253 citations · 1k across the 27 of their papers we have counts for
27 papers
HyBiomass: Global Hyperspectral Imagery Benchmark Dataset for Evaluating Geospatial Foundation Models in Forest Aboveground Biomass Estimation
Aaron Banze, Timothée Stassin, Nassim Ait Ali Braham +3
Comprehensive evaluation of geospatial foundation models (Geo-FMs) requires benchmarking across diverse tasks, sensors, and geographic regions. However, most existing benchmark dat…
SARFormer -- An Acquisition Parameter Aware Vision Transformer for Synthetic Aperture Radar Data
Jonathan Prexl, Michael Recla, Michael Schmitt
This manuscript introduces SARFormer, a modified Vision Transformer (ViT) architecture designed for processing one or multiple synthetic aperture radar (SAR) images. Given the comp…
Distribution Shifts at Scale: Out-of-distribution Detection in Earth Observation
Burak Ekim, Girmaw Abebe Tadesse, Caleb Robinson +4
Training robust deep learning models is crucial in Earth Observation, where globally deployed models often face distribution shifts that degrade performance, especially in low-data…
Mapping Land Naturalness from Sentinel-2 using Deep Contextual and Geographical Priors
Burak Ekim, Michael Schmitt
In recent decades, the causes and consequences of climate change have accelerated, affecting our planet on an unprecedented scale. This change is closely tied to the ways in which…
UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series
Patrick Ebel, Vivien Sainte Fare Garnot, Michael Schmitt +2
Clouds and haze often occlude optical satellite images, hindering continuous, dense monitoring of the Earth's surface. Although modern deep learning methods can implicitly learn to…
EOD: The IEEE GRSS Earth Observation Database
Michael Schmitt, Pedram Ghamisi, Naoto Yokoya +1
In the era of deep learning, annotated datasets have become a crucial asset to the remote sensing community. In the last decade, a plethora of different datasets was published, eac…
SEN12MS-CR-TS: A Remote Sensing Data Set for Multi-modal Multi-temporal Cloud Removal
Patrick Ebel, Yajin Xu, Michael Schmitt +1
About half of all optical observations collected via spaceborne satellites are affected by haze or clouds. Consequently, cloud coverage affects the remote sensing practitioner's ca…
Deep-Learning-Based Single-Image Height Reconstruction from Very-High-Resolution SAR Intensity Data
Michael Recla, Michael Schmitt
Originally developed in fields such as robotics and autonomous driving with image-based navigation in mind, deep learning-based single-image depth estimation (SIDE) has found great…
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…
Remote Sensing Image Classification with the SEN12MS Dataset
Michael Schmitt, Yu-Lun Wu
Image classification is one of the main drivers of the rapid developments in deep learning with convolutional neural networks for computer vision. So is the analogous task of scene…
Multi-task Learning for Human Settlement Extent Regression and Local Climate Zone Classification
Chunping Qiu, Lukas Liebel, Lloyd H. Hughes +3
Human Settlement Extent (HSE) and Local Climate Zone (LCZ) maps are both essential sources, e.g., for sustainable urban development and Urban Heat Island (UHI) studies. Remote sens…
Synthesizing Optical and SAR Imagery From Land Cover Maps and Auxiliary Raster Data
Gerald Baier, Antonin Deschemps, Michael Schmitt +1
We synthesize both optical RGB and synthetic aperture radar (SAR) remote sensing images from land cover maps and auxiliary raster data using generative adversarial networks (GANs).…
Mapping horizontal and vertical urban densification in Denmark with Landsat time-series from 1985 to 2018: a semantic segmentation solution
Tzu-Hsin Karen Chen, Chunping Qiu, Michael Schmitt +3
Landsat imagery is an unparalleled freely available data source that allows reconstructing horizontal and vertical urban form. This paper addresses the challenge of using Landsat d…
Multi-Sensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 Imagery
Patrick Ebel, Andrea Meraner, Michael Schmitt +1
This work has been accepted by IEEE TGRS for publication. The majority of optical observations acquired via spaceborne earth imagery are affected by clouds. While there is numerous…
Multi-level Feature Fusion-based CNN for Local Climate Zone Classification from Sentinel-2 Images: Benchmark Results on the So2Sat LCZ42 Dataset
Chunping Qiu, Xiaochong Tong, Michael Schmitt +2
As a unique classification scheme for urban forms and functions, the local climate zone (LCZ) system provides essential general information for any studies related to urban environ…
Weakly Supervised Semantic Segmentation of Satellite Images for Land Cover Mapping -- Challenges and Opportunities
Michael Schmitt, Jonathan Prexl, Patrick Ebel +2
Fully automatic large-scale land cover mapping belongs to the core challenges addressed by the remote sensing community. Usually, the basis of this task is formed by (supervised) m…
A framework for large-scale mapping of human settlement extent from Sentinel-2 images via fully convolutional neural networks
C. Qiu, M. Schmitt, C. Geiss +2
Human settlement extent (HSE) information is a valuable indicator of world-wide urbanization as well as the resulting human pressure on the natural environment. Therefore, mapping…
So2Sat LCZ42: A Benchmark Dataset for Global Local Climate Zones Classification
Xiao Xiang Zhu, Jingliang Hu, Chunping Qiu +14
Access to labeled reference data is one of the grand challenges in supervised machine learning endeavors. This is especially true for an automated analysis of remote sensing images…
SEN12MS -- A Curated Dataset of Georeferenced Multi-Spectral Sentinel-1/2 Imagery for Deep Learning and Data Fusion
Michael Schmitt, Lloyd Haydn Hughes, Chunping Qiu +1
The availability of curated large-scale training data is a crucial factor for the development of well-generalizing deep learning methods for the extraction of geoinformation from m…
Potential of nonlocally filtered pursuit monostatic TanDEM-X data for coastline detection
Michael Schmitt, Gerald Baier, Xiao Xiang Zhu
This article investigates the potential of nonlocally filtered pursuit monostatic TanDEM-X data for coastline detection in comparison to conventional TanDEM-X data, i.e. image pair…
Fusion of TanDEM-X and Cartosat-1 Elevation Data Supported by NeuralNetwork-Predicted Weight Maps
Hossein Bagheri, Michael Schmitt, Xiao Xiang Zhu
Recently, the bistatic SAR interferometry mission TanDEM-X provided a global terrain map with unprecedented accuracy. However, visual inspection and empirical assessment of TanDEM-…
A Framework for SAR-Optical Stereogrammetry over Urban Areas
Hossein Bagheri, Michael Schmitt, Pablo d'Angelo +1
Currently, numerous remote sensing satellites provide a huge volume of diverse earth observation data. As these data show different features regarding resolution, accuracy, coverag…
Fusion of Urban TanDEM-X raw DEMs using variational models
Hossein Bagheri, Michael Schmitt, Xiao Xiang Zhu
Recently, a new global Digital Elevation Model (DEM) with pixel spacing of 0.4 arcseconds and relative height accuracy finer than 2m for flat areas (slopes < 20%) and better than 4…
The SEN1-2 Dataset for Deep Learning in SAR-Optical Data Fusion
Michael Schmitt, Lloyd Haydn Hughes, Xiao Xiang Zhu
While deep learning techniques have an increasing impact on many technical fields, gathering sufficient amounts of training data is a challenging problem in remote sensing. In part…