Road scene model construction is an important aspect of intelligent transportation system research. This paper proposes an intelligent framework that can automatically construct road scene models from image sequences. The road and foreground regions are detected at superpixel level via a new kind of random walk algorithm. The seeds for different regions are initialized by trapezoids that are propagated from adjacent frames using optical flow information. The superpixel level region detection is implemented by the random walk algorithm, which is then refined by a fast two-cycle level set method. After this, scene stages can be specified according to a graph model of traffic elements. These then form the basis of 3D road scene models. Each technical component of the framework was evaluated and the results confirmed the effectiveness of the proposed approach.

Rice blast is one of the three major rice diseases recognized in the world, which greatly harms the quality and the yield of rice. In order to distinguish rice leaf blast disease from nutrient deficiency and diagnose early the leaf blast disease, this study was based on the natural incidence of rice and field experiments, hyperspectral imagers were used to obtain the imaging spectrum of health, nitrogen deficiency, mild disease and severe disease. Spectra of 4 types of leaves were extracted, and three kinds of different data pretreatment methods were used, and the SPA feature extraction method was combined with the support vector machine(SVM) and the linear discriminant analysis(LDA) to construct the rice leaf blast identification model. The experimental results show that, after preprocessing by the Savitzky-Golay method, 9 characteristic wavelengths were extracted by SPA for modeling, and the models had the best recognition effect. The prediction accuracy of the SG-SPA-SVM model and the SG-SPA-LDA model were both 98.7%.

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In recent years, data generated in the field of transportation has begun to explode. Individual continuous tracking data, such as mobile phone data, IC smart card data, taxi GPS data, bus GPS data and bicycle sharing order data, also known as "spatio-temporal big data" or "Track &Trace data" (Harrison et al., 2020), has great potential for applications in datadriven transportation research. These spatio-temporal big data typically require three aspects of information (Zhang et al., 2021): Who? When? Where? They are characterized by high data quality, large collection scope, and fine-grained spatio-temporal information, which can fully capture the daily activities of individuals and their travel behavior in the city in both temporal and spatial dimensions. The emergence of these data provides new ways and opportunities for potential transportation demand analysis and travel mechanism understanding in supporting urban transportation planning and management (Chen et al., 2021;Zhang et al., 2020). However, processing with these multi-source spatio-temporal big data usually requires a series of similar processing procedure (e.g., data quality assessment, data preprocessing, data cleaning, data gridding, data aggregation, and data visualization). There is an urgent need for a one-size-fits-all tool that can adapt to the various processing demands of different transportation data in this field.