Filip Wolf, MSc

Researcher
  filip-wolf-246981195

I am a PhD studenet at Visual Cognitive Systems Lab at the Faculty of Computer and Information Science, University of Ljubljana, under the supervision of Luka Čehovin Zajc.

My research primarily focuses on multimodal Earth observation foundation models (EOFMs) tailored towards dense tasks like segmentation.

Research

Foundation models for Earth observation

We study foundation models for Earth observation, focusing on transferable representations, multispectral imagery, cross-modal knowledge transfer, and efficient adaptation across sensors and tasks.

Remote sensing change detection

We develop supervised and unsupervised methods for detecting semantic changes in multi-temporal Earth-observation imagery, with an emphasis on robust design, generalization, and limited annotations.

Remote sensing

Contains 4 subtopics
We use modern computer vision and machine learning methods to analyze the growing volume of satellite and aerial imagery and address a range of remote-sensing problems.

Projects

Geospatial Information Technologies for a Resilient and Sustainable Society

July 2025 - June 2028
The GeoAI project develops advanced geospatial modeling and analytics methods to support the sustainable management of the built and natural environment.

EOFuseREarth Observation with Sensor-Fusion and Representation Learning

January 2025 - April 2026
This ESA funded project investigates the relationship between sensor fusion and self-supervised learning for data-driven Earth Observation. We focus on the role of self-supervised deep learning for sensor fusion from the perspective of different sources with different spatial resolutions and spectral coverage. The project is grounded in a real-world application in the field of hydrology, where the goal is to predict the water level in rivers using satellite and drone imagery.

RoDEORobust Deep Learning for Earth Observation​

January 2025 - December 2027
This ARIS funded project investigats the relationship between sensor fusion and self-supervised learning for data-driven Earth Observation. We focus on the role of self-supervised deep learning for sensor fusion from the perspective of different sources with different spatial resolutions and spectral coverage. The project is grounded in a real-world application in the field of hydrology, where the goal is to predict the water level in rivers using satellite and drone imagery.

Awards

Publications