NEWS! NEWS! NEWS! This website is under construction. Next up: CV section.

Hello .
I'm Tom an electrical engineer and
machine learning researcher!

Me smiling

Currently, I am working at the TU Dortmund University as a research assistant in the Image Signal Processing group. I am trying to obtain my Doctor of Engineering in the field of machine learning (AI). If you want, you can contact me anytime via the contact form linked below or through social media.


My coding languages

Here you can see the coding languages I am most familiar with.

Python

Experience: 5 years

PyTorch

Experience: 5 years

Matlab

Experience: 3 years

Git/GitHub

Experience: 6 years

Most important skills

A brief overview of my most important skills.

Machine Learning.

I apply machine learning to analyze remote sensing data, specializing in anomaly detection and uncovering multi-modal correlations. My work leverages transformer models to develop foundational models that interpret complex patterns across diverse remote-sensing datasets.

For example, I recently published research on landing sites and techno-signature detection in lunar images. This work utilized state-of-the-art anomaly detection methods, such as PatchCore and AnoVit, to identify these signatures within large-scale lunar surface datasets (GitHub).

I have also developed a multi-modal conversion model for grayscale images, normal maps, digital height maps, and albedo maps into each other. This work has demonstrated the potential for discovering valuable correlations between these diverse data types (To be presented at LPSC in March).
(For further projects, you can go to the Papers / Project page).

images of skills

Recent Paper / Project

The last paper or project I worked on and submitted.

Lunar Technosignatures: A Deep Learning Approach to Detecting Apollo Landing Sites on the Lunar Surface

profile picture
Tom Sander1
Christian Wöhler1

1TU Dortmund University, Image Analysis Group, Dortmund, Germany

Abstract

Uncovering anomalies on the lunar surface is crucial for understanding the Moon's geological and astronomical history. By identifying and studying these anomalies, new theories about the changes that have occurred on the Moon can be developed or refined. This study seeks to enhance anomaly detection on the Moon and replace the time-consuming manual data search process by testing an anomaly detection method using the Apollo landing sites. The landing sites are advantageous as they are both anomalous and can be located, enabling an assessment of the procedure. Our study compares the performance of various state-of-the-art machine learning algorithms in detecting anomalies in the Narrow-Angle Camera data from the Lunar Reconnaissance Orbiter spacecraft. The results demonstrate that our approach outperforms previous publications in accurately predicting landing site artifacts and technosignatures at the Apollo 15 and 17 landing sites. While our method achieves promising results, there is still room for improvement. Future refinements could focus on detecting more subtle anomalies, such as the rover tracks left by the Apollo missions.

Keywords:

Anomaly Detection Machine Learning Moon Lunar Surface

"Unlocking the potential of remote sensing data to cultivate a deeper understanding of our ever-changing world."

profile picture
Tom Sander
Owner of this website