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20 | 20 | "### [Guides](../)\n",
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21 | 21 | "* [Install and Setup](../guide/install-and-set-up/)\n",
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22 | 22 | " * Adds [`Install in Google Collab`](../guide/install-and-set-up/#install-in-google-colaboratory) section\n",
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23 |
| - "* [Deep Learning with ArcGIS])(../guide/)\n", |
24 |
| - " * [2d Computer Vision](../guide)\n", |
25 |
| - " * [Pixel Classification](../guide)\n", |
| 23 | + "* [Deep Learning with ArcGIS](../guide/)\n", |
| 24 | + " * [2d Computer Vision]\n", |
| 25 | + " * [Pixel Classification]\n", |
26 | 26 | " * [How PSETAE works](../guide/how-PSETAE-works)\n",
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27 | 27 | " \n",
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28 | 28 | "### [Samples](/python/samples/)\n",
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29 | 29 | "* [GIS analysts and data sciencists](/python/samples)\n",
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30 | 30 | " * [Tonga Volcano Eruption - SO2 Analysis](/python/samples/tonga-volcano-eruption-2022)\n",
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31 | 31 | "* [Advanced Machine Learning](/python/samples)\n",
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32 |
| - " * [2d Computer Vision](/python/samples)\n", |
33 |
| - " * [Object Detection](/python/samples)\n", |
| 32 | + " * [2d Computer Vision]\n", |
| 33 | + " * [Object Detection]\n", |
34 | 34 | " * [Detecting Palm Trees using Deep Learning](/python/samples/detecting-palm-trees-using-deep-learning)\n",
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35 | 35 | " * [Detecting Swimming Pools using Automated Deep Learning](/python/samples/detecting-swimming-pools-using-automated-machine-learning)\n",
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36 |
| - " * [Feature Extraction](/python/samples)\n", |
| 36 | + " * [Feature Extraction]\n", |
37 | 37 | " * [Streams extraction using deep learning](/python/samples/streams-extraction-using-deeplearning)\n",
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38 | 38 | " * [Extracting features and Land Use Land Cover using Panoptic Segmentation ](/python/samples/extracting0-features-and-land-use-land-cover-using-panoptic-segmentation)\n",
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39 | 39 | " * [Coastline extraction using Landsat-8 multispectral imagery and band ratio technique](/python/samples/coastline-extraction-usa-landsat8-multispectral-imagery)\n",
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40 |
| - " * [Object Classification](/python/samples)\n", |
| 40 | + " * [Object Classification]\n", |
41 | 41 | " * [Traffic Light Detection in Oriented Imagery Using ArcGIS Pretrained Model](/python/samples/traffic-light-detection-on-oriented-imagery)\n",
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42 |
| - " * [Generative Models](/python/samples)\n", |
| 42 | + " * [Generative Models]\n", |
43 | 43 | " * [Generating Land Surface Temperature from multispectral imagery using Pix2Pix](/python/samples/generating-lst-from-multispectral-imagery-using-pix2pix)\n",
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44 | 44 | " * [Generating cloud masks from satellite imagery - Part I ](/python/samples/cloud-detector-part1-cloudless-sentinel-&-unsupervised)\n",
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45 |
| - " * [Other Imagery Use Cases](/python/samples)\n", |
| 45 | + " * [Other Imagery Use Cases]\n", |
46 | 46 | " * [Flood inundation mapping and monitoring using SAR data and deep learning](/python/samples/flood-inundation-mapping-using-sar-data-and-deep-learning)\n",
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47 | 47 | " * [Cloud removal using pre-trained deep learning model and raster function](/python/samples/cloud-removal-using-pre-trained-deep-learning-model-and-raster-function)\n",
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48 | 48 | " * [Property Valuation in King County USA using AutoML and Geoenriched data](/python/samples/house-price-prediction-using-automl)\n",
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