Publications

Soil Texture Classification with 1D Convolutional Neural Networks based on Hyperspectral Data

Soil texture is important for many environmental processes. In this paper, we study the classification of soil texture based on …

SUSI: Supervised Self-Organizing Maps for Regression and Classification in Python

In many research fields, the sizes of the existing datasets vary widely. Hence, there is a need for machine learning techniques which …

Examples for CNN training and classification on Sentinel-2 data

Overview about state-of-the-art land-use classification from satellite data with CNNs based on an open dataset.

Developing a machine learning framework for estimating soil moisture with VNIR hyperspectral data

In this paper, we investigate the potential of estimating the soil-moisture content based on VNIR hyperspectral data combined with IR …

Fusion of hyperspectral and ground penetrating radar data to estimate soil moisture

In this contribution, we investigate the potential of hyperspectral data combined with either simulated ground penetrating radar (GPR) …

Introducing a Framework of Self-Organizing Maps for Regression of Soil Moisture with Hyperspectral Data

In this paper, we introduce a framework to solve regression problems based on high-dimensional and small datasets. This framework …

Modeling Subsurface Soil Moisture Based on Hyperspectral Data: First Results of a Multilateral Field Campaign

Soil moisture dynamics and its spatial distribution represent important data of a landscape. Until today, a precise and spatially …