There were systematic differences in performance among the fluxes, with the following ascending order: net ecosystem exchange (R 2 < 0.5), ecosystem respiration (R 2 > 0.6), gross primary production (R 2 > 0.7), latent heat (R 2 > 0.7), sensible heat (R 2 > 0.7), and net radiation (R 2 > 0.8). The ML methods predicted the across-site variability and the mean seasonal cycle of the observed fluxes very well (R 2 > 0.7), while the 8-day deviations from the mean seasonal cycle were not well predicted (R 2 < 0.5). Fluxes were better predicted at forested and temperate climate sites than at sites in extreme climates or less represented by training data (e.g., the tropics). The evaluated large ensemble of ML-based models will be the basis of new global flux products.Published by Copernicus Publications on behalf of the European Geosciences Union.

&lt;p&gt;&lt;strong&gt;Abstract.&lt;/strong&gt; Spatial-temporal fields of land-atmosphere fluxes derived from data-driven models can complement simulations by process-based Land Surface Models. While a number of strategies for empirical models with eddy covariance flux data have been applied, a systematic intercomparison of these methods has been missing so far. In this study, we perform a cross-validation experiment for predicting carbon dioxide (CO&lt;sub&gt;2&lt;/sub&gt;), latent heat, sensible heat and net radiation fluxes, in different ecosystem types with eleven machine learning (ML) methods from four different classes (kernel methods, neural network, tree methods, and regression splines). We employ two complementary setups: (1) eight days average fluxes based on remotely sensed data, and (2) daily mean fluxes based on meteorological data and mean seasonal cycle of remotely sensed variables. The pattern of predictions from different ML and setups were very consistent. There were systematic differences in performance among the fluxes, with the following ascending order: net ecosystem exchange (R&lt;sub&gt;2&lt;/sub&gt; &lt; 0.5), ecosystem respiration (R&lt;sub&gt;2&lt;/sub&gt; &gt; 0.6), gross primary production (R&lt;sub&gt;2&lt;/sub&gt; &gt; 0.7), latent heat (R&lt;sub&gt;2&lt;/sub&gt; &gt; 0.7), sensible heat (R&lt;sub&gt;2&lt;/sub&gt; &gt; 0.7), net radiation (R&lt;sub&gt;2&lt;/sub&gt; &gt; 0.8). ML methods predicted very well the across sites variability and the seasonal cycle (R&lt;sub&gt;2&lt;/sub&gt; &gt; 0.7) of the observed fluxes, while the eight days deviations from the mean seasonal cycle were not well predicted (R&lt;sub&gt;2&lt;/sub&gt; &lt; 0.5). Fluxes were better predicted at forested and temperate climate sites than at ones growing in extreme climates or less representated in training data (e.g. the tropics). The large ensemble of ML based models evaluated will be the basis of new global flux products.&lt;/p&gt;

Abstract:Classifying single image patches is important in many different applications, such as road detection or scene understanding. In this paper, we present convolutional patch networks, which are convolutional networks learned to distinguish different image patches and which can be used for pixel-wise labeling. We also show how to incorporate spatial information of the patch as an input to the network, which allows for learning spatial priors for certain categories jointly with an appearance model. In particular, we focus on road detection and urban scene understanding, two application areas where we are able to achieve state-of-the-art results on the KITTI as well as on the LabelMeFacade dataset. Furthermore, our paper offers a guideline for people working in the area and desperately wandering through all the painstaking details that render training CNs on image patches extremely difficult.

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