Due to advances in the field of machine learning in recent years, any pattern in image data visible to the human eye can also be made visible to a machine. Sometimes machines can even be trained to detect structures not visible to humans.
Currently the process is cost-intensive and/or a faster decision creates substantial value
A (trained) human could make a good decision mainly based on visual information
There is enough data available (as a rule of thumb: 200 - 2.000 images. This, of course, is highly dependent on the use-case)
In our experience, only by combining knowhow of internal operations with machine vision expertise, projects can be framed well. Feel free to approach us with questions, especially whether we deem your project to be technically feasible.
Together we discuss your process automation projects along three different dimensions: cost savings, strategic value and technical feasibility. After settling for a specific project, we put special emphasis on the needs of the end users.
We are an experienced team of machine learners. Our algorithms find complicated patterns in unstructured, mostly visual and text data. Once detected, these patterns are the basis for the automation of the underlying process.
We make a point of integrating our customers in the project's code repository as well as in weekly progress meetings. Agility, clean code and a modular program structure help us to deliver easy-to-maintain software, that simply works.
Using machine learning, computer vision and object recognition, dida succeeded in developing a software to automatically plan solar based on satellite data.
Machine learning and environmental protection: Development of software for automated detection of illegal small-scale mining using satellite data.
AI-supported optical defect detection for semiconductor laser production
Machine learning in remote sensing: Read here about our project with the DWD and the object detection of convective clouds using deep learning.
Machine learning and remote sensing: The computer vision software developed by dida enables predictions for innovative agriculture.
We as an AI software provider developed, with the help of computer vision, an algorithm for monitoring & predicting urban change.
As an AI-IT software provider, we designed software for Ströer that provides accurate simulations to predict the performance of advertising campaigns.
By Tiago Sanona • April 3rd, 2023
We explain a deep learning-based algorithm for the classification of crop types from satellite time series data which is based on the transformer architecture.
By Edit Szügyi • March 14th, 2023
Protecting the natural environment is arguably the biggest challenge of our generation. I show how and where Machine Learning can help.
By William Clemens (PhD) • September 26th, 2022
Introduction to metric learning: Expand your knowledge of metric learning, common distance measures & loss functions such as the triplet loss.
By Konrad Mundinger • August 29th, 2022
Expand your knowledge about recommender systems: Explanation & application and examples, as well as info about collaborative & content-based filtering here.
By Emilius Richter • August 2nd, 2022
Python tools for remote sensing using machine learning: Comparison of Python software for data retrieval and processing of satellite data read here.
By Madina Kasymova • May 31st, 2022
An application of deep learning: Read here about image labeling algorithms & an approach to image labeling - the attention mechanism.
By • March 21st, 2022
Read here how to get a labeling tool for image classification working in a Jupyter notebook & what options there are for extending it.
Machine Learning Project Lead