Search Results for author: Thomas Scholten

Found 6 papers, 2 papers with code

SoilNet: An Attention-based Spatio-temporal Deep Learning Framework for Soil Organic Carbon Prediction with Digital Soil Mapping in Europe

1 code implementation7 Aug 2023 Nafiseh Kakhani, Moien Rangzan, Ali Jamali, Sara Attarchi, Seyed Kazem Alavipanah, Thomas Scholten

Digital soil mapping (DSM) is an advanced approach that integrates statistical modeling and cutting-edge technologies, including machine learning (ML) methods, to accurately depict soil properties and their spatial distribution.

Attribute Decision Making +2

Latent State Inference in a Spatiotemporal Generative Model

no code implementations21 Sep 2020 Matthias Karlbauer, Tobias Menge, Sebastian Otte, Hendrik P. A. Lensch, Thomas Scholten, Volker Wulfmeyer, Martin V. Butz

Knowledge about the hidden factors that determine particular system dynamics is crucial for both explaining them and pursuing goal-directed interventions.

Causal Inference Time Series +1

Inferring, Predicting, and Denoising Causal Wave Dynamics

no code implementations19 Sep 2020 Matthias Karlbauer, Sebastian Otte, Hendrik P. A. Lensch, Thomas Scholten, Volker Wulfmeyer, Martin V. Butz

The novel DISTributed Artificial neural Network Architecture (DISTANA) is a generative, recurrent graph convolution neural network.

Denoising

Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran

no code implementations12 Jul 2020 Mostafa Emadi, Ruhollah Taghizadeh-Mehrjardi, Ali Cherati, Majid Danesh, Amir Mosavi, Thomas Scholten

The SOC content was the highest in udic soil moisture regime class with mean values of 4 percent, followed by the aquic and xeric classes, respectively.

BIG-bench Machine Learning feature selection

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