diff --git a/README.md b/README.md
index f665e02da320bad5b417d582b042a463475482d8..e5a1d523f20e8d2a00f03292e8543261a409b27b 100644
--- a/README.md
+++ b/README.md
@@ -1,20 +1,39 @@
# 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:
-*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:
+
+```
+python production/meraner_timeseries_processor.py
+ --s2_dir S2_PREPARE/T31TCJ
+ --s1_dir S1_PREPARE/T31TCJ
+ --model merunet_occitanie_pretrained/
+ --dem DEM_PREPARE/T31TCJ.tif
+ --out_dir meraner_timeseries/
+# Optional arguments:
+ --write_intermediate --overwrite
+ --start 2018-01-01 --end 2018-12-31
+ --ulx 306000 --uly 4895000 --lrx 320000 --lry 4888000
+```
+
+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)).
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.
diff --git a/decloud/production/monthly_synthesis_processor_s2s1.py b/decloud/production/monthly_synthesis_processor_s2s1.py
index 4243e9ff494302643631ee756a6328c1425b78b8..10adc0aea0faeaa99f090f9b81d2ca1c243da2d2 100644
--- a/decloud/production/monthly_synthesis_processor_s2s1.py
+++ b/decloud/production/monthly_synthesis_processor_s2s1.py
@@ -272,7 +272,7 @@ if __name__ == "__main__":
input_s1_products.sort(key=_closest_date, reverse=True)
input_s1_images_10m = [product.get_raster_10m() for product in input_s1_products]
# creating a mosaic with the N closest S1 images
- s1t = pyotb.App('Mosaic', input_s1_images_10m[:s1_Nimages], nodata=0)
+ s1t = pyotb.App('Mosaic', input_s1_images_10m[-s1_Nimages:], nodata=0)
candidates.append({'s2': s2_product, 's1': s1t})
diff --git a/doc/images/crga_os1_pretrained_model.png b/doc/images/crga_os1_pretrained_model.png
new file mode 100644
index 0000000000000000000000000000000000000000..46e64b732622a1f0b340e9de617180cfb317672a
Binary files /dev/null and b/doc/images/crga_os1_pretrained_model.png differ
diff --git a/doc/images/crga_os2_pretrained_model.png b/doc/images/crga_os2_pretrained_model.png
new file mode 100644
index 0000000000000000000000000000000000000000..b534af65ef100e05898e04e5f70e6b361f5a8d36
Binary files /dev/null and b/doc/images/crga_os2_pretrained_model.png differ
diff --git a/doc/images/merunet_pretrained_model.png b/doc/images/merunet_pretrained_model.png
new file mode 100644
index 0000000000000000000000000000000000000000..e8e96d3ab7ee8dda5c102d4fb6473dc65a0b3fae
Binary files /dev/null and b/doc/images/merunet_pretrained_model.png differ
diff --git a/doc/images/monthly_synthesis_s2_pretrained_model.png b/doc/images/monthly_synthesis_s2_pretrained_model.png
new file mode 100644
index 0000000000000000000000000000000000000000..953a10e38f1ec8a1c3571999ed0babdadcb8b0e2
Binary files /dev/null and b/doc/images/monthly_synthesis_s2_pretrained_model.png differ
diff --git a/doc/images/monthly_synthesis_s2s1_pretrained_model.png b/doc/images/monthly_synthesis_s2s1_pretrained_model.png
new file mode 100644
index 0000000000000000000000000000000000000000..36953d9cbbc712a8ad2d3a63e94a4485ea901138
Binary files /dev/null and b/doc/images/monthly_synthesis_s2s1_pretrained_model.png differ
diff --git a/doc/pretrained_models.md b/doc/pretrained_models.md
index 0535032b54c40e70047b16c1c866479356e64986..33a295773c30a39e5ba140e4c7797ee1adf13fd6 100644
--- a/doc/pretrained_models.md
+++ b/doc/pretrained_models.md
@@ -1,73 +1,81 @@
-## 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
+
-### 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
+
-### 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
+
-### 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
+
-### Monthly synthesis S2
-TODO: illustration inputs / output
+## Monthly synthesis S2
+
-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:
+
+```
+python production/crga_timeseries_processor.py
+ --s2_dir S2_PREPARE/T31TCJ
+ --s1_dir S1_PREPARE/T31TCJ
+ --model crga_os2_occitanie_pretrained/
+ --dem DEM_PREPARE/T31TCJ.tif
+ --out_dir reconstructed_timeseries/
+# Optional arguments:
+ --write_intermediate --overwrite
+ --start 2018-01-01 --end 2018-12-31
+ --ulx 306000 --uly 4895000 --lrx 320000 --lry 4888000
+```
+
+## 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
```