": {
+      "LightVehicle": 0,
+      "Person": 1,
+      "Building": 2,
+      "UPole": 3,
+      "Boat": 4,
+      "Bike": 5,
+      "Container": 6,
+      "Truck": 7,
+      "Gastank": 8,
+      "Digger": 9,
+      "SolarPanels": 10,
+      "Bus": 11
+    }
+  }

models/WALDO30-yolov8m-640x640/onnx/model.onnx

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models/WALDO30-yolov8m-640x640/onnx/model_quantized.onnx

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models/WALDO30-yolov8m-640x640/preprocessor_config.json

@@ -0,0 +1,13 @@
+{
+    "do_normalize": false,
+    "do_pad": false,
+    "do_rescale": true,
+    "do_resize": true,
+    "feature_extractor_type": "ImageFeatureExtractor",
+    "resample": 2,
+    "rescale_factor": 0.00392156862745098,
+    "size": {
+      "width": 640,
+      "height": 640
+    }
+}

models/building-detection/.gitattributes

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

models/building-detection/README.md

@@ -0,0 +1,105 @@
+---
+tags:
+- geospatial
+- geobase
+- building-detection
+- building-footprint-detection
+license: mit
+---
+| <img src="https://upload.wikimedia.org/wikipedia/commons/6/6a/JavaScript-logo.png" width="28" height="28"> | [GeoAi](https://www.npmjs.com/package/geoai) |
+|---|---|
+
+
+
+> `task = building-detection`
+
+### 🛠 Model Purpose
+This model is part of the **[GeoAi](https://github.com/decision-labs/geoai.js)** 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/building-detection.mp4"></video>
+
+---
+### 🚀 Demo
+
+Explore the model in action with the interactive [Demo](https://docs.geobase.app/geoai-live/tasks/building-detection).
+
+### 📦 Model Information
+- **Architecture**: MaskRCNN
+- **Source Model**: https://opengeoai.org/examples/train_building_footprints_usa
+- **Quantization**: Yes
+---
+
+### 💡 Example Usage
+
+```javascript
+import { geoai } from "geoai";
+
+// Example polygon (GeoJSON)
+const polygon = {
+  type: "Feature",
+  properties: {},
+  geometry: {
+    coordinates: [
+      [
+        [-117.59239617156095, 47.653614113446906],
+        [-117.59239617156095, 47.652878388765174],
+        [-117.59040545822742, 47.652878388765174],
+        [-117.59040545822742, 47.653614113446906],
+        [-117.59239617156095, 47.653614113446906]
+      ],
+    ],
+    type: "Polygon",
+  },
+} as GeoJSON.Feature;
+
+// Initialize pipeline
+const pipeline = await geoai.pipeline(
+  [{ task: "building-detection" }],
+  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/geoai
+- Demo Playground: https://docs.geobase.app/geoai-live/tasks/building-detection
+- GitHub Repo: https://github.com/decision-labs/geoai.js

models/building-detection/building-detection.mp4

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+size 154349

models/building-detection/config.json

@@ -0,0 +1 @@
+{}

models/building-detection/onnx/model_quantized.onnx

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+size 45456422

models/building-detection/preprocessor_config.json

@@ -0,0 +1,18 @@
+{
+  "data_format": "channels_first",
+  "default_to_square": true,
+  "device": null,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "image_mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "image_std": [
+    0.229,
+    0.224,
+    0.225
+  ],
+  "return_tensors": true
+}

