DOC: adding schemas of models + improving the pretrained_models doc

README.md

 # Decloud
 
-Decloud enables the training of various deep nets to remove clouds in optical images.
+Decloud enables the training and inference of various neural networks to remove clouds in optical images.
 
 Representative illustrations:
 
 ![](doc/images/cap2.jpg)
 ![](doc/images/cap1.jpg)
 
-*Examples of de-clouded images using the single date SAR/Optical U-Net model.*
+*Examples of de-clouded Sentinel-2 images using the single date SAR/Optical U-Net model.*
 
 ## Quickstart: Run a pre-trained model
-Some pre-trained models are available. You can find more info on how to use them [here](doc/pretrained_models.md)
+Some pre-trained models are available at this [url](https://nextcloud.inrae.fr/s/DEy4PgR2igSQKKH). 
+
+The easiest way to run a model is to run the timeseries processor such as: 
+
+<pre><code>python production/meraner_timeseries_processor.py
+<span style="padding:0 0 0 90px;color:blue">--s2_dir</span>  S2_PREPARE/T31TCJ 
+<span style="padding:0 0 0 90px;color:blue">--s1_dir</span>  S1_PREPARE/T31TCJ
+<span style="padding:0 0 0 90px;color:blue">--model</span>   merunet_occitanie_pretrained/
+<span style="padding:0 0 0 90px;color:blue">--dem</span>     DEM_PREPARE/T31TCJ.tif
+<span style="padding:0 0 0 90px;color:blue">--out_dir</span> meraner_timeseries/
+<span style="padding:0 0 0 90px;color:grey">--write_intermediate --overwrite</span>
+<span style="padding:0 0 0 90px;color:grey">--start</span> 2018-01-01 <span style="color:grey">--end</span> 2018-12-31 
+<span style="padding:0 0 0 90px;color:grey">--ulx</span> 306000 <span style="color:grey">--uly</span> 4895000 <span style="color:grey">--lrx</span> 320000 <span style="color:grey">--lry</span> 4888000
+</code></pre>
+*(mandatory arguments in blue, optional arguments in grey)*
+
+You can find more info on available models and how to use these models [here](doc/pretrained_models.md)
+
+
 
 ## Advanced usage: Train you own models
 
-1. Prepare the data: convert Sentinel-1 and Sentinel-2 images in the right format (see the documentation).
+1. Prepare the data: convert Sentinel-1 and Sentinel-2 images in the right format (see the [documentation](doc/user_doc.md#Part-A:-data-preparation)).
 ![](doc/images/step_1.png)
 2. Create some *Acquisition Layouts* (.json files) describing how the images are acquired, ROIs for training and validation sites, and generate some TFRecord files containing the samples.
 ![](doc/images/step_2.png)

doc/pretrained_models.md

-## URL
+# Download
 Models are located here: https://nextcloud.inrae.fr/s/DEy4PgR2igSQKKH
 
-## Models available
+# Models
 
-### CRGA OS2
-TODO: illustration inputs / output
+All models use Sentinel-2 and Sentinel-1 images as inputs. The inputs/output of each model architecture are presented below.
 
-This section covers how to run these pre-trained models:
-- crga_os2_occitanie_pretrained
-- crga_os2david_occitanie_pretrained
-- crga_os2_burkina_pretrained
+## CRGA OS2
+![](images/crga_os2_pretrained_model.png)
 
-### CRGA OS1
-TODO: illustration inputs / output
 
-This section covers how to run these pre-trained models:
-- crga_os1_occitanie_pretrained
-- crga_os1_burkina_pretrained
+## CRGA OS1
+![](images/crga_os1_pretrained_model.png)
 
-### Meraner
-TODO: illustration inputs / output
 
-This section covers how to run these pre-trained models:
-- meraner_occitanie_pretrained
-- meraner_burkina_pretrained
+## Merunet: Meraner U-Net
+![](images/merunet_pretrained_model.png)
 
