Automated semantic segmentation of bridge point cloud based on local descriptor and machine learning

In recent years, monitoring the health condition of existing bridges has become a common requirement. By providing an information management system, Bridge Information Model (BrIM) can highly improve the efficiency of health inspection and the reliability of condition evaluation. However, the current modeling processes still largely rely on manual work, where the cost outweighs the benefits. The main barrier lies in the challenging step of semantic segmentation of point clouds. Efforts have been made to identify and segment the structural components of bridges in existing research. But these methods are either dependent on manual data preprocessing or need big training dataset, which, however, has rendered them unpractical in real-world applications. This paper presents a combined local descriptor and machine learning based method to automatically detect structural components of bridges from point clouds. Based on the geometrical features of bridges, we design a multi-scale local descriptor, which is then used to train a deep classification neural network. In the end, a result refinement algorithm is adopted to optimize the segmentation results. Experiments on real-world reinforced concrete (RC) slab and beam-slab bridges show an average precision of 97.26%, recall of 98.00%, and intersection over union (IoU) of 95.38%, which significantly outperforms PointNet. This method has provided a potential solution to semantic segmentation of infrastructures by small sample learning and will contribute to the fulfillment of the automatic BrIM generation of typical highway bridges from the point cloud in the future

Data_Sheet_1_Internet search data showed increased interest in supplementary online education during the COVID-19 pandemic, with females showing a greater increase.pdf

The COVID-19 pandemic has led to tremendous disruptions in people’s everyday activities, including the pursuit of education. Internet search data may provide insights into potential audiences’ interest in online education. Using Internet search data, we examined the impact of COVID-19 on people’s interest in supplementary online education in the US over nine months (10/14/2019–07/19/2020). We found there was increased interest in supplementary online education after WHO announced COVID-19 as a pandemic, with a greater increase among females than males. We found that the increased interest in online education persisted after the stay-at-home orders were lifted; in addition, we identified concerns over unemployment as a key variable that significantly explained the variance in the interest in online education, even after controlling for COVID cases and deaths. Policymakers and online education platforms may take advantage of people’s, especially women’s increased interest in online education when designing policies or marketing mix.</p

Fine classification of crops based on an inductive transfer learning method with compact polarimetric SAR images

Compact polarimetric synthetic aperture radar (CP SAR) reduces fully polarimetric SAR system complexity and expands the imaging swath. Generally, fine classification of crop types relies on many labeled training samples. However, due to the temporal interval of crop phenology and ground environment variations over time, training samples from one dataset usually perform poorly for another. Therefore, in this study, transfer learning is introduced to crop classification to ensure classification accuracy by improving reusability of training samples. A stable and robust inductive transfer learning method, i.e. the Transfer Bagging-based Ensemble Learning (TBEL) algorithm, is proposed. The main idea is to select an adequate number of representative samples from unlabeled datasets to characterize each class in the target domain based on limited labeled samples and construct a classifier set to classify the target domain. This study investigates CP SAR data performance in transfer learning for crop classification. The proposed algorithm in the experimental study is compared with six typical methods (Subspace Alignment (SA), CORrelation ALignment (CORAL), Joint Distribution Adaptation (JDA), Balanced Distribution Adaptation (BDA), Transfer Bagging (TrBagg), and Bagging-based Ensemble Transfer Learning (BETL)). The experimental results show that the crop classification accuracy based on the TBEL algorithm is more stable, with an improved overall classification accuracy of 2–6%. Classifying the same rice harvest stage in the cross-year domain has the highest overall accuracy of 92.2%. Wheat fields in different scenes are also classified. Based on the TBEL algorithm, the overall classification accuracy improves by 1–10% compared with typical methods, with an accuracy of at least 87.6%. Furthermore, by testing the CP mode classification performance over various crops in transfer learning, we find that the circular CP mode performs better than the linear mode in most cases. This conclusion agrees with single-scene applications and was first verified in transfer learning.</p

The network structure among all participants, in which triangles represent small enterprises, circles represent commercial banks and diamonds represent micro-credit companies.

<p>The network structure among all participants, in which triangles represent small enterprises, circles represent commercial banks and diamonds represent micro-credit companies.</p

Payoffs of commercial bank No.0.

<p>Payoffs of commercial bank No.0.</p

The recognition rates of each classifier for face recognition on AR database with disguise occlusion.

<p>(a) The testing images with sunglasses from session 1; (b) The testing images with scarves from session 1; (c) The testing images with sunglasses from session 2; (d) The testing images with scarves from session 2.</p

Payoffs of small enterprise No.16.

<p>Payoffs of small enterprise No.16.</p

The strategy and corresponding payoff between small enterprises and micro-credit companies.

<p>X: small enterprises; Y2: micro-credit companies</p><p>The strategy and corresponding payoff between small enterprises and micro-credit companies.</p

The recognition rates of SRC, GSRC, CESR, RSC, RRC_L₂ and R-GRR under the occlusion percentage from 0 to 50.

<p>The recognition rates of SRC, GSRC, CESR, RSC, RRC_L₂ and R-GRR under the occlusion percentage from 0 to 50.</p

(a) The recognition rate of each method for handwritten numeral recognition on the CENPARMI database versus the variation of dimensions; (b) The recognition rate of each method corresponds to the number of classs training samples that varies from 100 to 500 with an inerval 100.

<p>The number in brakets of each method means the number of low dimentional trasforamation features.</p