# Understanding Natural Disaster Scenes from Mobile Images Using Deep Learning
## 1 Introduction
Because of such geo-readiness, many application efforts concern _change detection_, which is to detect landscape differences at different times for a terrain of interest on the Earth surface. As such, besides basic photogrammetric processing methods, a wide range of digital change-detection methods are found [13; 14; 15; 16; 17]. Mobile RS images differ from traditional ones in that they are often opportunistically collected. Due to their low-altitude or ground-level capturing mode, numerous images at different perspective angles are necessary for characterizing a local scene. Therefore, the involved processing to obtain a full view of the object (e.g., via a 3-dimensional reconstruction process) is much more complex than processing geo-ready images. Second, although mobile RS images can be geo-tagged via geographical positioning services in smart devices, many social or crowdsourced images are often marked with questionable geo-tags or simply have not geo-tags [18; 19]. Last, traditional RS images captured at different times can give rise to a time-series coverage for a particular region, whereas mobile RS data lacks such temporal advantages. These differences imply that one cannot use the traditional photogrammetric processing and change-detection methods to deal with mobile RS images that are arranged spatially and temporally non-structural. From this point of view, the process of processing and understanding mobile RS images, including extraction, interpretation, and localization of objects in images, belong to the general _image understanding_ problem as defined in the computer or machine vision literature [20; 21]. By borrowing this concept, _image-based disaster-scene understanding_ is coined in this paper, which is defined as a computational process of extracting information and detecting features or objects from images relevant to interpret a disaster. As will be further elaborated, research gaps exist towards a more cognitive mobile RS-based disaster-scene understanding. This paper contributes to the knowledge by developing and testing a deep-learning framework to understand disaster scenes. In this framework, the proposed learning models identify two essential properties in a given disaster image (namely the hazard type and the disaster-induced damage level). Performance evaluation reveals the insights in understanding disaster scenes in mobile images, including that the general damage-level classification is more challenging than hazard-type recognition. To achieve the goal, the authors built a multi-hazard disaster database with images from multiple sources,which forms another contribution of this paper. In the following, the specific research problems and challenges are defined. The methodology framework is proposed, including data preparation, network architectures and adjustment for two deep-learning models, and transfer-learning-based training. Three strategically designed deep-learning models are evaluated in this paper, followed by a comprehensive discussion. Conclusions are then given based on the research findings in this paper.