feat(post): add alquimodelia post

Author: yellowcap

_config.yml

@@ -29,6 +29,13 @@ authors:
     web: https://github.com/photonbit
     twitter: https://github.com/photonbit
     description: "Head of Programming and Spiritual Jouney at Tesselo. Has helped devloping the platform and increased team happiness by professionalizing our organizational structure using Kanban and beyond."
+  joao:
+    name: João Santos
+    display_name: João
+    avatar: 'assets/images/joao.jpeg'
+    web: https://github.com/JotaFan
+    twitter: https://github.com/JotaFan
+    description: "Versatile programmer, deep learning modeling wizzard, and geospatial analyist. Has contributed to the pixels platform and executed many projects for Tesselo."
 
 
 # Plugins

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

@@ -0,0 +1,49 @@
+---
+layout: post
+title:  "Model Alquemy"
+author: joao
+categories: [ AI, code ]
+image: assets/images/our-models.png
+description: "An introduction to Tesselo's AI modeling, explaining the model types we used for our mapping with EO data."
+featured: false
+hidden: false
+---
+Tesselo's deep learning models are presented in this post. We have used them to
+do large scale land cover modeling across the world.
+
+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.
+
+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.
+
+## Use all bands
+
+For our modeling, we moslty used all available bands of the multispectral satellite
+images. For Sentinel-2 we used the 10 bands that have 10m or 20m resolution. Similarly,
+for Landsat we used the available bands.
+
+In our pre-processing pipeline we simply resampled all bands into the target resolution.
+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.
+
+## Classifiers
+
+Here we are giving a quick overview of the different model types and their use cases.
+Detailed posts about some of the models will follow separately as well.
+
+### Pixel based time series classifier
+
+This classifier is quite small but very powerful for small training datasets. It is
+non-sequential and based on one-dimensional convolution. It has two branches that
+are detecting patterns in time series at different levels.
+
+### Single scene image segmentation
+
+2D U-Net or ResNet based.
+
+### Time series of images
+
+3D U-Net based.