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(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>