1 code implementation • 10 Oct 2023 • Maja Schneider, Marco Körner
With leaps in machine learning techniques and their applicationon Earth observation challenges has unlocked unprecedented performance across the domain.
no code implementations • 10 Oct 2023 • Ayshah Chan, Maja Schneider, Marco Körner
We propose an approach for early crop classification through identifying important timesteps with eXplainable AI (XAI) methods.
no code implementations • 14 Jun 2021 • Maja Schneider, Amelie Broszeit, Marco Körner
We present EuroCrops, a dataset based on self-declared field annotations for training and evaluating methods for crop type classification and mapping, together with its process of acquisition and harmonisation.
1 code implementation • RC 2020 • Maja Schneider, Marco Körner
Additionally, we also compiled an alternative dataset similar to the one presented in the paper and evaluated the methodology on it.
no code implementations • 23 Nov 2020 • Chunping Qiu, Lukas Liebel, Lloyd H. Hughes, Michael Schmitt, Marco Körner, Xiao Xiang Zhu
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.
no code implementations • 28 Apr 2020 • Marc Rußwurm, Sherrie Wang, Marco Körner, David Lobell
This indicates that model optimization with meta-learning may benefit tasks in the Earth sciences whose data show a high degree of diversity from region to region, while traditional gradient-based supervised learning remains suitable in the absence of a feature or label shift.
no code implementations • 6 Apr 2020 • Lukas Liebel, Ksenia Bittner, Marco Körner
Such basic models can be filtered by convolutional neural networks (CNNs), trained on labels derived from digital elevation models (DEMs) and 3D city models, in order to obtain a refined DSM.
2 code implementations • 23 Oct 2019 • Marc Rußwurm, Marco Körner
The amount of available Earth observation data has increased dramatically in the recent years.
no code implementations • 24 Sep 2019 • Peter König, Sandra Aigner, Marco Körner
This ensures the quality of the predicted frames to be sufficient to enable accurate detection of objects, which is especially important for autonomously driving cars.
no code implementations • 27 Aug 2019 • Marc Rußwurm, Romain Tavenard, Sébastien Lefèvre, Marco Körner
In this work, we introduce a recently developed early classification mechanism to satellite-based agricultural monitoring.
1 code implementation • 25 Jul 2019 • Lukas Liebel, Marco Körner
Hence, in order to overcome the notorious instability and slow convergence of depth value regression during training, MultiDepth makes use of depth interval classification as an auxiliary task.
2 code implementations • 28 May 2019 • Marc Rußwurm, Charlotte Pelletier, Maximilian Zollner, Sébastien Lefèvre, Marco Körner
We present Breizhcrops, a novel benchmark dataset for the supervised classification of field crops from satellite time series.
no code implementations • 22 Apr 2019 • Ksenia Bittner, Marco Körner, Peter Reinartz
We present the workflow of a DSM refinement methodology using a Hybrid-cGAN where the generative part consists of two encoders and a common decoder which blends the spectral and height information within one network.
1 code implementation • 8 Mar 2019 • Ksenia Bittner, Marco Körner, Peter Reinartz
We describe the workflow of a digital surface models (DSMs) refinement algorithm using a hybrid conditional generative adversarial network (cGAN) where the generative part consists of two parallel networks merged at the last stage forming a WNet architecture.
1 code implementation • 28 Oct 2018 • Marc Rußwurm, Marco Körner
Clouds frequently cover the Earth's surface and pose an omnipresent challenge to optical Earth observation methods.
1 code implementation • 2 Oct 2018 • Sandra Aigner, Marco Körner
The main advantage of the FutureGAN framework is that it is applicable to various different datasets without additional changes, whilst achieving stable results that are competitive to the state-of-the-art in video prediction.
no code implementations • 7 Jul 2018 • Seyed Majid Azimi, Eleonora Vig, Reza Bahmanyar, Marco Körner, Peter Reinartz
During training, we minimize joint horizontal and oriented bounding box loss functions, as well as a novel loss that enforces oriented boxes to be rectangular.
Ranked #49 on Object Detection In Aerial Images on DOTA (using extra training data)
1 code implementation • 16 May 2018 • Lukas Liebel, Marco Körner
Multi-task convolutional neural networks (CNNs) have shown impressive results for certain combinations of tasks, such as single-image depth estimation (SIDE) and semantic segmentation.
no code implementations • 3 May 2018 • Tobias Koch, Lukas Liebel, Friedrich Fraundorfer, Marco Körner
While an increasing interest in deep models for single-image depth estimation methods can be observed, established schemes for their evaluation are still limited.
no code implementations • 19 Mar 2018 • Seyed Majid Azimi, Peter Fischer, Marco Körner, Peter Reinartz
Therefore, accurate and reliable lane marking semantic segmentation in the imagery of roads and highways is needed to achieve such goals.
no code implementations • 25 Feb 2018 • Jian Kang, Marco Körner, Yuanyuan Wang, Hannes Taubenböck, Xiao Xiang Zhu
The proposed method is based on Convolutional Neural Networks (CNNs) which classify facade structures from street view images, such as Google StreetView, in addition to remote sensing images which usually only show roof structures.
no code implementations • International Journal of Geo-Information 2018 • Marc Rußwurm, Marco Körner
Inspired by these sequence-to-sequence models, we adapt an encoder structure with convolutional recurrent layers in order to approximate a phenological model for vegetation classes based on a temporal sequence of Sentinel 2 (S2) images.