7 citations · 8 across the 7 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
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…
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…
Kuro Siwo: 33 billion under the water. A global multi-temporal satellite dataset for rapid flood mapping
Nikolaos Ioannis Bountos, Maria Sdraka, Angelos Zavras +6
Global floods, exacerbated by climate change, pose severe threats to human life, infrastructure, and the environment. Recent catastrophic events in Pakistan and New Zealand undersc…
Benchmarking and scaling of deep learning models for land cover image classification
Ioannis Papoutsis, Nikolaos-Ioannis Bountos, Angelos Zavras +2
The availability of the sheer volume of Copernicus Sentinel-2 imagery has created new opportunities for exploiting deep learning (DL) methods for land use land cover (LULC) image c…