30.07.2025

Teaser image to Tracking Our Changing Planet from Space - with Xiaoxiang Zhu

Tracking Our Changing Planet From Space - With Xiaoxiang Zhu

Research Film

From dreaming of seeing the Earth from space to leading efforts to understand our planet using AI and satellite data to tackle urgent global challenges. Xiaoxiang Zhu, Chair Professor for Data Science in Earth Observation at TUM and PI at MCML, develops machine learning systems that analyze petabytes of satellite imagery. Her work focuses on extracting reliable geo-information from raw data, especially in places where data is scarce or misleading.

In this video, Xiaoxiang Zhu explains how her team segments informal settlements across the Global South and estimates population density using building height and function. These tools help close critical knowledge gaps, particularly in regions where poverty is underrepresented in current datasets.

Her aim is to turn complex remote sensing data into actionable insights for addressing urbanization, climate change, and the UN’s Sustainable Development Goals. By combining technical innovation with social impact, her work shows how AI can help us better understand — and improve — life on Earth.

The film was produced and edited by Nicole Huminski and Nikolai Huber.

 

#blog #research #zhu

Related

Tiny logo
Link to MCML at ICDAR 2026

28.08.2026

MCML at ICDAR 2026

MCML researchers are represented with 1 paper at ICDAR 2026.

Read more
Link to Transparency for global health aid

26.08.2026

Transparency for Global Health Aid

MCML PI Stefan Feuerriegel and his team use machine learning to uncover disparities in the global allocation of health aid.

Read more
Link to David Rügamer: We Need Uncertainty Quantification

25.08.2026

David Rügamer: We Need Uncertainty Quantification

MCML PI David Rügamer explains why uncertainty quantification is essential for building trustworthy AI and making informed decisions.

Read more
Link to Using Data Science to Personalize Depression Care

24.08.2026

Using Data Science to Personalize Depression Care

Five young researchers explore how data science can contribute to better healthcare as part of DSSGx Munich 2026, supported by MCML.

Read more
Tiny logo
Link to MCML at IJCAI-ECAI 2026

14.08.2026

MCML at IJCAI-ECAI 2026

MCML researchers are represented with 1 paper at IJCAI-ECAI 2026.

Read more
Back to Top