47 citations · 75 across the 9 of their papers we have counts for
9 papers
On the Disagreement in Perturbation-based xAI -- Benchmarking Perturbation Choices for Flood Detection from SAR Images
Anastasia Schlegel, Ronny Hänsch
Perturbation-based xAI methods are widely used to analyze the behavior and predictions of deep learning models. By altering input regions and measuring the resulting changes in cla…
Hybrid Machine Learning Model for Forest Height Estimation from TanDEM-X and Landsat Data
Islam Mansour, Ronny Haensch, Irena Hajnsek +1
Integrating machine learning (ML) with physical models (PM) has emerged as a promising way of retrieving geophysical parameters from remote sensing data. In this context, a ML mode…
Data-Centric Benchmark for Label Noise Estimation and Ranking in Remote Sensing Binary Building Segmentation
Keiller Nogueira, Codrut-Andrei Diaconu, Dávid Kerekes +9
High-quality pixel-level annotations are essential for the semantic segmentation of remote sensing imagery. However, such labels are expensive to obtain and often affected by noise…
Very High-Resolution Forest Mapping with TanDEM-X InSAR Data and Self-Supervised Learning
José-Luis Bueso-Bello, Benjamin Chauvel, Daniel Carcereri +8
Deep learning models have shown encouraging capabilities for mapping accurately forests at medium resolution with TanDEM-X interferometric SAR data. Such models, as most of current…
Hybrid AI-Physical Modeling for Penetration Bias Correction in X-band InSAR DEMs: A Greenland Case Study
Islam Mansour, Georg Fischer, Ronny Haensch +1
Digital elevation models derived from Interferometric Synthetic Aperture Radar (InSAR) data over glacial and snow-covered regions often exhibit systematic elevation errors, commonl…
Better, Not Just More: Data-Centric Machine Learning for Earth Observation
Ribana Roscher, Marc Rußwurm, Caroline Gevaert +8
Recent developments and research in modern machine learning have led to substantial improvements in the geospatial field. Although numerous deep learning architectures and models h…
There Are No Data Like More Data- Datasets for Deep Learning in Earth Observation
Michael Schmitt, Seyed Ali Ahmadi, Yonghao Xu +4
Carefully curated and annotated datasets are the foundation of machine learning, with particularly data-hungry deep neural networks forming the core of what is often called Artific…
Deep Learning and Earth Observation to Support the Sustainable Development Goals
Claudio Persello, Jan Dirk Wegner, Ronny Hänsch +4
The synergistic combination of deep learning models and Earth observation promises significant advances to support the sustainable development goals (SDGs). New developments and a…
There is no data like more data -- current status of machine learning datasets in remote sensing
Michael Schmitt, Seyed Ali Ahmadi, Ronny Hänsch
Annotated datasets have become one of the most crucial preconditions for the development and evaluation of machine learning-based methods designed for the automated interpretation…