The aim of this project is to automate the planning process of solar systems.
Recognition of small obstacles, different input quality, estimating (3-dimensional) roof dimensions, handling of special roof shapes, separation of different roof sides, interfaces.
The entire process was automated up to the visualization of the solar panels.
Develop a Computer Vision tool to automatically segment ASM sites in satellite images.
Gather a sufficient set of training data.
We train a Deep Neural Network with a U-Net architecture. ASMSpotter can be developed into a cloud service, which analyzes satellite images upon request.
Reliably detect Cumulonimbus and Towering Cumulus clouds using AI.
The input data is in an unusual format that requires extensive preprocessing using domain specific knowledge before Machine Learning can be applied.
A neural network was trained using the satellite data as the input. We were able to obtain a 98% accuracy.
Develop an intelligent tool that supports lawyers in analyzing contracts using AI.
Input: photograph of the rental agreement as PDF file
Output: explained and textually substantiated assessment of the effectiveness of the cosmetic repair clause.
We have developed a software which fully automates all steps from the evaluation of the quality of the document image to the decision on the admissibility of contractual regulations.