activity
20222026
most citedSelf-supervised Contrastive Learning for Volcanic Unrest Detection

29 citations · 49 across the 14 of their papers we have counts for

collaborators

15 papers

cs.CV2026

Hide and Seek: Investigating Redundancy in Earth Observation Imagery

Tasos Papazafeiropoulos, Nikolaos Ioannis Bountos, Nikolas Papadopoulos +1

The growing availability of Earth Observation (EO) data and recent advances in Computer Vision have driven rapid progress in machine learning for EO, producing domain-specific mode…

cs.CV2026

TIRAuxCloud: A Thermal Infrared Dataset for Day and Night Cloud Detection

Alexis Apostolakis, Vasileios Botsos, Niklas Wölki +4

Clouds are a major obstacle in Earth observation, limiting the usability and reliability of critical remote sensing applications such as fire disaster response, urban heat island m…

cs.CV2025

TeleViT1.0: Teleconnection-aware Vision Transformers for Subseasonal to Seasonal Wildfire Pattern Forecasts

Ioannis Prapas, Nikolaos Papadopoulos, Nikolaos-Ioannis Bountos +3

Forecasting wildfires weeks to months in advance is difficult, yet crucial for planning fuel treatments and allocating resources. While short-term predictions typically rely on loc…

cs.CV2025

Thalia: A Global, Multi-Modal Dataset for Volcanic Activity Monitoring

Nikolas Papadopoulos, Nikolaos Ioannis Bountos, Maria Sdraka +3

Monitoring volcanic activity is of paramount importance to safeguarding lives, infrastructure, and ecosystems. However, only a small fraction of known volcanoes are continuously mo…

cs.LG2025

Probabilistic Machine Learning for Noisy Labels in Earth Observation

Spyros Kondylatos, Nikolaos Ioannis Bountos, Ioannis Prapas +3

Label noise poses a significant challenge in Earth Observation (EO), often degrading the performance and reliability of supervised Machine Learning (ML) models. Yet, given the crit…

cs.CV2025

Towards a Unified Copernicus Foundation Model for Earth Vision

Yi Wang, Zhitong Xiong, Chenying Liu +8

Advances in Earth observation (EO) foundation models have unlocked the potential of big satellite data to learn generic representations from space, benefiting a wide range of downs…