By Emilius Richter • August 2nd, 2022
I present the best (Python) tools for remote sensing and processing of satellite data, based on our practical experience with them at dida. For some I provide application examples including code.
By Emilius Richter • July 18th, 2022
For a software provider, the project proposal is the first step toward meeting the needs of the customer. In this article, I will describe the most important modules in machine learning project proposals.
By Tiago Sanona • August 19th, 2021
Following the paper by Garnot et al., we utilize an altered version of the Transformer to classify crop fields from time series of satellite images. With this, we achieve better results than traditional methods (e. g. random forests) and with less resources than recurrent networks.
By Fabian Gringel • June 21st, 2021
Contrastive Language-Image Pretraining (short: CLIP) makes use of image captions to train a zero-shot image classifier. In this blog article I will give a rough non-technical outline of how CLIP works, and I will also show how you can try CLIP out yourself!
By Moritz Besser • June 4th, 2021
In this blog article, I want to briefly describe the process of migrating labels from Planet Scope to Sentinel-2 images.
By Emilius Richter • May 21st, 2021
We discuss what questions should be considered and answered up front to launch a successful machine learning software project.
By The best image labeling tools for Computer Vision • April 8th, 2021
Here we have a closer look at some of the best image labeling tools for Computer Vision tasks. We will install and configure the tools and illustrate their capabilities by applying them to label real images for an object detection task.
By Fabian Dechent • March 19th, 2021
In this article, we will have a look at the possibilities that remote sensing via spaceborne imaging provides for greenfield exploration, i.e. locating areas where the soil bears high grade minerals of interest.