<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Multimodal Data on Michael Mommert</title><link>https://mommermi.github.io/tags/multimodal-data/</link><description>Recent content in Multimodal Data on Michael Mommert</description><generator>Hugo</generator><language>en-US</language><lastBuildDate>Thu, 30 Apr 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://mommermi.github.io/tags/multimodal-data/index.xml" rel="self" type="application/rss+xml"/><item><title>Modeling of Road Traffic Noise using Deep Learning</title><link>https://mommermi.github.io/research/2026_04_30-modellierung_der_strassenlaermbelastung_mittels_deep_learning/</link><pubDate>Thu, 30 Apr 2026 00:00:00 +0000</pubDate><guid>https://mommermi.github.io/research/2026_04_30-modellierung_der_strassenlaermbelastung_mittels_deep_learning/</guid><description>&lt;p&gt;Deep learning has already proven its potential in many areas of remote sensing. One example is the classification of land use and land cover from satellite and aerial imagery.&lt;/p&gt;
&lt;p&gt;But perhaps we can go one step further: once an AI model has learned how certain things are related, can we also ask it &amp;ldquo;what if?&amp;rdquo; questions? By deliberately changing the data we feed into the model, we can observe how its output changes. In this way, an AI could serve as a kind of simplified simulator, for example in so-called digital twins. These are virtual replicas of real cities or landscapes in which changes can be tested before they are implemented in the real world.&lt;/p&gt;</description></item></channel></rss>