Data Scientist for Machine Learning and Remote Sensing
Using Tensorflow for Infrared UAV-based Wildlife Detection
We developed a prototype to detect and classify animal signatures in real-time using deep learning frameworks in combination with UAV-based infrared remote sensing data. Being robust and highly performant, the approach exhibits an enormous potential for applicability in ecology and forestry management use case scenarios such as population assessment, mowing operation fawn recovery and wildlife damage prevention.
Machine Learning for Land Use / Land Cover Statistics of Switzerland
This project demonstrates a powerful prototype to classify land use / land cover statistics for an entire country using a Deep Learning approach for aerial imagery processing and a Random Forest architecture for data fusion with time series and other auxilliary datasets.
Deep Learning using Airborne Imagery
An introduction to Deep Learning for Airborne Imagery.