21 citations · 40 across the 20 of their papers we have counts for
22 papers
On the Effectiveness of Adaptation Strategies for VLM-Based Federated Learning in Remote Sensing
Simon Lösche, Barış Büyüktaş, Mathis Adler +3
Federated learning (FL) enables collaborative training of deep learning models across decentralized image archives without requiring data centralization. This paradigm is particula…
OSMGraphCLIP: Learning Global Location Representations from OpenStreetMap Graphs
Dimitrios Michail, Eleni Saka, Ioannis Giannopoulos +1
We present OSMGraphCLIP, a CLIP-style geospatial representation model that learns global location embeddings from freely available OpenStreetMap (OSM) data. OSMGraphCLIP represents…
Set Prediction for Next-Day Active Fire Forecasting
Yuchen Bai, Georgios Athanasiou, Xin Yu +4
Accurate next-day active fire forecasts can support early warning, disaster response, forest risk assessment, and downstream estimation of fire-related carbon emissions. Existing m…
BigEarthNet.txt: A Large-Scale Multi-Sensor Image-Text Dataset and Benchmark for Earth Observation
Johann-Ludwig Herzog, Mathis Jürgen Adler, Leonard Hackel +5
Vision-langugage models (VLMs) have shown strong performance in computer vision (CV), yet their performance on remote sensing (RS) data remains limited due to the lack of large-sca…
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…
Magnifying change: Rapid burn scar mapping with multi-resolution, multi-source satellite imagery
Maria Sdraka, Dimitrios Michail, Ioannis Papoutsis
Delineating wildfire affected areas using satellite imagery remains challenging due to irregular and spatially heterogeneous spectral changes across the electromagnetic spectrum. W…
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…
Uncertainty-Aware Deep Learning for Wildfire Danger Forecasting
Spyros Kondylatos, Nikolas Papadopoulos, Gustau Camps-Valls +1
Wildfires are among the most severe natural hazards, posing a significant threat to both humans and natural ecosystems. The growing risk of wildfires increases the demand for forec…
Wildfire spread forecasting with Deep Learning
Nikolaos Anastasiou, Spyros Kondylatos, Ioannis Papoutsis
Accurate prediction of wildfire spread is crucial for effective risk management, emergency response, and strategic resource allocation. In this study, we present a deep learning (D…
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…
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…
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…
On the Generalization of Representation Uncertainty in Earth Observation
Spyros Kondylatos, Nikolaos Ioannis Bountos, Dimitrios Michail +3
Recent advances in Computer Vision have introduced the concept of pretrained representation uncertainty, enabling zero-shot uncertainty estimation. This holds significant potential…
GAIA: A Global, Multi-modal, Multi-scale Vision-Language Dataset for Remote Sensing Image Analysis
Angelos Zavras, Dimitrios Michail, Xiao Xiang Zhu +2
Existing Vision-Language Models (VLMs) are predominantly trained on web-scraped, noisy image-text data, exhibiting limited exposure to the specialized domain of RS. This deficiency…
FireCastNet: Earth-as-a-Graph for Seasonal Fire Prediction
Dimitrios Michail, Charalampos Davalas, Konstantinos Chafis +5
With climate change intensifying fire weather conditions globally, accurate seasonal wildfire forecasting has become critical for disaster preparedness and ecosystem management. We…
AI for Extreme Event Modeling and Understanding: Methodologies and Challenges
Gustau Camps-Valls, Miguel-Ángel Fernández-Torres, Kai-Hendrik Cohrs +22
In recent years, artificial intelligence (AI) has deeply impacted various fields, including Earth system sciences. Here, AI improved weather forecasting, model emulation, parameter…
Seasonal Fire Prediction using Spatio-Temporal Deep Neural Networks
Dimitrios Michail, Lefki-Ioanna Panagiotou, Charalampos Davalas +4
With climate change expected to exacerbate fire weather conditions, the accurate anticipation of wildfires on a global scale becomes increasingly crucial for disaster mitigation. I…
Causal Graph Neural Networks for Wildfire Danger Prediction
Shan Zhao, Ioannis Prapas, Ilektra Karasante +4
Wildfire forecasting is notoriously hard due to the complex interplay of different factors such as weather conditions, vegetation types and human activities. Deep learning models s…
Neural Plasticity-Inspired Multimodal Foundation Model for Earth Observation
Zhitong Xiong, Yi Wang, Fahong Zhang +7
Earth observation (EO) in open-world settings presents a unique challenge: different applications rely on diverse sensor modalities, each with varying ground sampling distances, sp…
Mind the Modality Gap: Towards a Remote Sensing Vision-Language Model via Cross-modal Alignment
Angelos Zavras, Dimitrios Michail, Begüm Demir +1
Deep Learning (DL) is undergoing a paradigm shift with the emergence of foundation models. In this work, we focus on Contrastive Language-Image Pre-training (CLIP), a Vision-Langua…
SeasFire as a Multivariate Earth System Datacube for Wildfire Dynamics
Ilektra Karasante, Lazaro Alonso, Ioannis Prapas +3
The global occurrence, scale, and frequency of wildfires pose significant threats to ecosystem services and human livelihoods. To effectively quantify and attribute the antecedent…
FoMo: Multi-Modal, Multi-Scale and Multi-Task Remote Sensing Foundation Models for Forest Monitoring
Nikolaos Ioannis Bountos, Arthur Ouaknine, Ioannis Papoutsis +1
Forests are vital to ecosystems, supporting biodiversity and essential services, but are rapidly changing due to land use and climate change. Understanding and mitigating negative…