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.
no code implementations • 23 Aug 2022 • Ahmet Kerem Aksoy, Pavel Dushev, Eleni Tzirita Zacharatou, Holmer Hemsen, Marcela Charfuelan, Jorge-Arnulfo Quiané-Ruiz, Begüm Demir, Volker Markl
To address this limitation, we have recently proposed MiLaN, a content-based image retrieval approach for fast similarity search in satellite image archives.
no code implementations • 16 Feb 2019 • Gencer Sumbul, Marcela Charfuelan, Begüm Demir, Volker Markl
This paper presents the BigEarthNet that is a new large-scale multi-label Sentinel-2 benchmark archive.
no code implementations • LREC 2014 • Renlong Ai, Marcela Charfuelan, Walter Kasper, Tina Kl{\"u}wer, Hans Uszkoreit, Feiyu Xu, S Gasber, ra, Gien, Philip t
Modern language learning courses are no longer exclusively based on books or face-to-face lectures.
no code implementations • LREC 2014 • Renlong Ai, Marcela Charfuelan
In the area of Computer Assisted Language Learning(CALL), second language (L2) learners spoken data is an important resource for analysing and annotating typical L2 pronunciation errors.