Search

Understanding material surfaces and interfaces is vital in applications like catalysis or electronics. Ab initio simulations, combining energies from electronic structure with statistical mechanics, can, in principle, predict the structure of material surfaces as a function of thermodynamic variables. However, accurate energy simulations are prohibitive when coupled to the vast phase space that must be statistically sampled. Here, we present a bi-faceted computational loop to predict surface phase diagrams of multi-component materials that accelerates both the energy scoring and statistical sampling methods. Fast, scalable, and data-efficient machine learning interatomic potentials are trained on high-throughput density-functional theory calculations through closed-loop active learning. Markov-chain Monte Carlo sampling in the semi-grand canonical ensemble is enabled by using virtual surface sites. The predicted surfaces for GaN(0001) and SrTiO3(001) are in agreement with past work and suggest that the proposed strategy can model complex material surfaces and discover previously unreported surface terminations., 15 pages, 6 figures/tables

The diversity of tree species and the complexity of land use in cities create challenging issues for tree species classification. The combination of deep learning methods and RGB optical images obtained by unmanned aerial vehicles (UAVs) provides a new research direction for urban tree species classification. We proposed an RGB optical image dataset with 10 urban tree species, termed TCC10, which is a benchmark for tree canopy classification (TCC). TCC10 dataset contains two types of data: tree canopy images with simple backgrounds and those with complex backgrounds. The objective was to examine the possibility of using deep learning methods (AlexNet, VGG-16, and ResNet-50) for individual tree species classification. The results of convolutional neural networks (CNNs) were compared with those of K-nearest neighbor (KNN) and BP neural network. Our results demonstrated: (1) ResNet-50 achieved an overall accuracy (OA) of 92.6% and a kappa coefficient of 0.91 for tree species classification on TCC10 and outperformed AlexNet and VGG-16. (2) The classification accuracy of KNN and BP neural network was less than 70%, while the accuracy of CNNs was relatively higher. (3) The classification accuracy of tree canopy images with complex backgrounds was lower than that for images with simple backgrounds. For the deciduous tree species in TCC10, the classification accuracy of ResNet-50 was higher in summer than that in autumn. Therefore, the deep learning is effective for urban tree species classification using RGB optical images.

Individual tree crown detection and morphological parameter estimation can be used to quantify the social, ecological, and landscape value of urban trees, which play increasingly important roles in densely built cities. In this study, a novel architecture based on deep learning was developed to automatically detect tree crowns and estimate crown sizes and tree heights from a set of red-green-blue (RGB) images. The feasibility of the architecture was verified based on high-resolution unmanned aerial vehicle (UAV) images using a neural network called FPN-Faster R-CNN, which is a unified network combining a feature pyramid network (FPN) and a faster region-based convolutional neural network (Faster R-CNN). Among more than 400 tree crowns, including 213 crowns of Ginkgo biloba, in 7 complex test scenes, 174 ginkgo tree crowns were correctly identified, yielding a recall level of 0.82. The precision and F -score were 0.96 and 0.88, respectively. The mean absolute error (MAE) and mean absolute percentage error (MAPE) of crown width estimation were 0.37 m and 8.71%, respectively. The MAE and MAPE of tree height estimation were 0.68 m and 7.33%, respectively. The results showed that the architecture is practical and can be applied to many complex urban scenes to meet the needs of urban green space inventory management.

Trees are an integral part of the forestry ecosystem. In forestry work, the precise acquisition of tree morphological parameters and attributes is affected by complex illumination and tree morphology. In order to minimize a series of inestimable problems, such as yield reduction, ecological damage, and destruction, caused by inaccurate acquisition of tree location information, this paper proposes a ground tree detection method GMOSTNet. Based on the four types of tree species in the GMOST dataset and Faster R-CNN, it extracted the features of the trees, generate candidate regions, classification, and other operations. By reducing the influence of illumination and occlusion factors during experimentation, more detailed information of the input image was obtained. Meanwhile, regarding false detections caused by inappropriate approximations, the deviation and proximity of the proposal were adjusted. The experimental results showed that the AP value of the four tree species is improved after using GMOSTNet, and the overall accuracy increases from the original 87.25% to 93.25%.

The diversity of features in urban forest poses challenges to the task of delineation individual tree crowns. Multispectral image helps to improve the accuracy of individual tree crown delineation. It is necessary to reduce the dimensionality of multispectral images, but which dimensionality reduction method is suitable for the individual tree crown task based on deep learning still needs further research. In this study, four dimensionality reduction methods (principal component analysis, independent component analysis, optimum index factor, standard false color composite) were used to reduce the dimensionality of multispectral images. The images after dimensionality reduction were made as dataset for network training. Two instance segmentation networks (BlendMask, Mask R-CNN) were used to delineate the ginkgo tree crowns of UAV multispectral images after dimensionality reduction in urban. The effect of dimensionality reduction methods on two networks was evaluated in detail. The result of experiments presented that the standard false color composite method obtained the best value with 60.0% in average precision, 95.3% in average precision (Intersection over Union = 0.5) and 70.8% in average recall. The feature extraction methods (principal component analysis, independent component analysis) showed a good performance in the simple plot, but failed in the dense plot. The band selection methods (optimum index factor, standard false color composite) were more stable than the feature extraction methods in both plots. This article provides an important reference for related researchers on the choice of dimensionality reduction methods.