Launched in 2020, PLATEAU continues to grow as a platform for the participation of many local governments, private companies, and diverse researchers, engineers, and creators. PLATEAU is a platform for urban management, consolidation of urban functions, and sustainable urban development. The digital transformation of city planning based on PLATEAU will solve social and regional issues. By generating 3D urban models from existing data, we can see the possibilities of cities for the future. As the data is open, the sharing of wisdom and new actions will spread. By integrating and visualizing urban information, transformed into valuable information for the society of the future. The 3D urban model will be the foundation of the coming Society 5.0.

Environmental impact assessments and natural environment surveys associated with development projects require, among other tasks, surveys of the habitats of fish and other species. These fish surveys generally involve on-site capture surveys and visual identification. However, it has been observed that stress from such capture surveys has an adverse effect on the fish. Furthermore, such surveys require significant human effort, including the task of visual identification by technical experts. Therefore, as the need for an efficient automatic fish species recognition and counting system is needed. We built a system based on the YOLOv7 deep-learning object detector and a tracking algorithm that automatically distinguishes and counts multiple fish species in underwater video footage. After verifying the performance of the recognition model, we evaluated the system’s accuracy in counting each fish species. The evaluation showed an accuracy rate of 94.1%, demonstrating highly accurate automatic recognition even in a pond environment with low transparency.

One of the important purposes of regional revitalization activities by local governments is so-called “city promotion”, the improvement of external recognition, attractiveness, and reputation, and so on. On the other hand, reliable methods for quantitatively measuring and evaluating the effectiveness of those activities have not been sufficiently established, and this has become a challenging issue when evaluating such a “city promotion” project. This study attempts to measure the public relations effectiveness of community revi- talization efforts utilizing “water” as a community resource in the case of Ono City, Fukui Prefecture, which is famous for its spring water. We collected articles containing the keywords “Ono city” and “water” in the Fukui Shimbun, a local newspaper that occupies a large share in Fukui Prefecture, over the past 15 years, and analyzed the negativity and positivity associated through the sentiment analysis with the word “water” in these articles. As a result, a certain consistency was confirmed between city promotion efforts and changes in the level of negative-positive values, and it became clear that the approach of sentiment analysis using articles in local newspapers has a certain effectiveness as one of the methods to assess the results of city promotion policies.

In recent years, heavy rains have caused frequent sediment disasters in Japan, and it is essential to improve the reliability of sediment disaster risk estimation to reduce the damage caused by these disasters. In this study, we selected the input data in which only cells with high reliability for the slope failure occurrence were used to learn a sediment disaster risk estimation method. 60-minute total rainfall and soil rainfall index were inputted as triggers to a fully coupled deep neural network, and maximum slope angle, forest area, surface geology, and topographic classification were inputted as inherent factors. For training, validation, and threshold determination, we used the cases of sediment disasters caused by typhoons on September 6, 2007, September 21, 2011, and September 3, 2011. We conducted an estimation experiment and quantitative evaluation for the case of Typhoon Hagibis (No. 19) on October 12, 2019; we created a confusion matrix by counting each administrative district, calculated accuracy indices, and evaluated the results. The results showed that the estimation was performed without false negatives, and the accuracy was improved to 0.614 compared to the previous study (0.314).