models/building-detection/train_building_footprints_usa.ipynb

@@ -0,0 +1,308 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qQTUAW1SVA2V"
+      },
+      "source": [
+        "# Train a Building Footprints Detection Model for the USA\n",
+        "\n",
+        "[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/opengeos/geoai/blob/main/docs/examples/train_building_footprints_usa.ipynb)\n",
+        "\n",
+        "## Install package\n",
+        "To use the `geoai-py` package, ensure it is installed in your environment. Uncomment the command below if needed."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "VnWQt3hoVA2X"
+      },
+      "outputs": [],
+      "source": [
+        "# %pip install geoai-py"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vq_xVhuYVA2Y"
+      },
+      "source": [
+        "## Import libraries"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "PdEFa-XfVA2Y"
+      },
+      "outputs": [],
+      "source": [
+        "import geoai"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KizJ2iYSVA2Y"
+      },
+      "source": [
+        "## Download sample data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "K7jqYNmMVA2Z"
+      },
+      "outputs": [],
+      "source": [
+        "train_raster_url = (\n",
+        "    \"https://huggingface.co/datasets/giswqs/geospatial/resolve/main/naip_rgb_train.tif\"\n",
+        ")\n",
+        "train_vector_url = \"https://huggingface.co/datasets/giswqs/geospatial/resolve/main/naip_train_buildings.geojson\"\n",
+        "test_raster_url = (\n",
+        "    \"https://huggingface.co/datasets/giswqs/geospatial/resolve/main/naip_test.tif\"\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "tEh9XXbQVA2Z"
+      },
+      "outputs": [],
+      "source": [
+        "train_raster_path = geoai.download_file(train_raster_url)\n",
+        "train_vector_path = geoai.download_file(train_vector_url)\n",
+        "test_raster_path = geoai.download_file(test_raster_url)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rQGZE0cyVA2Z"
+      },
+      "source": [
+        "## Visualize sample data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4wQhj5zTVA2Z"
+      },
+      "outputs": [],
+      "source": [
+        "geoai.view_vector_interactive(train_vector_path, tiles=train_raster_url)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "DCPbd9vHVA2Z"
+      },
+      "outputs": [],
+      "source": [
+        "geoai.view_raster(test_raster_url)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "w4HScHX7VA2Z"
+      },
+      "source": [
+        "## Create training data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ro0N9uV2VA2a"
+      },
+      "outputs": [],
+      "source": [
+        "out_folder = \"output\"\n",
+        "tiles = geoai.export_geotiff_tiles(\n",
+        "    in_raster=train_raster_path,\n",
+        "    out_folder=out_folder,\n",
+        "    in_class_data=train_vector_path,\n",
+        "    tile_size=512,\n",
+        "    stride=256,\n",
+        "    buffer_radius=0,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "46bZXIPjVA2a"
+      },
+      "source": [
+        "## Train object detection model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "EL-jaLrsVA2a"
+      },
+      "outputs": [],
+      "source": [
+        "geoai.train_MaskRCNN_model(\n",
+        "    images_dir=f\"{out_folder}/images\",\n",
+        "    labels_dir=f\"{out_folder}/labels\",\n",
+        "    output_dir=f\"{out_folder}/models\",\n",
+        "    num_channels=3,\n",
+        "    pretrained=True,\n",
+        "    batch_size=4,\n",
+        "    num_epochs=100,\n",
+        "    learning_rate=0.005,\n",
+        "    val_split=0.2,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OkFMZIXcVA2a"
+      },
+      "source": [
+        "## Run inference"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4QpDbBCPVA2a"
+      },
+      "outputs": [],
+      "source": [
+        "masks_path = \"naip_test_prediction.tif\"\n",
+        "model_path = f\"{out_folder}/models/building_footprints_usa.pth\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-92uqCV7VA2a"
+      },
+      "outputs": [],
+      "source": [
+        "geoai.object_detection(\n",
+        "    test_raster_path,\n",
+        "    masks_path,\n",
+        "    model_path,\n",
+        "    window_size=512,\n",
+        "    overlap=256,\n",
+        "    confidence_threshold=0.5,\n",
+        "    batch_size=4,\n",
+        "    num_channels=3,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eXXtPHj_VA2a"
+      },
+      "source": [
+        "## Vectorize masks"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LG1RNB34VA2a"
+      },
+      "outputs": [],
+      "source": [
+        "output_path = \"naip_test_prediction.geojson\"\n",
+        "gdf = geoai.orthogonalize(masks_path, output_path, epsilon=2)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AsNQs1MKVA2a"
+      },
+      "source": [
+        "## Visualize results"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "hKFpDWDaVA2a"
+      },
+      "outputs": [],
+      "source": [
+        "geoai.view_vector_interactive(output_path, tiles=test_raster_url)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "VGLbXF1QVA2a"
+      },
+      "outputs": [],
+      "source": [
+        "geoai.create_split_map(\n",
+        "    left_layer=output_path,\n",
+        "    right_layer=test_raster_url,\n",
+        "    left_args={\"style\": {\"color\": \"red\", \"fillOpacity\": 0.2}},\n",
+        "    basemap=test_raster_url,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oul-qaMUVA2a"
+      },
+      "source": [
+        "![image](https://github.com/user-attachments/assets/8dfcc69e-7a6c-408a-9fae-10b81b7d85dc)"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.9"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

models/building-footprint-segmentation/.gitattributes

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
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models/building-footprint-segmentation/README.md

