67 citations · 185 across the 30 of their papers we have counts for
11 papers · 1 filter
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…
Transformer-based Federated Learning for Multi-Label Remote Sensing Image Classification
Barış Büyüktaş, Kenneth Weitzel, Sebastian Völkers +2
Federated learning (FL) aims to collaboratively learn deep learning model parameters from decentralized data archives (i.e., clients) without accessing training data on clients. Ho…
A Label Propagation Strategy for CutMix in Multi-Label Remote Sensing Image Classification
Tom Burgert, Kai Norman Clasen, Jonas Klotz +2
The development of supervised deep learning-based methods for multi-label scene classification (MLC) is one of the prominent research directions in remote sensing (RS). However, co…
Estimating Physical Information Consistency of Channel Data Augmentation for Remote Sensing Images
Tom Burgert, Begüm Demir
The application of data augmentation for deep learning (DL) methods plays an important role in achieving state-of-the-art results in supervised, semi-supervised, and self-supervise…
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…
Radio Map Estimation -- An Open Dataset with Directive Transmitter Antennas and Initial Experiments
Fabian Jaensch, Giuseppe Caire, Begüm Demir
Over the last years, several works have explored the application of deep learning algorithms to determine the large-scale signal fading (also referred to as ``path loss'') between…
Exploring Masked Autoencoders for Sensor-Agnostic Image Retrieval in Remote Sensing
Jakob Hackstein, Gencer Sumbul, Kai Norman Clasen +1
Self-supervised learning through masked autoencoders (MAEs) has recently attracted great attention for remote sensing (RS) image representation learning, and thus embodies a signif…