In the educational series RTV SLO Let's Bite Science, we presented aspects of the use of satellite data, prepared graphic materials for the series, and participated in the implementation of the live show. In this way, we make sure that the technological field of Earth observation is presented to the public in a popular way and support the educational broadcast. The show has been on the program for 10 years, and the show's editor-in-chief received the first prize in the TV / video category for her determined and imaginative performance at an international conference of science reporters. The awards are given by the Association of British Science Journalists (ABSW), in 2019.
F.17 Transfer of existing technologies, know-how, methods and procedures into practice
COBISS.SI-ID: 44953901In this thesis, we have described satellite data with emphasis on Sentinel-2 satellites. We showed the definition of time series and methods for their collection over the internet. We compared cloud mask algorithms used and developed for sentinel-hub portal, with other commonly used cloud mask algorithms. We gave a short description of Saviztky-Golay, LOESS and Whittaker-Eilers signal smoothing algorithms with NDVI, EVI and EVI2 vegetation indices. In the second part of the thesis, we provide a simplified way for getting and storing generalised raster statistical data in Python programming language and Spatialite database. We compared two time-series smoothing methods concerning input smoothing parameters. Similarly, we analysed time series of three smoothed vegetation indices. In the end, we provided a way for building comparable vectors and demonstrated our program on simple SVM classification model. For this thesis, we have written a program in Python, which is freely available online and simplifies work with Sentinel-2 time series.
D.10 Educational activities
COBISS.SI-ID: 8887649Within the project, we analyse the data in Python. We use many openly available libraries, in addition to standard (NumPy, matplotlib, pandas) also specific to spatial data and analysis of satellite imagery (GDAL, GeoPandas, Rasterio, Fiona, SentinelHub). We perform analysis with our software, which is exchanged within the group via the GitHub private repository. The software code, which is of interest for the wider community, has been published on GitHub in the opensource library satimagets. The library is accessible at https://github.com/EarthObservation/satimagets under the MIT license, which is one of the most open and permits the use of code also in commercial products when including reference to the source. The published version provides basic tools for processing and harmonisation of satellite data. As an example, we added code to harmonise the imagery PlanetScope and Sentinel-2. The library will be extended in the future with a code that is currently in development and is still internal.
F.23 Development of new system-wide, normative and programme solutions, and methods