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Sentinel-1 based TU Wien flood mapping workflow written using the openEO API and prepared for local processing.

Home Page: https://intertwin-eu.github.io/openeo-flood-mapper-local/

License: GNU General Public License v3.0

Makefile 2.33% Jupyter Notebook 74.37% TeX 8.73% Python 12.47% CSS 0.14% nesC 1.96%

openeo-flood-mapper-local's Introduction

openeo-flood-mapper

Description

This project represents a workflow retrieving flooded surfaces from intensity (backscatter) data of Sentinel-1. The workflow is developed as part of the interTwin project by the Department of Geodesy and Geoinformation at the Technische Universität Wien, as part of an interdisiplinary Digital Twin Engine (DTE). This workflow is based on the openEO syntax.

The current workflow uses openEO locally and remotely and provides Python scripts to prepare and view the data for this purpose. Additionally, the workflow is represented and described in the dedicated notebooks and the accompanying website: https://intertwin-eu.github.io/openeo-flood-mapper-local/.

openeo-flood-mapper-local's People

Contributors

martinschobben avatar fl0roth avatar swamydev avatar

Watchers

Andrea Manzi avatar Matthias Schramm avatar  avatar

Forkers

martinschobben

openeo-flood-mapper-local's Issues

netCDF structure - remove band dim

There's an issue with the converted netCDFs:

plia_id = Path(f"{ROOT_DATA}openEO_local/s1_parameters/S1_CSAR_IWGRDH/PLIA-TAG/V01R03/EQUI7_EU020M/E054N006T3/PLIA-TAG-MEAN_20200101T000000_20201231T235959__D080_E054N006T3_EU020M_V01R03_S1IWGRDH.nc")
sig0_id = Path(f"{ROOT_DATA}openEO_local/s1_parameters/S1_CSAR_IWGRDH/SIG0/V1M1R1/EQUI7_EU020M/E054N006T3/SIG0_20180228T043908__VV_D080_E054N006T3_EU020M_V1M1R1_S1AIWGRDH_TUWIEN.nc")

Running

sig0_dc_xr = sig0_dc.execute()
sig0_dc_xr

Same problem for the plia file.

You will notice that there is a band dimension which shouldn't be there, since it's a Dataset and the data is under variables.
immagine

I guess that the fix would be to add a .drop("band") or add "band" inside squeeze:

da = rioxarray.open_rasterio(file, mask_and_scale=True, chunks=500).squeeze()

Try to use the `fit_curve` and `predict_curve` processes

In openEO we defined and implemented two processes to work with harmonic models fitting and predicting, which are:

fit_curve:

predict_curve:

Currently in the code there is a prediction step for a single date (when it's already known there's a flood). I guess it would be interesting to predict values for a longer time series and then work with the result, implementing the flood detection/alarm part.

Reproducibility and future plans

@ms801221 @fl0roth @MartinSchobben @SwamyDev
I was wondering what's the plan in terms of usage of this code.

From what I understand:

  • Currently it's using pre-defined netCDFs with Sentinel-1 data (backscatter and local incidence angle) and a netCDF containing pre-computed parameters of the fitted function to the current area.
  • It demonstrates how to use openEO to build and run the flood detection workflow.
  • The plan for moving forward is to use the Sentinel-1 Sigma0 data and openEO back-end provided by EODC (instead of the local data and processing).

Some open points which are not documented yet are:

  • How to compute the fitted parameters? Is it planned to provide the code for it?
  • What are the specifications of the input data? (Resolution, other important parameters used for the pre-processing?) Please specify them if possible.

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