A Tool for Bridge Detection in Major Infrastructure Works Using Satellite Images

The identification of bridges in major infrastructure works is crucial to provide information about the status of these constructions and support possible decision-making processes. Typically, this identification is performed by human agents that must detect the bridges into large-scale datasets, analyzing image by image, a time-consuming task. In this paper, we propose a novel tool to perform bridge detection and identification in large-scale remote sensing datasets. This tool implements a deep learning-based algorithm, the Faster R-CNN (Regions with CNN features), a technique that is the current state-of-the-art for many object detection and identification applications. Since deep training usually requires a lot of data, we also created a bridge image dataset, composed of remote sensing images from around the globe. The proposed tool was encapsulated into an ArcGIS plugin in order to facilitate its use by non-programmer users.