no code implementations • 25 Mar 2024 • Francisco Mena, Diego Arenas, Andreas Dengel
Deep learning models have proven to be effective for this task by mapping time series data to high-level representation for prediction.
Ranked #1 on Crop Classification on CropHarvest - Brazil
1 code implementation • 21 Mar 2024 • Francisco Mena, Diego Arenas, Marcela Charfuelan, Marlon Nuske, Andreas Dengel
In this work, we assess the impact of missing temporal and static EO sources in trained models across four datasets with classification and regression tasks.
no code implementations • 22 Jan 2024 • Francisco Mena, Deepak Pathak, Hiba Najjar, Cristhian Sanchez, Patrick Helber, Benjamin Bischke, Peter Habelitz, Miro Miranda, Jayanth Siddamsetty, Marlon Nuske, Marcela Charfuelan, Diego Arenas, Michaela Vollmer, Andreas Dengel
The GU module learned different weights based on the country and crop-type, aligning with the variable significance of each data source to the prediction task.
no code implementations • 17 Aug 2023 • Deepak Pathak, Miro Miranda, Francisco Mena, Cristhian Sanchez, Patrick Helber, Benjamin Bischke, Peter Habelitz, Hiba Najjar, Jayanth Siddamsetty, Diego Arenas, Michaela Vollmer, Marcela Charfuelan, Marlon Nuske, Andreas Dengel
We introduce a simple yet effective early fusion method for crop yield prediction that handles multiple input modalities with different temporal and spatial resolutions.
1 code implementation • 10 Aug 2023 • Francisco Mena, Diego Arenas, Marlon Nuske, Andreas Dengel
Instead, we present a comparison of multi-view fusion methods for three different datasets and show that, depending on the test region, different methods obtain the best performance.
Ranked #2 on Crop Classification on CropHarvest - Togo
1 code implementation • 20 Dec 2022 • Francisco Mena, Diego Arenas, Marlon Nuske, Andreas Dengel
However, the approaches in the literature vary greatly since different terminology is used to refer to similar concepts or different illustrations are given to similar techniques.
1 code implementation • 23 Jul 2020 • Anthony D. Blaom, Franz Kiraly, Thibaut Lienart, Yiannis Simillides, Diego Arenas, Sebastian J. Vollmer
MLJ (Machine Learing in Julia) is an open source software package providing a common interface for interacting with machine learning models written in Julia and other languages.
no code implementations • 24 Nov 2014 • Diego Arenas, Remy Chevirer, Said Hanafi, Joaquin Rodriguez
The elaboration of this timetable is done to respond to the commercial requirements for both passenger and freight traffic, but also it must respect a set of security and capacity constraints associated with the railway network, rolling stock and legislation.