-### Monthly synthesis S2/S1
-TODO: illustration inputs / output
 
-This section covers how to run these pre-trained models:
-- monthly_synthesis_s2s1_occitanie_pretrained
-- monthly_synthesis_s2s1_david_occitanie_pretrained
+## Monthly synthesis S2/S1
+![](images/monthly_synthesis_s2s1_pretrained_model.png)
 
-### Monthly synthesis S2
-TODO: illustration inputs / output
+## Monthly synthesis S2
+![](images/monthly_synthesis_s2_pretrained_model.png)
 
-This section covers how to run these pre-trained models:
-- monthly_synthesis_s2_david_occitanie_pretrained
+# Inputs
 
-## How to run a model
+- Sentinel-1 SAR images, pre-processed using the S1Tiling OTB Remote Module
+- Sentinel-2 optical images (L2 level), can be from the THEIA Land Data Center or from ESA scihub
+- DEM: Digital Elevation Model, 20m resolution
 
+# How to run a model
+
+## Time series processor
+This is the highest-level way of running the inference of a model. For example, you can run a CRGA model on a time series like this:
+
+<pre><code>python production/crga_timeseries_processor.py \
+<span style="padding:0 0 0 90px;color:blue">--s2_dir</span>  S2_PREPARE/T31TCJ \
+<span style="padding:0 0 0 90px;color:blue">--s1_dir</span>  S1_PREPARE/T31TCJ \
+<span style="padding:0 0 0 90px;color:blue">--model</span>   crga_os2_occitanie_pretrained/ \
+<span style="padding:0 0 0 90px;color:blue">--dem</span>     DEM_PREPARE/T31TCJ.tif \
+<span style="padding:0 0 0 90px;color:blue">--out_dir</span> reconstructed_timeseries/ \
+<span style="padding:0 0 0 90px;color:grey">--write_intermediate --overwrite</span> \
+<span style="padding:0 0 0 90px;color:grey">--start</span> 2018-01-01 <span style="color:grey">--end</span> 2018-12-31 \
+<span style="padding:0 0 0 90px;color:grey">--ulx</span> 306000 <span style="color:grey">--uly</span> 4895000 <span style="color:grey">--lrx</span> 320000 <span style="color:grey">--lry</span> 4888000
+</code></pre>
+*(mandatory arguments in blue, optional arguments in grey)*
+
+
+## Processor
 For instance, we use `crga_processor.py` to perform the inference of the *crga* models.
 This program not only performs the inference, but also takes care of preparing the right input images to feed the model, and also the post-processing steps (like removing inferred no-data pixels).
-It is built exclusively using OTB application pipelines, and is fully streamable (not limitation or images size).
+It is built exclusively using OTB application pipelines, and is fully streamable (no limitation on images size).
 
 Below is an example of use : 
 
