Urban green spaces such as parks and gardens are indispensable in both virtual and real-world environments. Therefore, planning such spaces is highly valuable. While scene synthesis literature has limited interest in this topic, many existing parametric design and procedural content generation approaches can be adapted to generate urban green spaces. However, these approaches heavily rely on manual work or are prone to producing monotonously repeated objects. This paper presents a framework that can automatically plan urban green spaces. Tailored to urban green space design, our framework comprises three steps: road system generation, region type planning, and model placement. First, it constructs undirected graphs to generate a sound road system for an empty site and divides the space into separate regions. Then it applies a genetic algorithm to plan suitable surface and vegetation for every region. Finally, it places landscape models based on various patterns and adds embellishments to complete an appealing urban green space. Our framework enables the automatic production of urban green spaces. Through extensive experiments, we demonstrate that the generated results are plausible and reasonable.

Abstract Considering the problem of missing fields in the criminal case system, this article proposes a deep learning algorithm to extract the features of the case description and fill in the missing value. Due to Chinese expressions and characteristics of criminal cases, we make both character vectors and word vectors to present text embedding. Character vectors are from bert model. Word vector is trained by long short‐term memory model with attention. The experiment uses 13,890 data totally. This work is an extension of our short conference proceeding paper. The results show that the combination of characters and words can effectively improve the accuracy of the conference paper by 9%. This is the first time to cascade the character and word dimensions on the criminal case information preprocess and it can provide higher quality data especially for the crime data mining.

In this paper, we apply the deep learning algorithm to preprocess the criminal case information. By extracting the characteristics of the brief case field, the missing content of other required field is plugged through model training. The experimental results show that the precision rate of CNN-LSTM model is 3% higher than that of LSTM-CNN model in text classification. After the Bert model is integrated, the precision rate, recall rate, and F value are all improved by 10%. To the best of our knowledge, this is the first time to use Bert model in preprocessing criminal case information.