67 citations · 185 across the 30 of their papers we have counts for
32 papers
FedMAD: Modulation-Aware Directional Aggregation for Federated Learning in Remote Sensing Image Classification
Barış Büyüktaş, Begüm Demir
Federated learning (FL) has recently attracted increasing attention in remote sensing (RS) since it enables collaborative model training across decentralized RS image archives with…
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…
Evaluating the Interpretability of Sparse Autoencoders with Concept Annotations
Jonas Klotz, Cassio F. Dantas, Pallavi Jain +2
Sparse autoencoders (SAEs) are increasingly used to extract interpretable concepts from vision and vision language models, yet existing evaluation methods largely rely on proxy met…
Agentic AI for Remote Sensing: Technical Challenges and Research Directions
Muhammad Akhtar Munir, Muhammad Umer Sheikh, Akashah Shabbir +5
Earth Observation (EO) is moving beyond static prediction toward multi-step analytical workflows that require coordinated reasoning over data, tools, and geospatial state. While fo…
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…
TerraScope: Pixel-Grounded Visual Reasoning for Earth Observation
Yan Shu, Bin Ren, Zhitong Xiong +4
Vision-language models (VLMs) have shown promise in earth observation (EO), yet they struggle with tasks that require grounding complex spatial reasoning in precise pixel-level vis…
Radio Map Prediction from Noisy Environment Information and Sparse Observations
Fabian Jaensch, Çağkan Yapar, Giuseppe Caire +1
Many works have investigated radio map and path loss prediction in wireless networks using deep learning, in particular using convolutional neural networks. However, most assume pe…
Rank-based Geographical Regularization: Revisiting Contrastive Self-Supervised Learning for Multispectral Remote Sensing Imagery
Tom Burgert, Leonard Hackel, Paolo Rota +1
Self-supervised learning (SSL) has become a powerful paradigm for learning from large, unlabeled datasets, particularly in computer vision (CV). However, applying SSL to multispect…
How Much of a Model Do We Need? Redundancy and Slimmability in Remote Sensing Foundation Models
Leonard Hackel, Tom Burgert, Begüm Demir
Large-scale foundation models (FMs) in remote sensing (RS) (denoted as RS FMs) are developed following paradigms established in computer vision (CV), yet the validity of transferri…
Noise-Adaptive Regularization for Robust Multi-Label Remote Sensing Image Classification
Tom Burgert, Julia Henkel, Begüm Demir
The development of reliable methods for multi-label classification (MLC) has become a prominent research direction in remote sensing (RS). As the scale of RS data continues to expa…
ImageNet-trained CNNs are not biased towards texture: Revisiting feature reliance through controlled suppression
Tom Burgert, Oliver Stoll, Paolo Rota +1
The hypothesis that Convolutional Neural Networks (CNNs) are inherently texture-biased has shaped much of the discourse on feature use in deep learning. We revisit this hypothesis…
CSMoE: An Efficient Remote Sensing Foundation Model with Soft Mixture-of-Experts
Leonard Hackel, Tom Burgert, Begüm Demir
Self-supervised learning (SSL) through masked autoencoders (MAEs) has recently attracted great attention for remote sensing (RS) foundation model (FM) development, enabling improve…
FedX: Explanation-Guided Pruning for Communication-Efficient Federated Learning in Remote Sensing
Barış Büyüktaş, Jonas Klotz, Begüm Demir
Federated learning (FL) enables the collaborative training of deep neural networks across decentralized data archives (i.e., clients), where each client stores data locally and onl…
EarthMind: Leveraging Cross-Sensor Data for Advanced Earth Observation Interpretation with a Unified Multimodal LLM
Yan Shu, Bin Ren, Zhitong Xiong +5
Earth Observation (EO) data analysis is vital for monitoring environmental and human dynamics. Recent Multimodal Large Language Models (MLLMs) show potential in EO understanding bu…
Redundancy-Aware Pretraining of Vision-Language Foundation Models in Remote Sensing
Mathis Jürgen Adler, Leonard Hackel, Gencer Sumbul +1
The development of foundation models through pretraining of vision-language models (VLMs) has recently attracted great attention in remote sensing (RS). VLM pretraining aims to lea…
Deep Learning-based Bathymetry Retrieval without In-situ Depths using Remote Sensing Imagery and SfM-MVS DSMs with Data Gaps
Panagiotis Agrafiotis, Begüm Demir
Accurate, detailed, and high-frequent bathymetry is crucial for shallow seabed areas facing intense climatological and anthropogenic pressures. Current methods utilizing airborne o…
A Multi-Modal Federated Learning Framework for Remote Sensing Image Classification
Barış Büyüktaş, Gencer Sumbul, Begüm Demir
Federated learning (FL) enables the collaborative training of deep neural networks across decentralized data archives (i.e., clients) without sharing the local data of the clients.…
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…
Communication-Efficient Federated Learning Based on Explanation-Guided Pruning for Remote Sensing Image Classification
Jonas Klotz, Barış Büyüktaş, Begüm Demir
Federated learning (FL) is a decentralized machine learning paradigm in which multiple clients collaboratively train a global model by exchanging only model updates with the centra…
Radio Map Prediction from Aerial Images and Application to Coverage Optimization
Fabian Jaensch, Giuseppe Caire, Begüm Demir
Several studies have explored deep learning algorithms to predict large-scale signal fading, or path loss, in urban communication networks. The goal is to replace costly measuremen…
reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysis
Kai Norman Clasen, Leonard Hackel, Tom Burgert +3
This paper presents refined BigEarthNet (reBEN) that is a large-scale, multi-modal remote sensing dataset constructed to support deep learning (DL) studies for remote sensing image…
Multi-Modal Vision Transformers for Crop Mapping from Satellite Image Time Series
Theresa Follath, David Mickisch, Jan Hemmerling +3
Using images acquired by different satellite sensors has shown to improve classification performance in the framework of crop mapping from satellite image time series (SITS). Exist…
MagicBathyNet: A Multimodal Remote Sensing Dataset for Bathymetry Prediction and Pixel-based Classification in Shallow Waters
Panagiotis Agrafiotis, Łukasz Janowski, Dimitrios Skarlatos +1
Accurate, detailed, and high-frequent bathymetry, coupled with complex semantic content, is crucial for the undermapped shallow seabed areas facing intense climatological and anthr…
Annotation Cost-Efficient Active Learning for Deep Metric Learning Driven Remote Sensing Image Retrieval
Genc Hoxha, Gencer Sumbul, Julia Henkel +2
Deep metric learning (DML) has shown to be effective for content-based image retrieval (CBIR) in remote sensing (RS). Most of DML methods for CBIR rely on a high number of annotate…