no code implementations • 12 Mar 2024 • Robin Zbinden, Nina van Tiel, Marc Rußwurm, Devis Tuia
In the face of significant biodiversity decline, species distribution models (SDMs) are essential for understanding the impact of climate change on species habitats by connecting environmental conditions to species occurrences.
no code implementations • 8 Dec 2023 • Ribana Roscher, Marc Rußwurm, Caroline Gevaert, Michael Kampffmeyer, Jefersson A. dos Santos, Maria Vakalopoulou, Ronny Hänsch, Stine Hansen, Keiller Nogueira, Jonathan Prexl, Devis Tuia
These examples provide concrete steps to act on geospatial data with data-centric machine learning approaches.
1 code implementation • 28 Nov 2023 • Konstantin Klemmer, Esther Rolf, Caleb Robinson, Lester Mackey, Marc Rußwurm
The resulting SatCLIP location encoder efficiently summarizes the characteristics of any given location for convenient use in downstream tasks.
1 code implementation • 10 Oct 2023 • Marc Rußwurm, Konstantin Klemmer, Esther Rolf, Robin Zbinden, Devis Tuia
At the same time, little attention has been paid to the exact design of the neural network architectures with which these functional embeddings are combined.
1 code implementation • 5 Jul 2023 • Marc Rußwurm, Sushen Jilla Venkatesa, Devis Tuia
Here, remote sensing can provide reliable estimates of plastic pollution by regularly monitoring and detecting marine debris in coastal areas.
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.
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 • 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.
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.
2 code implementations • 30 Jan 2019 • Marc Rußwurm, Nicolas Courty, Rémi Emonet, Sébastien Lefèvre, Devis Tuia, Romain Tavenard
In this work, we present an End-to-End Learned Early Classification of Time Series (ELECTS) model that estimates a classification score and a probability of whether sufficient data has been observed to come to an early and still accurate decision.
1 code implementation • 5 Dec 2018 • Tim G. J. Rudner, Marc Rußwurm, Jakub Fil, Ramona Pelich, Benjamin Bischke, Veronika Kopackova, Piotr Bilinski
We propose a novel approach for rapid segmentation of flooded buildings by fusing multiresolution, multisensor, and multitemporal satellite imagery in a convolutional neural network.
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.
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.