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Lena Enzenhofer, MSc

Master Thesis: Nutzerzentriertes Design eines Interface zum Labeling von mehrdimensionalen videobasierten Daten aus Luftbildaufnahmen.

UX ResearchPrototypingInteraction DesignUser Tests

Imagine this: miles of aerial footage, multidimensional data like RGB, infrared, and GPS, all waiting to be labeled and used for AI training. The problem? Existing tools made it feel like trying to solve a Rubik's Cube with one hand—clunky, inefficient, and anything but user-friendly. My goal was clear: design a system that makes data labeling intuitive, efficient, and even enjoyable.

How to approach this Challenge 👩‍💻

To tackle this, I immersed myself in research, breaking down existing tools to understand what worked—and what didn’t. The question that fueled my work was: How can we design an interface that simplifies labeling while empowering users to work smarter, not harder? I followed a user-centered design process:
  • Concept Development: I created multiple interface prototypes focusing on intuitive data visualization, efficient workflows, and accessible labeling.
  • Iterative Prototyping: Using tools like Adobe XD, I designed three versions of a clickable prototype, each exploring different approaches to displaying and labeling data.
  • Testing and Refinement: I brought 15 participants into the mix, testing how they interacted with the designs. The goal? Discover what felt natural, efficient, and enjoyable.
But I didn’t stop there. I also analyzed mountains of usability data—timing each interaction, logging errors, and capturing user feedback. Every click and every comment helped me refine the design into something truly user-centric.
Sketch Variante 1Sketch Variante 2Sketch Variante 3

The Solution 💡

The final prototype combined advanced data visualization with seamless interaction:
  • Dynamic Data Views: A large primary view with preview panels allowed users to compare RGB, infrared, and other data types without unnecessary clicks.
  • One-Click Annotation: Users could label objects with minimal effort, cutting down on time and cognitive load.
  • Overlay Attribute Input: Attributes could be assigned directly on the object, avoiding clunky side panels and keeping the focus on the task.
But I didn’t stop there. I also analyzed mountains of usability data—timing each interaction, logging errors, and capturing user feedback. Every click and every comment helped me refine the design into something truly user-centric.
Mockup final Prototype

Data Driven Insides

Testing wasn’t just about making something that “felt good”—it was about proving the design’s impact. I analyzed the results meticulously:
  • Users completed tasks significantly faster with the one-click method compared to traditional tools.
  • Visualization mattered: the preview-pane layout helped users identify objects more accurately and confidently.
  • Overlays for attribute input reduced mouse travel and boosted efficiency, especially in high-density datasets.
The study results were visualized in detailed charts, uncovering patterns and pinpointing how specific design choices directly improved the workflow.
Data Analysis 1Data Analysis 2

What I Learned

Design is as much about testing and analysis as it is about creativity. By combining user feedback with rigorous data analysis, I was able to build a solution that wasn’t just functional—it was transformative. Watching my design empower users to handle complex data with ease was incredibly rewarding.

Why It Matters:

This project was more than just a thesis—it was about creating real-world impact. By improving workflows and reducing frustration, I helped build a tool that can advance fields like biodiversity monitoring and AI training, where accurate, efficient labeling is critical.

Want to dive deeper into the process or chat about how design can turn complexity into simplicity? Let’s connect—I’d love to share more about my work! 😊

Download my CV