Update prez

otbtf_pres/index.html

@@ -93,7 +93,7 @@ docker pull mdl4eo/otbtf:3.3.0-gpu  # GPU enabled
         </section>
 
         <section>
-          <h4>TensorFlow graphs</h4>
+          <h2>TensorFlow graphs</h2>
           <img width="17.5%" data-src="illustrations/computational_graph.gif">
           <p><small>Source: the TensorFlow website</small></p>
         </section>
@@ -126,7 +126,14 @@ otbcli_TensorflowModelServe                                     \
   -model.dir "/tmp/my_savedmodel" -model.fullyconv on           \
   -out "output.tif"
           </code></pre>
-              
+        </section>
+
+        <section>
+          <h2>Deep learning in Earth Observation</h2>
+          <h3><strike>Bridging the gap between DL and EO</strike></h3>
+          <h3>Bridging the gap between litterature and real life </h3>
+          <img width="40%" data-src="illustrations/listof.jpg">
+          <p><small>Made with imgflip.com</small></p>
         </section>
 
       </section>
@@ -263,7 +270,7 @@ tf_ds = TFRecords("/path/to/tfrecords_dir").read()
           <h4><y>New</y> in v3.3.0!</h4>
           <br>
           <ul>
-            <li>Ease the implementation of deep nets</li>
+            <li>Ease the <h>implementation of deep nets</h> in python</li>
             <li>Provides all the necessary to work smoothly with TensorflowModelServe</li>
           </ul>
           <br>
@@ -271,7 +278,7 @@ tf_ds = TFRecords("/path/to/tfrecords_dir").read()
         </section>
 
         <section data-transition="fade">
-          <h4>Model implementation example</h4>
+          <h4>Model implementation</h4>
           <img width="48px" data-src="illustrations/python.png" style="float:left;padding-left:18%;margin:30px">
           <pre style="width:1000px"><code data-trim class="python">
 from otbtf import ModelBase
@@ -294,7 +301,7 @@ class MyModel(ModelBase):
         </section>
 
         <section data-transition="fade">
-          <h4>Dataset preparation example</h4>
+          <h4>Dataset preparation</h4>
           <img width="48px" data-src="illustrations/python.png" style="float:left;padding-left:18%;margin:30px">
           <pre style="width:1000px"><code data-trim class="python">
     def dataset_preprocessing_fn(examples):
@@ -372,8 +379,8 @@ app.write("output_y.tif")
           <img width="50%" data-src="illustrations/fig12.10_new.png">
           <br>
           <p><h>Example</h>: simple U-Net like model for dense pixel classification</p>
+          <p><small>Cresson, R. (2020). Deep Learning for Remote Sensing Images with Open Source Software. CRC Press.</small></p>
         </section>
-        
 
       </section>
 
@@ -388,24 +395,11 @@ app.write("output_y.tif")
         </section>
 
         <section>
-          <h3>Rocks mapping</h3>
-          <img width="40%" data-src="illustrations/telescop_cailloux.jpg">
-          <p><small>Source:
-              https://www.soslrc.com/2020/05/15/82-scientifiques-franc-comtois-signent-contre-le-casse-cailloux</small>
-          </p>
-        </section>
-
-        <section>
-          <img width="80%" data-src="illustrations/telescop_cap1.jpg">
-          <p><small>Copyright <h>LaTeleScop</h> and <h>Damien MARAGE</h> (DREAL Bourgogne-Franche-Comté)</small></p>
-        </section>
-
-        <section>
-          <h3>Large scale land cover mapping</h3>
+          <h2>Large scale land cover mapping</h2>
           <h4>Semantic segmentation of buildings footprint over france mainland at 1.5m spacing</h4>
           <img width="65%" data-src="illustrations/tosca.png">
-          <p><a href="https://www.theia-land.fr/en/product/buildings-footprint"
-                target="_blank">https://www.theia-land.fr/en/product/buildings-footprint/</a></p>
+          <p><small>Product available at <a href="https://www.theia-land.fr/en/product/buildings-footprint"
+                target="_blank">https://www.theia-land.fr/en/product/buildings-footprint/</a></small></p>
         </section>
 
         <section data-background-image='illustrations/gif_2160.gif'></section>
@@ -416,13 +410,12 @@ app.write("output_y.tif")
         </section>
 
         <section>
-          <h3>Super-resolution</h3>
+          <h2>Super-resolution</h2>
           <h>https://github.com/remicres/sr4rs</h>
           <br>
           <img width="40%" data-src="illustrations/sr4rs_logos.jpg">
         </section>
 
-
         <section data-background-image='illustrations/sr4rs_tours.jpg'></section>
         <section data-background-image='illustrations/sr4rs_ams.jpg'></section>
         <section data-background-image='illustrations/sr4rs_lisboa.jpg'></section>
@@ -500,22 +493,11 @@ if __name__ == "__main__":
           <img width="20%" data-src="illustrations/pyotbadvert.png">
         </section>
 
