Convolutional neural networks

Mapping land-use intensity of grasslands in Germany with machine learning and Sentinel-2 time series

Information on grassland land-use intensity (LUI) is crucial for understanding trends and dynamics in biodiversity, ecosystem functioning, earth system science and environmental monitoring. LUI is a major driver for numerous environmental processes …

Spatially Autocorrelated Training and Validation Samples Inflate Performance Assessment of Convolutional Neural Networks

Deep learning and particularly Convolutional Neural Networks (CNN) in concert with remote sensing are becoming standard analytical tools in the geosciences. A series of studies has presented the seemingly outstanding performance of CNN for predictive …

Mapping Forest Tree Species in High Resolution UAV-Based RGB-Imagery by Means of Convolutional Neural Networks

The use of unmanned aerial vehicles (UAVs) in vegetation remote sensing allows a time-flexible and cost-effective acquisition of very high-resolution imagery. Still, current methods for the mapping of forest tree species do not exploit the …