Welcome to fastgs

Introduction

This library is currently in alpha, neither the functionality nor the API is stable

This library provides geospatial multi-spectral image support for fastai. FastAI already has extensive support for RGB images in the pipeline. I try to achieve feature parity for multi-spectral images with this library, specifically in the context of Sentinel 2 geospatial imaging.

Sample Notebooks

You can find demo usage of the library at

1. On the kaggle 38-cloud/95-cloud landsat dataset
2. With a private Sentinel 2 dataset

These are boths works in progress and purposely designed to display the features of the library.

Install

pip install -Uqq fastgs

conda install -c restlessronin fastgs

How to use

The low-level functionality is wrapped into a class that loads sets of Sentinel 2 channels into a multi-spectral tensor (a TensorImageMS subclass of fastai TensorImage which itself is a subclass of the pytorch Tensor).

from fastgs.multispectral import *

The following code creates a class that can load 11 Sentinel 2 channels into a TensorImageMS.

from fastgs.test.io import * # defines read_multichan_files_as_tensor

sentinel2 = createSentinel2Descriptor()

snt_12 = MSData.from_all(
    sentinel2,
    ["B02","B03","B04","B05","B06","B07","B08","B8A","B11","B12","AOT"],
    [sentinel2.rgb_combo["natural_color"], ["B07","B06","B05"],["B12","B11","B8A"],["B08"]],
    get_channel_filenames,
    read_multichan_files
)

The second parameter is a list of 4 channel sets that are minimally required to visualize all the individual channels.

img_12 = snt_12.load_image(66)
img_12.show()

[<AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>]

Acknowledgements

This library is inspired by the following notebooks (and related works by the authors)

• Mauricio Cordeiro’s multi-spectral segmentation fastai pipeline
• Nick Wright’s multi-spectral classification notebook