-        
-
-        <section>
-          <h3>Soil moisture mapping (Theia product)</h3>
-          <h4>Approx. 20 Theia products/month. More than 6k available products around the globe.</h4>
-          <p>SAR backscattering model inversion using ANN from S1 and S2</p>
-          <h>https://gitlab.irstea.fr/loic.lozach/AgriSoilMoisture</h>
-          <br>
-          <img width="40%" data-src="illustrations/soil_moisture.jpg">
-        </section>
-
         <section>
-          <h3>Cloud removal in optical images</h3>
+          <h2>Cloud removal in optical images</h2>
           <h>https://github.com/cnes/decloud</h>
           <br>
-          <img width="40%" data-src="illustrations/tetiscneslogo.png">
+          <img width="20%" data-src="illustrations/tetiscneslogo.png">
         </section>
 
         <section>
@@ -526,12 +508,12 @@ if __name__ == "__main__":
 
         <section>
           <img width="80%" data-src="illustrations/decloud_cap2.jpg">
-          <p><small>Left: <h>original</h> Sentinel-2 image. Right: <h>reconstructed</h></p></small>
+          <p><small>Left: <h>original</h> Sentinel-2 image. Right: <h>reconstructed</h>. Copyright <h>INRAE</h>, <h>CESBIO</h>, <h>CNES</h> 2021</p></small>
         </section>
 
         <section>
           <img width="80%" data-src="illustrations/decloud_cap1.jpg">
-          <p><small>Left: <h>original</h> Sentinel-2 image. Right: <h>reconstructed</h>
+          <p><small>Left: <h>original</h> Sentinel-2 image. Right: <h>reconstructed</h>. Copyright <h>INRAE</h>, <h>CESBIO</h>, <h>CNES</h> 2021
           </p></small>
         </section>
 
@@ -542,12 +524,15 @@ if __name__ == "__main__":
 
         </section>
 
-        <section>
-          <img width="65%" data-src="illustrations/decloud_anim.gif">
-          <p><small>
-              <h>4 years</h> of reconstructed optical time series (Occitanie area)</p></small>
+        <section data-transition="fade" data-transition-speed="slow">
+        <p>
+          <h>4 years</h> of reconstructed optical time series (Occitanie area). 
+          Copyright <h>INRAE</h>, <h>CESBIO</h>, <h>CNES</h> 2021
+        </p>
         </section>
 
+        <section data-background-image='illustrations/decloud_anim.gif'></section>
+
         <section>
           <h4>Read more</h4>
           <br>
@@ -559,6 +544,29 @@ if __name__ == "__main__":
           <img width="50%" data-src="illustrations/crga_os2_unet_slide3.png">
           <p><small>Cresson, R., Narçon, N., Gaetano, R., Dupuis, A., Tanguy, Y., May, S., and Commandré, B.: COMPARISON OF CONVOLUTIONAL NEURAL NETWORKS FOR CLOUDY OPTICAL IMAGES RECONSTRUCTION FROM SINGLE OR MULTITEMPORAL JOINT SAR AND OPTICAL IMAGES, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 1317–1326</small></p>
         </section>
+
+        <section>
+          <h2>Rocks mapping from VHRS</h2>
+          <img width="40%" data-src="illustrations/telescop_cailloux.jpg">
+          <p><small>Source:
+              https://www.soslrc.com/2020/05/15/82-scientifiques-franc-comtois-signent-contre-le-casse-cailloux</small>
+          </p>
+        </section>
+
+        <section>
+          <img width="80%" data-src="illustrations/telescop_cap1.jpg">
+          <p><small>Copyright <h>LaTeleScop</h> and <h>Damien MARAGE</h> (DREAL Bourgogne-Franche-Comté)</small></p>
+        </section>
+
+        <section>
+          <h2>Soil moisture mapping from SAR</h2>
+          <h4>Approx. 20 Theia products/month. More than 6k available products around the globe.</h4>
+          <p>SAR backscattering model inversion using ANN from S1 and S2</p>
+          <h>https://gitlab.irstea.fr/loic.lozach/AgriSoilMoisture</h>
+          <br>
+          <img width="60%" data-src="illustrations/soil_moisture.jpg">
+        </section>
+
       </section>
 
 
@@ -568,14 +576,14 @@ if __name__ == "__main__":
         </section>
 
         <section>
-          <h3>What you can do with OTBTF</h3>
+          <h2>What you can do with OTBTF</h2>
           <ul>
             <li>Use the OTB applications to <h>create datasets</h> from vector/raster</li>
             <li><h>Build models</h> in python</li>
             <li><h>Train models </h>
               <ul>
                 <li>Beginners: from CLI from patches images</li>
-                <li>Developers: from python, using <i>otbtf.dataset</i> and otbtf.TFRecords classes</li>
+                <li>Developers: from python, using dataset classes</li>
                 <li>Distributed training with Tensorflow 2</li>
               </ul>
             <li><h>Run models</h> in OTB pipelines</li>
@@ -583,7 +591,7 @@ if __name__ == "__main__":
         </section>
 
         <section>
-          <h3>Future work</h3>
+          <h2>Future work</h2>
           <ul>
             <li>Improve <h>python API</h></li>
             <li>More <h>examples and applications</h> out of the box</li>
@@ -591,7 +599,11 @@ if __name__ == "__main__":
             <li>Fix OTB x TensorFlow compilation limitations</li>
             <li>Integration in <h>pyotb</h></li>
           </ul>
+        </section>
 
+        <section>
+          <h2>Thank you for your attention</h2>
+          <h3>Questions? Thoughts?</h3>
         </section>
         
       </section>