Wildfire Detection in Video Images Using Deep Learning and HMM for Early Fire Notification System Conflagration is disaster that strongly influence environmental ecology and lives. In building, sensors are sufficient to support notification and evacuation. In contrast, the outdoor fire notification is supported via image fire detection. For wildfire notification and assistance in rescue, many researches extract fire characteristic from digital image to detect fire. However, the false alarm of detection system happens when it find the little flame can be extinguished itself with time. In case of false alarm often happens, the rescuer may neglect it and lose the first opportunity to rescue in mission. In this paper, we propose a system that combine deep learning and hidden markov model (HMM) in order to reduce frequency of false alarm. Our proposed system defines the status transformation of imagesframes. Afterwards, the relationship of status transition is employed to notification classes. As experimental result, the fire detection rate of all deep learning architectures can achieve more than 96%. And our proposed system can also reduce up to 88.54% false alarm rate to decrease the waste of emergency manpower effectively.

Prototyping a Social Media Flooding Photo Screening System Based on Deep Learning This article aims to implement a prototype screening system to identify floodingrelated photos from social media. These photos, associated with their geographic locations, can provide free, timely, and reliable visual information about flood events to the decisionmakers. This screening system, designed for application to social media images, includes several key modules: tweetimage downloading, flooding photo detection, and a WebGIS application for human verification. In this study, a training dataset of 4800 flooding photos was built based on an iterative method using a convolutional neural network (CNN) developed and trained to detect flooding photos. The system was designed in a way that the CNN can be retrained by a larger training dataset when more analystverified flooding photos are being added to the training set in an iterative manner. The total accuracy of flooding photo detection was 93% in a balanced test set, and the precision ranges from 46xe2x80x9363% in the highly imbalanced realtime tweets. The system is plugin enabled, permitting flexible changes to the classification module. Therefore, the system architecture and key components may be utilized in other types of disaster events, such as wildfires, earthquakes for the damageimpact assessment.