In recent years, the global adoption of electric vehicles (EVs) has surged, prompting a corresponding rise in the installation of charging stations. This proliferation has underscored the importance of expediting the deployment of charging infrastructure. Both academia and industry have thus devoted to addressing the charging station location problem (CSLP) to streamline this process. However, prevailing algorithms addressing CSLP are hampered by restrictive assumptions and computational overhead, leading to a dearth of comprehensive evaluations in the spatiotemporal dimensions. Consequently, their practical viability is restricted. Moreover, the placement of charging stations exerts a significant impact on both the road network and the power grid, which necessitates the evaluation of the potential post-deployment impacts on these interconnected networks holistically. In this study, we propose CSLens, a visual analytics system designed to inform charging station deployment decisions through the lens of coupled transportation and power networks. CSLens offers multiple visualizations and interactive features, empowering users to delve into the existing charging station layout, explore alternative deployment solutions, and assess the ensuring impact. To validate the efficacy of CSLens, we conducted two case studies and engaged in interviews with domain experts. Through these efforts, we substantiated the usability and practical utility of CSLens in enhancing the decision-making process surrounding charging station deployment. Our findings underscore CSLens's potential to serve as a valuable asset in navigating the complexities of charging infrastructure planning.

The issue of traffic congestion poses a significant obstacle to the development of global cities. One promising solution to tackle this problem is intelligent traffic signal control (TSC). Recently, TSC strategies leveraging reinforcement learning (RL) have garnered attention among researchers. However, the evaluation of these models has primarily relied on fixed metrics like reward and queue length. This limited evaluation approach provides only a narrow view of the model's decision-making process, impeding its practical implementation. Moreover, effective TSC necessitates coordinated actions across multiple intersections. Existing visual analysis solutions fall short when applied in multi-agent settings. In this study, we delve into the challenge of interpretability in multi-agent reinforcement learning (MARL), particularly within the context of TSC. We propose MARLens, a visual analytics system tailored to understand MARL-based TSC. Our system serves as a versatile platform for both RL and TSC researchers. It empowers them to explore the model's features from various perspectives, revealing its decision-making processes and shedding light on interactions among different agents. To facilitate quick identification of critical states, we have devised multiple visualization views, complemented by a traffic simulation module that allows users to replay specific training scenarios. To validate the utility of our proposed system, we present three comprehensive case studies, incorporate insights from domain experts through interviews, and conduct a user study. These collective efforts underscore the feasibility and effectiveness of MARLens in enhancing our understanding of MARL-based TSC systems and pave the way for more informed and efficient traffic management strategies.

The rapid advancement of autonomous driving technology has reshaped the automotive industry, highlighting the need for diverse and high-quality image data. Existing image datasets for training and improving autonomous driving technologies lack rare scenarios like extreme weather, limiting the effectiveness and reliability of autonomous driving technologies. One possible way of expanding the dataset coverage is to augment the existing dataset with artificial ones, which, however, still suffers from various challenges like limited controllability and unclear corner case boundaries. To address these challenges, we design and develop an interactive visual analysis system, HuGe , to achieve efficient and semi-automatic controllable image generation. HuGe incorporates weather transformation models and a novel semi-automatic knowledge-based controllable object insertion method which leverages the controllability of convex optimization and the variability of diffusion models. We formulate the design requirements, propose an effective framework, and design four coordinated views to support controllable image generation, multidimensional dataset analysis, and evaluation of the generated samples. Two case studies, a metric-based evaluation and interviews with domain experts demonstrate the practicality and effectiveness of HuGe in controllable image generation for autonomous driving.

Quantifying woody vegetation cover is essential for understanding vegetation evolution and formulating land use management policies. However, most attention has focused on the retrieval of forest attributes, and it is imperative to enhance the capability to monitor the cover of woody vegetation. This study proposed an adaptive stacking regressor with a self-iterative optimization module to improve the quantification of the fractional woody cover. The adaptive stacking model was trained using synthetic mixing techniques and the 2021 Landsat composite to estimate the proportion of woody cover in Shaanxi Province, China. Results show that: 1) the proposed model exhibits a stable relationship ($R^{2}$= 0.92 and RMSE = 0.1020) between very high-resolution (VHR)-derived reference data and the Landsat-based wood cover fraction estimates; 2) the proposed model outperforms other classifiers in heterogeneous region mapping of woody vegetation, followed by random forest (RF) ($R^{2}$= 0.82 and RMSE = 0.1492), gradient-boosting decision tree (GBDT) ($R^{2}$= 0.79 and RMSE = 0.2550), and classification and regression tree (CART) ($R^{2}$= 0.69 and RMSE = 0.2042) models; and 3) uncertainty analysis based on the Monte Carlo method revealed that the adaptive stacking model improved the overestimations of low cover fractions and underestimations of high cover fractions. The adaptive stacking model is expected to advance the systematic monitoring of biophysical parameters of the land surface.

Autonomous driving has become a prevalent research topic in recent years, arousing the attention of many academic universities and commercial companies. As human drivers rely on visual information to discern road conditions and make driving decisions, autonomous driving calls for vision systems such as vehicle detection models. These vision models require a large amount of labeled data while collecting and annotating the real traffic data are time‐consuming and costly. Therefore, we present a novel vehicle detection framework based on the parallel vision to tackle the above issue, using the specially designed virtual data to help train the vehicle detection model. We also propose a method to construct large‐scale artificial scenes and generate the virtual data for the vision‐based autonomous driving schemes. Experimental results verify the effectiveness of our proposed framework, demonstrating that the combination of virtual and real data has better performance for training the vehicle detection model than the only use of real data.