Spatial Python for Remote Sensing
In this course we will elaborate large and complex workflows dealing with the analysis and handling of big data using Python. Moreover, we will try to develop a fully automated processing chain in Python for landcover mapping starting with the download of Landsat data, preprocessing, classification and building up a spatial database that enables GIS functionality over large datasets for further analysis.
We will start with the basics of Python programming language and quickly evolve towards image processing techniques with packages such as scipy, numpy, scikit-learn, scikit-image and gdal. We will also focus on arcpy for ESRI software products which allows for a convenient and powerful automatization of spatial analysis functions.
General Course News and Updates
Our EAGLEs in 2018 visited the German Aerospace Center, namely the Earth Observation Center, close to Munich. Various topics were presented by DLR scientist and the EAGLEs hat the chance to discuss various topics in small groups with individual scientists.read more
on Thursday, December 13th, at 12:30 we will have the following presentations in the student working room (Josef Martin Weg 52, 3rd floor): internship presentations: Johni Miah"Remote Sensing and Geographic Information System for Decision...read more
On Monday, 24th of September, at 1pm the following internship reports will be presented: Bharath: "Installation and Characterization of an imaging Spectrometer for the UAV-based remote sensing" Johannes: "Crop classification based on S1/S2 in...read more
The following students presented their innovation labs, internships and ideas for MSc. thesis: Ahmed: Innovation Lab at DLR (team of Ursula Gessner) and Master Thesis Idea: Title: Status of Agricultural Lands in Egypt using Earth Observation Maninder (at DLR,...read more
Today some of our EAGLE students presented their internship and innovation laboratory projects. Very interesting topics and they obviously applied and deepened their remote sensing knowledge a lot. Julia Sauerbrey: Prediction of Organic Matter Content from Sentinel-2...read more
The course "from field work to spatial data" by Tobias Ullmann and Martin Wegmann is covering the whole range of field campaign planning and especially training all necessary methods such as GPS handling, coordinate systems, setting waypoints or finding locations. In...read more
Today our EAGLE students applied data munging, pipes, plotting and statistics using colour distribution of sweets. They specifically used the dplyr, ggplot, kableExtra and others to compute derivatives, rearrange the data, plot it and run statistics on colour...read more
The final project presentations of the spatial coding course by the EAGLE students revealed quite some impressive analysis achieved within the last couple of months. All analysis were done using R and presentations created within R using knitr. The aim was to run a...read more
The following internship and innovation laboratory projects were presented today: Karsten Wiertz did his internship at the Białowieza national park on "Spatio-temporal analysis of tree mortality and gaps in the Białowieza Forest using high resolution imagery". Jakob...read more
Last week Steven Hill and Thorsten Dahms gave a course that introduced EAGLE students to Python-based spatial data analysis. The advantages and challenges of different python libraries, data sets and methods were covered in hands-on exercises and also discussed...read more