feat(post): complement model descriptions

_posts/2023-02-28-our-models.md

@@ -15,6 +15,10 @@ We have packaged our models into a repository that makes it easy
 to use Tesselo's most common models. You can find the model references
 in our [Alquimodelia](https://github.com/tesselo/alquimodelia) repository.
 
+The aim of Alquimodelia was to create an user friendly and easy way to use and 
+change parameters on the common model architectures used in Tesselo. No need of any
+knowledge in keras or tensorflow, just some parameters and you had your model ready to use.
+
 Depending on the context and the goal of the modeling, we have used a series of
 different models. They range from pixel based classifiers to time-series based
 U-Net type architectures.
@@ -29,6 +33,10 @@ In our pre-processing pipeline we simply resampled all bands into the target res
 Usually this meant to upsample the lower resolution bands to the resolution of the
 band with the highest resolution. That is 10m for Sentinel-2 images for instance.
 
+Or using the same approuch we would create super-resolution, by upsampling our imagery data
+to the resolution of the target data. We had successful models that would build 1m resolution
+images out of 10m resolution data.
+
 ## Classifiers
 
 Here we are giving a quick overview of the different model types and their use cases.
@@ -44,6 +52,22 @@ are detecting patterns in time series at different levels.
 
 2D U-Net or ResNet based.
 
+#### ResNet
+
+The ResNet architecture uses two-dimensional convolutions to provide a classification to
+a given image. This has been used as a way to classify images with a single class. Or it
+could be used to classify a single pixel, but with the context of the surroundings.
+
+#### 2D U-Net
+
+The 2D U-Net is similiar to ResNet in terms of the usage of two-dimensional convolutions, but
+instead of giving one answer for each image, it responds with also an image.
+Used in image classification and segmentation.
+
 ### Time series of images
 
-3D U-Net based.
+#### 3D U-Net
+
+The 3D U-Net architeture follows the same patterns and the 2D, but instead of two-dimensional convolutions it uses three-dimensional convolutions, multiple images across time.
+The answer would still be a single image, but produced with time context.
+Great to surpass problems like clouds and other imagery artifacts.