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.

This project aims to develop a novel motion understanding paradigm, centered on automatically determining the minimal scene understanding required to track one or multiple objects throughout a video. It tackles three core challenges: developing a few-shot object detector capable of identifying all objects in a category based on limited examples, tracking individual objects amid distractors, and extending this to track transformable objects in complex environments.

The functional objective of the project is to advance anomaly detection in images by developing multimodal models that integrate visual and linguistic information to not only detect if and where anomalies occur but also explain why. The main research goal is to create novel methods for semantic image understanding, zero-shot multimodal anomaly detection, and multimodal explanations that improve AI’s interpretability and transparency while reducing reliance on annotated data.

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.

Terraced landscapes are cultural landscapes of special significance. Cultivated terraces, which are often intended for farming, ensure food and have a priceless ecological, cultural, and historical value. Terraced landscapes are disappearing in places due to overgrowth or improper maintenance. In order to raise awareness of their significance, the Honghe Declaration was passed at the global level in 2010. Slovenia still has not managed to implement suitable criteria for terrace identification or introduce a comprehensive management system despite having officially recognized terraces as a valuable element for landscape preservation (see the Decree on Cross-Compliance, Official Gazette of the Republic of Slovenia, no. 97/2015). A precise overview (register) of the locations of cultivated terraces and spatial analyses are required as the foundation for any further measures.

SMASH is an innovative, intersectoral, career-development training program for outstanding postdoctoral researchers, co-funded by the Marie Skłodowska-Curie Actions COFUND program. SMASH is open to researchers around the world who are interested in developing cutting-edge machine learning applications for science and humanities.

This project is a continuation of the successful collaboration enjoyed between Poland, Germany and Portugal in the first SABUVIS project. In this first iteration, three different Biomimetic Underwater Vehicles (BUVs) were constructed with the resulting conclusion that BUVs can be designed with varying degrees of similarity with living organisms. Some of the identified benefits are that undulating propulsion consumes less electrical energy than conventional propulsion systems utilising screw based propellers, and different hydroacoustic signatures are produced with a corresponding lower noise level.

Computer vision is becoming a focal problem area of artificial intelligence. On the wings of deep learning it has become very powerful tool for solving various problems involving processing of visual information. In the framework of this programme we are addressing several research questions ranging from visual tracking to visual learning for autonomous robots, with a special emphasis on going beyond supervised deep learning.