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otbtf keras tutorial

How to work with OTBTF and Keras / Tensorflow 2?

This tutorial is a revamp of the "Semantic Segmentation" part of this book, using Keras. Everything is coded in python.

Materials are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

CC BY-SA 4.0

Introduction

This is a very simple tutorial showing how to create training/validation/test datasets from geospatial images, training a model, evaluating the model, and applying it to map buildings from a Spot-7 product. A slight variation from the original book is that the proposed model shows how to consume multiple sources of different resolution and caracteristics natively: the XS and Pan images of the Spot-7 product are directly fed in the model, without any pre-processing.

Get started

Docker image

Pull the latest otbtf image on dockerhub.

Note At least version 4.2.0 must be used

Here is an example with version 4.2.0, CPU flavored:

docker pull mdl4eo/otbtf:4.2.0-cpu

Then start a container and follow the tutorial inside it. Here we mount some local volume in /data as storage for the data.

docker run -ti -v /some/local/pth:/data mdl4eo/otbtf:4.2.0-cpu

pyotb

Install pyotb >= 2.0.0 inside the container.

pip install pyotb

Data

We use the same data as in the "Semantic Segmentation" part of this book.

  • One Spot-7 product, that can be freely downloaded from the Airbus Defense and Space website
  • One label image of buildings, with the same extent and spatial resolution as the panchromatic channel of the Spot-7 product, that can be downloaded here

For all steps, we suggest to use the following directories:

  • Put amsterdam_labelimage.tif in the /data/terrain_truth directory,
  • Decompress the Spot-7 product archive into /data/spot,
  • We will use /data/output to generate all output files.

Step 1: sampling

In this section, we extract patches into 3 images:

  • the panchromatic channel of the Spot-7 product, 64x64 pixels, monoband, uint8
  • the multispectral image of the Spot-7 product: 16x16 pixels, multiband, uint8
  • the label image of buildings: 64x64 pixels, monoband, uint8

Please note that Spot-7 images are encoded in uint8 because they are free products but in the real world they are in uint16. We extract the 3 sources simultaneously using the PatchesExtraction application. The location of the patches centroids is trivialy done using the PatchesSelection application. Note that in practice you probably want to go with your own approach, however this simple case is good enough for our tutorial!

Run the following:

python sampling.py

After the execution, the following new files should have been created:

  • For the training dataset:
    • train_p_patches.tif
    • train_xs_patches.tif
    • train_labels_patches.tif
  • For the validation dataset:
    • valid_p_patches.tif
    • valid_xs_patches.tif
    • valid_labels_patches.tif
  • For the testing dataset:
    • test_p_patches.tif
    • test_xs_patches.tif
    • test_labels_patches.tif

If you want to deploy training at production scale, you can use otbtf.TFRecords to convert the otbtf.DatasetFromPatchesImage into TFRecords then to feed the training process. However that's not covered in this tutorial (more info in the otbtf documentation).

Step 2: training

We train a very lightweight U-Net from the previously extracted patches. The training is done using Keras in python. Instances of otbtf.DatasetFromPatchesImage are used to generate tensorflow datasets to train, valid, and test our network.

Run the following:

python training.py --model_dir /data/output/savedmodel

In the end the best network (wrt. validation dataset) is exported as a SavedModel in /data/output/savedmodel. The model is evaluated against the test dataset and precision, recall metrics are displayed.

Step 3: inference

Run the following to generate the nice buildings map:

python inference.py