@@ -0,0 +1,105 @@
+---
+tags:
+- geospatial
+- geobase
+- building-footprint-segmentation
+- building-detection
+---
+| <img src="https://upload.wikimedia.org/wikipedia/commons/6/6a/JavaScript-logo.png" width="28" height="28"> | [GeoAi](https://www.npmjs.com/package/geoai) |
+|---|---|
+
+
+
+> `task = building-footprint-segmentation`
+
+### 🛠 Model Purpose
+This model is part of the **[GeoAi](https://github.com/decision-labs/geoai.js)** 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/building-footprint-segmentation.mp4"></video>
+
+---
+### 🚀 Demo
+
+Explore the model in action with the interactive [Demo](https://docs.geobase.app/geoai-live/tasks/building-footprint-segmentation).
+
+### 📦 Model Information
+- **Architecture**: U-Net–style Convolutional Neural Network (CNN)
+- **Source Model**: [gunayk3/building_footprint_segmentation](https://huggingface.co/spaces/gunayk3/building_footprint_segmentation)
+- **Quantization**: Yes
+---
+
+### 💡 Example Usage
+
+```javascript
+import { geoai } from "geoai";
+
+// Example polygon (GeoJSON)
+const polygon = {
+  type: "Feature",
+  properties: {},
+  geometry: {
+    coordinates: [
+      [
+        [-117.42351735397804, 47.659839523657155],
+        [-117.42351735397804, 47.6533360375098],
+        [-117.41165191515506, 47.6533360375098],
+        [-117.41165191515506, 47.659839523657155],
+        [-117.42351735397804, 47.659839523657155]
+      ],
+    ],
+    type: "Polygon",
+  },
+} as GeoJSON.Feature;
+
+// Initialize pipeline
+const pipeline = await geoai.pipeline(
+  [{ task: "building_footprint_segmentation" }],
+  providerParams
+);
+ 
+// Run detection
+const result = await pipeline.inference({
+  inputs: { polygon }
+});
+ 
+// Sample output format
+// {
+//     "detections": {
+//         "type": "FeatureCollection",
+//         "features": [
+//             {
+//                 "type": "Feature",
+//                 "properties": {
+//                     "confidence": 0.8438083529472351
+//                 },
+//                 "geometry": {
+//                     "type": "Polygon",
+//                     "coordinates": [
+//                        [
+//                          [-117.41771164648438, 47.650790343749996],
+//                          [-117.41766873046875, 47.650790343749996],
+//                          [-117.41762581445313,47.650790343749996],
+//                          ...
+//                          [-117.41771164648438, 47.650790343749996]
+//                        ]
+//                     ]
+//                 }
+//             },
+//             {"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/geoai
+- Demo Playground: https://docs.geobase.app/geoai-live/tasks/building-footprint-segmentation
+- GitHub Repo: https://github.com/decision-labs/geoai.js

models/building-footprint-segmentation/building-footprint-segmentation.mp4

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models/building-footprint-segmentation/building_footprint_segmentation.zip

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models/building-footprint-segmentation/config.json

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+}

models/building-footprint-segmentation/onnx/model.onnx

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models/building-footprint-segmentation/onnx/model_quantized.onnx

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models/building-footprint-segmentation/preprocessor_config.json

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+{
+  "data_format": "channels_first",
+  "default_to_square": true,
+  "device": null,
+  "do_convert_rgb": true,
+  "return_tensors": true
+}

models/car-detection/.gitattributes

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models/car-detection/README.md

