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.

Intelligent Object Recognition of Urban Water Bodies Based on Deep Learning for MultiSource and MultiTemporal High Spatial Resolution Remote Sensing Imagery High spatial resolution remote sensing image (HSRRSI) data provide rich texture, geometric structure, and spatial distribution information for surface water bodies. The rich detail information provides better representation of the internal components of each object category and better reflects the relationships between adjacent objects. In this context, recognition methods such as geographic objectbased image analysis (GEOBIA) have improved significantly. However, these methods focus mainly on bottomup classifications from visual features to semantic categories, but ignore topdown feedback which can optimize recognition results. In recent years, deep learning has been applied in the field of remote sensing measurements because of its powerful feature extraction ability. A special convolutional neural network (CNN) based region proposal generation and object detection integrated framework has greatly improved the performance of object detection for HSRRSI, which provides a new method for water body recognition based on remote sensing data. This study uses the excellent xe2x80x9cselflearning abilityxe2x80x9d of deep learning to construct a modified structure of the Mask RCNN method which integrates bottomup and topdown processes for water recognition. Compared with traditional methods, our method is completely datadriven without prior knowledge, and it can be regarded as a novel technical procedure for water body recognition in practical engineering application. Experimental results indicate that the method produces accurate recognition results for multisource and multitemporal water bodies, and can effectively avoid confusion with shadows and other ground features.