Soil texture is important for many environmental processes. In this paper, we study the classification of soil texture based on hyperspectral data. We develop and implement three 1-dimensional (1D) convolutional neural networks (CNN): the LucasCNN, …
Im Forschungsfeld des Maschinellen Lernens werden zunehmend leicht zugängliche Framework wie Keras, Tensorflow oder Pytorch verwendet. Hierdurch ist ein Austausch und die Wiederverwendung bestehender (trainierter) neuronaler Netze möglich. --- Wir am …
Overview about state-of-the-art land-use classification from satellite data with CNNs based on an open dataset.
The largest earth observation programme Copernicus (http://copernicus.eu) makes it possible to perform terrestrial observations providing data for all kinds of purposes. One important objective is to monitor the land-use and land-cover changes with …
In this paper, we investigate the potential of estimating the soil-moisture content based on VNIR hyperspectral data combined with IR data. Measurements from a multi-sensor field campaign represent the benchmark dataset which contains measured …
Inland waters are of great importance for scientists as well as authorities since they are essential ecosystems and well known for their biodiversity. When monitoring their respective water quality, in situ measurements of water quality parameters …