@@ -0,0 +1,105 @@
+---
+tags:
+- geospatial
+- geobase
+- car-detection
+- vehicle-detection
+license: mit
+---
+| <img src="https://upload.wikimedia.org/wikipedia/commons/6/6a/JavaScript-logo.png" width="28" height="28"> | [GeoAi](https://www.npmjs.com/package/geoai) |
+|---|---|
+
+
+
+> `task = car-detection`
+
+### 🛠 Model Purpose
+This model is part of the **[GeoAi](https://github.com/decision-labs/geoai.js)** 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/car-detection-model.mp4"></video>
+
+---
+### 🚀 Demo
+
+Explore the model in action with the interactive [Demo](https://docs.geobase.app/geoai-live/tasks/car-detection).
+
+### 📦 Model Information
+- **Architecture**: MaskRCNN
+- **Source Model**: https://opengeoai.org/examples/car_detection/
+- **Quantization**: Yes
+---
+
+### 💡 Example Usage
+
+```javascript
+import { geoai } from "geoai";
+
+// Example polygon (GeoJSON)
+const polygon = {
+  type: "Feature",
+  properties: {},
+  geometry: {
+    coordinates: [
+      [
+        [-95.42148774154262, 29.67906487977089],
+        [-95.42148774154262, 29.678781807220446],
+        [-95.4210323139897, 29.678781807220446],
+        [-95.4210323139897, 29.67906487977089],
+        [-95.42148774154262, 29.67906487977089]
+      ],
+    ],
+    type: "Polygon",
+  },
+} as GeoJSON.Feature;
+
+// Initialize pipeline
+const pipeline = await geoai.pipeline(
+  [{ task: "car-detection" }],
+  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/geoai
+- Demo Playground: https://docs.geobase.app/geoai-live/tasks/car-detection
+- GitHub Repo: https://github.com/decision-labs/geoai.js

models/car-detection/car-detection-model.mp4

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models/car-detection/car_detection.ipynb

@@ -0,0 +1,315 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fuxc8_nTVPd-"
+      },
+      "source": [
+        "# Car Detection\n",
+        "\n",
+        "[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/opengeos/geoai/blob/main/docs/examples/car_detection.ipynb)\n",
+        "\n",
+        "## Install package\n",
+        "To use the `geoai-py` package, ensure it is installed in your environment. Uncomment the command below if needed."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "exUeCEIpVPeA"
+      },
+      "outputs": [],
+      "source": [
+        "# %pip install geoai-py"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Bs2IHDHhVPeB"
+      },
+      "source": [
+        "## Import libraries"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ZKSRd09lVPeB"
+      },
+      "outputs": [],
+      "source": [
+        "import geoai"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "suhsezsIVPeB"
+      },
+      "source": [
+        "## Download sample data\n",
+        "\n",
+        "We will download a sample image from Hugging Face Hub to use for car detection. You can find more high-resolution images from [OpenAerialMap](https://openaerialmap.org)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8gtxHPS8VPeC"
+      },
+      "outputs": [],
+      "source": [
+        "raster_url = (\n",
+        "    \"https://huggingface.co/datasets/giswqs/geospatial/resolve/main/cars_7cm.tif\"\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-rUBO1n9VPeC"
+      },
+      "outputs": [],
+      "source": [
+        "raster_path = geoai.download_file(raster_url)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9ZVXVBknVPeC"
+      },
+      "source": [
+        "## Visualize the image"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "jjt39Qn3VPeC"
+      },
+      "outputs": [],
+      "source": [
+        "geoai.view_raster(raster_url)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ux5m_qo9VPeC"
+      },
+      "source": [
+        "## Initialize the model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_ycnkIw1VPeC"
+      },
+      "outputs": [],
+      "source": [
+        "detector = geoai.CarDetector()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yav6-QSZVPeD"
+      },
+      "source": [
+        "## Extract cars\n",
+        "\n",
+        "Extract cars from the image using the model and save the output image."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Ye5yIUaBVPeD"
+      },
+      "outputs": [],
+      "source": [
+        "mask_path = detector.generate_masks(\n",
+        "    raster_path=raster_path,\n",
+        "    output_path=\"cars_masks.tif\",\n",
+        "    confidence_threshold=0.3,\n",
+        "    mask_threshold=0.5,\n",
+        "    overlap=0.25,\n",
+        "    chip_size=(400, 400),\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "s9isS5SeVPeD"
+      },
+      "source": [
+        "Convert the image masks to polygons and save the output GeoJSON file."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xdUzoOsbVPeD"
+      },
+      "outputs": [],
+      "source": [
+        "gdf = detector.vectorize_masks(\n",
+        "    masks_path=\"cars_masks.tif\",\n",
+        "    output_path=\"cars.geojson\",\n",
+        "    min_object_area=100,\n",
+        "    max_object_area=2000,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QwjgQLYJVPeD"
+      },
+      "source": [
+        "## Add geometric properties"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "FdMYiW6UVPeD"
+      },
+      "outputs": [],
+      "source": [
+        "gdf = geoai.add_geometric_properties(gdf)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CNnxe0fZVPeD"
+      },
+      "source": [
+        "## Visualize initial results"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Q4k8QUTFVPeD"
+      },
+      "outputs": [],
+      "source": [
+        "geoai.view_vector_interactive(gdf, column=\"confidence\", tiles=raster_url)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "a_Ko7c-CVPeD"
+      },
+      "source": [
+        "## Filter cars by area"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "90ozn_RlVPeD"
+      },
+      "outputs": [],
+      "source": [
+        "gdf_filter = gdf[\n",
+        "    (gdf[\"area_m2\"] > 8) & (gdf[\"area_m2\"] < 60) & (gdf[\"minor_length_m\"] > 1)\n",
+        "]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "IVt91FPKVPeD"
+      },
+      "outputs": [],
+      "source": [
+        "len(gdf_filter)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "J6dCdsOlVPeD"
+      },
+      "source": [
+        "## Visualiza final results"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "L3Si-LFKVPeD"
+      },
+      "outputs": [],
+      "source": [
+        "geoai.view_vector_interactive(gdf_filter, column=\"confidence\", tiles=raster_url)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "5N-WyYMnVPeD"
+      },
+      "outputs": [],
+      "source": [
+        "geoai.view_vector_interactive(gdf_filter, tiles=raster_url)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4G1T76IEVPeD"
+      },
+      "source": [
+        "![image](https://github.com/user-attachments/assets/a1e4c871-b152-466a-b902-7c00e62e5444)"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "torch",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.8"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

