Soil organic carbon is an essential element of environmental quality assessment because carbon dioxide is the main greenhouse gas causing global warming and climate change. Trees and plants acquire a lot of carbon in their lifetimes, and soil helps keep this portion of carbon from releasing to the environment. Thus, understanding the current carbon storage in the soil and the relationship with tree richness is important. The soil organic carbon datasets and corresponding tree information are available. However, the current visualizations for these datasets are either static visualizations that report initial findings to the public or visualizations that are too complicated to be useful for a broad audience. Therefore, this work proposes an interactive visualization solution for analyzing soil organic carbon values distribution and their relationship with tree heights and diameters at breast heights, called RaCAViz. The design of this interactive visualization is based on the analytical evaluation of visual and interaction idioms to tackle typical analysis tasks on this type of dataset. However, this paper also provides a specific use case to illustrate its usefulness in bringing different perspectives on soil organic carbon datasets to a broad audience.

Maintaining the roadway infrastructure is one of the essential factors in enabling a safe, economic, and sustainable transportation system. Manual roadway damage data collection is laborious and unsafe for humans to perform. This area is poised to benefit from the rapid advance and diffusion of artificial intelligence technologies. Specifically, deep learning advancements enable the detection of road damages automatically from the collected road images. This work proposes to collect and label road damage data using Google Street View and use YOLOv7 (You Only Look Once version 7) together with coordinate attention and related accuracy fine-tuning techniques such as label smoothing and ensemble method to train deep learning models for automatic road damage detection and classification. The proposed approaches are applied to the Crowdsensing-based Road Damage Detection Challenge (CRDDC2022), IEEE BigData 2022. The results show that the data collection from Google Street View is efficient, and the proposed deep learning approach results in F1 scores of 81.7% on the road damage data collected from the United States using Google Street View and 74.1% on all test images of this dataset. With these results, we received rank 2 (silver prize) as a data contributor and rank 3 (bronze prize) as the predictive model in this competition among 54 leaders (private companies and academic institutions) in this area.