-```yaml
+```bash
 python production/crga_processor.py \
 --il_s1before \
-  /data/decloud/bucket/S1_PREPARE/T31TEJ/s1b_31TEJ_vvvh_DES_139_20201001txxxxxx_from-10to3dB.tif \
-  /data/decloud/bucket/S1_PREPARE/T31TEJ/s1a_31TEJ_vvvh_DES_037_20200930txxxxxx_from-10to3dB.tif \
-  /data/decloud/bucket/S1_PREPARE/T31TEJ/s1b_31TEJ_vvvh_DES_110_20200929t060008_from-10to3dB.tif \
+  /data/s1b_31TEJ_vvvh_DES_139_20201001txxxxxx_from-10to3dB.tif \
+  /data/s1a_31TEJ_vvvh_DES_037_20200930txxxxxx_from-10to3dB.tif \
+  /data/s1b_31TEJ_vvvh_DES_110_20200929t060008_from-10to3dB.tif \
 --il_s1 \
-  /data/decloud/bucket/S1_PREPARE/T31TEJ/s1b_31TEJ_vvvh_DES_139_20201013txxxxxx_from-10to3dB.tif \
-  /data/decloud/bucket/S1_PREPARE/T31TEJ/s1b_31TEJ_vvvh_DES_110_20201011t060008_from-10to3dB.tif \
-  /data/decloud/bucket/S1_PREPARE/T31TEJ/s1a_31TEJ_vvvh_DES_037_20201012txxxxxx_from-10to3dB.tif \
+  /data/s1b_31TEJ_vvvh_DES_139_20201013txxxxxx_from-10to3dB.tif \
+  /data/s1b_31TEJ_vvvh_DES_110_20201011t060008_from-10to3dB.tif \
+  /data/s1a_31TEJ_vvvh_DES_037_20201012txxxxxx_from-10to3dB.tif \
 --il_s1after \
-  /data/decloud/bucket/S1_PREPARE/T31TEJ/s1b_31TEJ_vvvh_DES_139_20201025txxxxxx_from-10to3dB.tif \
-  /data/decloud/bucket/S1_PREPARE/T31TEJ/s1a_31TEJ_vvvh_DES_037_20201024txxxxxx_from-10to3dB.tif \
-  /data/decloud/bucket/S1_PREPARE/T31TEJ/s1b_31TEJ_vvvh_DES_110_20201023t060008_from-10to3dB.tif \
+  /data/s1b_31TEJ_vvvh_DES_139_20201025txxxxxx_from-10to3dB.tif \
+  /data/s1a_31TEJ_vvvh_DES_037_20201024txxxxxx_from-10to3dB.tif \
+  /data/s1b_31TEJ_vvvh_DES_110_20201023t060008_from-10to3dB.tif \
 --il_s2before \
-  /data/decloud/bucket/S2_PREPARE/T31TEJ/SENTINEL2B_20200929-104857-489_L2A_T31TEJ_C_V2-2 \
-  /data/decloud/bucket/S2_PREPARE/T31TEJ/SENTINEL2B_20200926-103901-393_L2A_T31TEJ_C_V2-2 \
+  /data/SENTINEL2B_20200929-104857-489_L2A_T31TEJ_C_V2-2 \
+  /data/T31TEJ/SENTINEL2B_20200926-103901-393_L2A_T31TEJ_C_V2-2 \
 --il_s2after \
-  /data/decloud/bucket/S2_PREPARE/T31TEJ/SENTINEL2B_20201026-103901-924_L2A_T31TEJ_C_V2-2 \
-  /data/decloud/bucket/S2_PREPARE/T31TEJ/SENTINEL2A_20201024-104859-766_L2A_T31TEJ_C_V2-2 \
---in_s2 /data/decloud/bucket/S2_PREPARE/T31TEJ/SENTINEL2B_20201012-105848-497_L2A_T31TEJ_C_V2-2 \
---dem /data/decloud/bucket/DEM_PREPARE/T31TEJ.tif \
---savedmodel /data/decloud/todel/savedmodel_david2/09-04-21_224907_various_enhancements_and_todos_93228_crga_os2_david_bt48_bv48 \
---output /data/decloud/results/theia_data/SENTINEL2B_20201012-105848-497_L2A_T31TEJ_C_V2-2_FRE_10m_reconst_reference.tif
+  /data/SENTINEL2B_20201026-103901-924_L2A_T31TEJ_C_V2-2 \
+  /data/SENTINEL2A_20201024-104859-766_L2A_T31TEJ_C_V2-2 \
+--in_s2 /data/SENTINEL2B_20201012-105848-497_L2A_T31TEJ_C_V2-2 \
+--dem /data/DEM_T31TEJ.tif \
+--savedmodel /path/to/saved/model/ \
+--output SENTINEL2B_20201012-105848-497_L2A_T31TEJ_C_V2-2_FRE_10m_reconstructed.tif
 ```

[Narcon Nicolas]

@nicolas.narcon remove html (isn't rendered by gitlab nor github)