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+  "data_format": "channels_first",
+  "default_to_square": true,
+  "device": null,
+  "do_convert_rgb": true,
+  "return_tensors": true
+}

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models/dinov3-vits16-pretrain-lvd1689m-ONNX/LICENSE.md

@@ -0,0 +1,66 @@
+# DINOv3 License
+
+*Last Updated: August 14, 2025*
+
+**“Agreement”** means the terms and conditions for use, reproduction, distribution and modification of the DINO Materials set forth herein.
+
+**“DINO Materials”** means, collectively, Documentation and the models, software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code, and other elements of the foregoing distributed by Meta and made available under this Agreement.
+
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models/dinov3-vits16-pretrain-lvd1689m-ONNX/README.md

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+---
+language:
+- en
+tags:
+- dino
+- dinov3
+license: other
+license_name: dinov3-license
+license_link: https://ai.meta.com/resources/models-and-libraries/dinov3-license
+pipeline_tag: image-feature-extraction
+library_name: transformers.js
+base_model:
+- facebook/dinov3-vits16-pretrain-lvd1689m
+---

models/dinov3-vits16-pretrain-lvd1689m-ONNX/config.json

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+{
+  "architectures": [
+    "DINOv3ViTModel"
+  ],
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+  "drop_path_rate": 0.0,
+  "hidden_act": "gelu",
+  "hidden_size": 384,
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "key_bias": false,
+  "layer_norm_eps": 1e-05,
+  "layerscale_value": 1.0,
+  "mlp_bias": true,
+  "model_type": "dinov3_vit",
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+  "transformers_version": "4.56.0.dev0",
+  "use_gated_mlp": false,
+  "value_bias": true,
+  "transformers.js_config": {
+    "dtype": "fp32",
+    "use_external_data_format": true
+  }
+}

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+}

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models/geo-task-classifier-transformer/README.md

