Analysis-Ready-Data: Comparison of different pre-processing routines in the big Earth data context

Suggested by: Dirk Tiede, Martin Sudmanns, Hannah Augustin

Background

Due to the massive volume, variety, and velocity of the Earth observation (EO) data offered by the Copernicus programme, the term Analysis-Ready-Data (ARD) is emerging when talking about big Earth data. This term refers to data that has been pre-processed to a certain degree such that they can be used directly as input to analyses without further pre-processing. It aims to shift the burden of routine, basic pre-processing from users to Earth observation data providers so that users can focus on their domain-specific applications rather than issues of calibration, topographic correction, etc. However, there is still no commonly agreed definition, understanding or standard of what ARD is or can/will be. Several stakeholders are developing their own ARD products (e.g. Planet, UGSS with Landsat). At Z_GIS we aim to push requirements of ARD further to include not only the provision of data, but providing it together with automatically generated, generic information layers in the context of the Sen2Cube.at project (http://sen2cube.at).

General workflow for generating information products from observations. ARD are concerned with the four first steps (data acquisition, conversion to radiance, TOA reflectance and Surface reflectance) allowing then to analyse data and generate time-series (from Giuliani et al. 2017)

Short description

The tasks include:

• conducting a state-of-the-art assessment of existing ARD
• providing a comparison metric for different definitions
• selecting suitable approaches for ARD in the context of the Z_GIS project Sen2Cube.at
• reviewing, selecting or developing appropriate quality indicators for data and workflows
• (optional) implementing ARD generation in an automated workflow 

Suggested reading

https://medium.com/planet-stories/analysis-ready-data-defined-5694f6f48815

Giuliani, G., Chatenoux, B., De Bono, A., Rodila, D., Richard, J.P., Allenbach, K., Dao, H. and Peduzzi, P., 2017. Building an earth observations data cube: Lessons learned from the Swiss data cube (SDC) on generating analysis ready data (ARD). Big Earth Data, 1(1-2), pp.100-117.

Tiede, D., Baraldi, A., Sudmanns, M., Belgiu, M. and Lang, S., 2017. Architecture and prototypical implementation of a semantic querying system for big Earth observation image bases. European journal of remote sensing, 50(1), pp.452-463.

Start: Anytime

Prerequisites/qualification

Remote Sensing ++

programming + (optional)