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
The EAGLE course "Remote Sensing in Biodiversity and Conservation Science" took place in the last week of the summer term at the field research station in Fabrik Schleichach, Steigerwald. 20 biology and EAGLE students worked and lived together for one week and...read more
The course on UAV application for Remote Sensing started successfully. The weather was good enough to do some first flights. In the next weeks and months more flights will be undertaken and data collected for different fields sites in order to gain more information...read more
one of our field sites in the Steigerwald For several upcoming EAGLE courses we visited potential field sites and tested our equipment. During this first field work of the year our UAV and D-GPS data collection were tested in the Steigerwald at the research station of...read more
On Monday 17th of October we welcomed our new EAGLE students. The EAGLEs in 2016 are from Afghanistan, Bangladesh, Columbia, Egypt, India, Iran, Pakistan, Sweden and Germany. After the official welcome by all lecturer and the study program coordinator Christopher Conrad and the head of the remote sensing department and director of the DLR-DFD, Stefan Dech,read more
The course on remote sensing for biodiversity analysis covered 10 days of field work, R coding, testing field methods such as UAVs and lots of hiking in the National Park Bavarian Forest. This year we had sunny and rainy weather and on the peaks also snow which made...read more
Analyzing species-environment interaction is feasible using various data and method. An increasing technology is the tracking of animals and especially its linkage to remote sensing, as covered in AniMove.org. However, with this technology new challenges have to be...read more
This study will examine whether the application of hybrid classifiers increases the classification accuracy in comparison to a single classifier. A combination between parametric and non-parametric classifiers will be applied and their performance will be assessed....read more