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+---
+library_name: transformers
+base_model: nreimers/BERT-Tiny_L-2_H-128_A-2
+tags:
+- generated_from_trainer
+model-index:
+- name: bert_tiny_trained
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# bert_tiny_trained
+
+This model is a fine-tuned version of [nreimers/BERT-Tiny_L-2_H-128_A-2](https://huggingface.co/nreimers/BERT-Tiny_L-2_H-128_A-2) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5820
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 50
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss |
+|:-------------:|:-----:|:----:|:---------------:|
+| No log        | 1.0   | 11   | 1.3285          |
+| No log        | 2.0   | 22   | 1.3144          |
+| No log        | 3.0   | 33   | 1.2977          |
+| No log        | 4.0   | 44   | 1.2683          |
+| No log        | 5.0   | 55   | 1.2476          |
+| No log        | 6.0   | 66   | 1.2256          |
+| No log        | 7.0   | 77   | 1.1961          |
+| No log        | 8.0   | 88   | 1.1853          |
+| No log        | 9.0   | 99   | 1.1625          |
+| No log        | 10.0  | 110  | 1.1284          |
+| No log        | 11.0  | 121  | 1.1036          |
+| No log        | 12.0  | 132  | 1.0812          |
+| No log        | 13.0  | 143  | 1.0573          |
+| No log        | 14.0  | 154  | 1.0323          |
+| No log        | 15.0  | 165  | 1.0092          |
+| No log        | 16.0  | 176  | 0.9913          |
+| No log        | 17.0  | 187  | 0.9725          |
+| No log        | 18.0  | 198  | 0.9492          |
+| No log        | 19.0  | 209  | 0.9269          |
+| No log        | 20.0  | 220  | 0.9061          |
+| No log        | 21.0  | 231  | 0.8869          |
+| No log        | 22.0  | 242  | 0.8719          |
+| No log        | 23.0  | 253  | 0.8521          |
+| No log        | 24.0  | 264  | 0.8357          |
+| No log        | 25.0  | 275  | 0.8169          |
+| No log        | 26.0  | 286  | 0.8026          |
+| No log        | 27.0  | 297  | 0.7936          |
+| No log        | 28.0  | 308  | 0.7783          |
+| No log        | 29.0  | 319  | 0.7677          |
+| No log        | 30.0  | 330  | 0.7577          |
+| No log        | 31.0  | 341  | 0.7516          |
+| No log        | 32.0  | 352  | 0.7431          |
+| No log        | 33.0  | 363  | 0.7355          |
+| No log        | 34.0  | 374  | 0.7287          |
+| No log        | 35.0  | 385  | 0.7220          |
+| No log        | 36.0  | 396  | 0.7154          |
+| No log        | 37.0  | 407  | 0.7119          |
+| No log        | 38.0  | 418  | 0.7073          |
+| No log        | 39.0  | 429  | 0.7025          |
+| No log        | 40.0  | 440  | 0.6976          |
+| No log        | 41.0  | 451  | 0.6931          |
+| No log        | 42.0  | 462  | 0.6890          |
+| No log        | 43.0  | 473  | 0.6859          |
+| No log        | 44.0  | 484  | 0.6830          |
+| No log        | 45.0  | 495  | 0.6807          |
+| 0.7544        | 46.0  | 506  | 0.6785          |
+| 0.7544        | 47.0  | 517  | 0.6774          |
+| 0.7544        | 48.0  | 528  | 0.6769          |
+| 0.7544        | 49.0  | 539  | 0.6768          |
+| 0.7544        | 50.0  | 550  | 0.6767          |
+
+
+### Framework versions
+
+- Transformers 4.48.3
+- Pytorch 2.5.1+cu124
+- Datasets 3.3.2
+- Tokenizers 0.21.0

models/geo-task-classifier-transformer/config.json

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+{
+  "_name_or_path": "nreimers/BERT-Tiny_L-2_H-128_A-2",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
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+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 128,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3"
+  },
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+  "intermediate_size": 512,
+  "label2id": {
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+    "LABEL_2": 2,
+    "LABEL_3": 3
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 2,
+  "num_hidden_layers": 2,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

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+  "unk_token": "[UNK]"
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models/geo-task-classifier/config.json

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+{
+  "_attn_implementation_autoset": true,
+  "_name_or_path": "mhassanch/bert_tiny_trained",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 128,
+  "id2label": {
+    "0": "object-detection:geobase/WALDO30_yolov8m_640x640",
+    "1": "zero-shot-object-detection:onnx-community/grounding-dino-tiny-ONNX",
+    "2": "zero-shot-object-detection:Xenova/owlvit-base-patch32",
+    "3": "LABEL_3"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 512,
+  "label2id": {
+    "object-detection:geobase/WALDO30_yolov8m_640x640": 0,
+    "zero-shot-object-detection:onnx-community/grounding-dino-tiny-ONNX": 1,
+    "zero-shot-object-detection:Xenova/owlvit-base-patch32": 2,
+    "mask-generation:Xenova/slimsam-77-uniform": 3
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 2,
+  "num_hidden_layers": 2,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "transformers_version": "4.48.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}