Recent technological advancements in geomatics and mobile sensing have led to various urban big data, such as Tencent Street View (TSV)photographs yet, the urban objects in the big data have hitherto been inadequately exploited. This work represents the first time-discrete model of lost person dynamics validated with data from real SAR incidents and has the potential to improve current methods for wilderness SAR. We validate these results with a leave-one-out analysis. We systematically simulate a range of possible behavior distributions and find a best-fit behavioral profile for a hiker with the International Search and Rescue Incident Database. The behavior random variable selects from a distribution of six known lost person reorientation strategies to simulate the agent’s trajectory. In this paper, we introduce an agent-based model of lost person behavior which allows agents to move on known landscapes with behavior defined as independent realizations of a random variable. To optimize the search process, mathematical models of lost person behavior with respect to landscape can be used in conjunction with current SAR practices. As time passes, the search area grows, survival rate decreases, and searchers are faced with an increasingly daunting task of searching large areas in a short amount of time. Thousands of people are reported lost in the wilderness in the United States every year and locating these missing individuals as rapidly as possible depends on coordinated search and rescue (SAR) operations. The analysis results showed that the improved DCNN model had excellent recognition speed and accuracy and could accurately recognize and classify the risk of a forest fire under natural light conditions, thereby providing a technical reference for preventing and tackling forest fires. When verifying the impact of different batch sizes and loss rates on verification accuracy, the loss rate of the DCN_Fire model of 0.5 and the batch size of 50 provided the optimal value for verification accuracy (0.983). The true positive rate was 7.41% and the false positive rate was 4.8%. The difficulty of using DCNN to monitor forest fire risk was solved, and the model’s detection accuracy was further improved. The traditional DCNN model was improved and the recognition speed and accuracy were compared and analyzed with the other three DCNN model algorithms with different architectures. ![]() Moreover, the original and enhanced image data sets were used to evaluate the impact of data enhancement on the model’s accuracy. The constructed 15-layer forest fire risk identification DCNN model named “DCN_Fire” could accurately identify core fire insurance areas. Second, principal component analysis (PCA) reconstruction technology was used in the appropriate subspace. First, the DCNN network model was trained in combination with transfer learning, and multiple pre-trained DCNN models were used to extract features from forest fire images. In this work, an improved dynamic convolutional neural network (DCNN) model to accurately identify the risk of a forest fire was established based on the traditional DCNN model.
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