Update documentation

_sources/content/ml4ops/HOWTO_inference_on_image_time_series.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "d2d45cdd",
+   "id": "e85a25a9-ac27-47fb-a5c7-24a339305c15",
    "metadata": {},
    "source": [
     "# Run inference on time series of Sentinel-2 images\n",
@@ -13,10 +13,25 @@
     "\n",
     "This notebook shows how to query time series of images of Sentinel-2 over an area of interest (AoI) between two dates using the Google Earth Engine. Hence to run this notebook you a Google Earth Engine account. \n",
     "\n",
-    "We show how to export those images, run inference on them to produce flood segmentation maps that are vectorized and shown in an interactive map. We will run the clouds-aware flood segmentation model proposed in:\n",
+    "We will run the clouds-aware flood segmentation model proposed in:\n",
     "\n",
     "> E. Portalés-Julià, G. Mateo-García, C. Purcell, and L. Gómez-Chova [Global flood extent segmentation in optical satellite images](https://www.nature.com/articles/s41598-023-47595-7). _Scientific Reports 13, 20316_ (2023). DOI: 10.1038/s41598-023-47595-7.\n",
+    ">\n",
     "> \n",
+    "We show how to export those images, run inference, vectorise the segmentation output and show the results in an interactive map:\n",
+    "\n",
+    "<iframe width=\"600\" height=\"400\"\n",
+    "src=\"https://www.youtube.com/embed/x4hUYHMJAkQ?autoplay=0\">\n",
+    "</iframe>\n",
+    "\n",
+    "*Video with the expected output results*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35e1865e-b1c0-4891-aabe-3100c1a888e5",
+   "metadata": {},
+   "source": [
     "**Note**: If you run this notebook in Google Colab change the running environment to use a GPU."
    ]
   },

content/ml4ops/HOWTO_inference_on_image_time_series.html

@@ -420,10 +420,15 @@ <h1>Run inference on time series of Sentinel-2 images<a class="headerlink" href=
 </ul>
 <hr class="docutils" />
 <p>This notebook shows how to query time series of images of Sentinel-2 over an area of interest (AoI) between two dates using the Google Earth Engine. Hence to run this notebook you a Google Earth Engine account.</p>
-<p>We show how to export those images, run inference on them to produce flood segmentation maps that are vectorized and shown in an interactive map. We will run the clouds-aware flood segmentation model proposed in:</p>
+<p>We will run the clouds-aware flood segmentation model proposed in:</p>
 <blockquote>
 <div><p>E. Portalés-Julià, G. Mateo-García, C. Purcell, and L. Gómez-Chova <a class="reference external" href="https://www.nature.com/articles/s41598-023-47595-7">Global flood extent segmentation in optical satellite images</a>. <em>Scientific Reports 13, 20316</em> (2023). DOI: 10.1038/s41598-023-47595-7.</p>
 </div></blockquote>
+<p>We show how to export those images, run inference, vectorise the segmentation output and show the results in an interactive map:</p>
+<iframe width="600" height="400"
+src="https://www.youtube.com/embed/x4hUYHMJAkQ?autoplay=0">
+</iframe>
+<p><em>Video with the expected output results</em></p>
 <p><strong>Note</strong>: If you run this notebook in Google Colab change the running environment to use a GPU.</p>
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