# Understanding Natural Disaster Scenes from Mobile Images Using Deep Learning
## 2 Research Problems and Challenges  
Disaster scenes from extreme events, such as hurricanes, floods, earthquakes, tsunamis, and tornadoes, are considerably complex. From the perspective of naked eyes, a disaster scene includes countless visual patterns related to built objects, natural hazards, landscapes, human activities, and many others. The semantic attributes for labeling patterns relevant to this effort are named _hazard-type_ and _damage-level_. The rationale for this proposition is justified as follows by examining the practice of professional reconnaissance activities. It is a cognitive process when professionals in a disaster field conduct digital recording via cameras or smart apps, e.g., Fulcrum [22]. To the trained eyes of professional engineers, their attention can be quickly paid to the visual patterns of interest, the built objects, the apparent damage features (e.g., cracking or debris), and other clues that are relevant to damage due to the extraordinary intelligence of human beings and their professional training. For example, a post-tsunami image often contains inundation marks or water-related textures, whereas the post-earthquake images usually show conspicuous cracking in buildings or cluttered debris. For tornado scenes, the damaging effects usually lie on the roof or the upper area of buildings, showing peeled surface materials due to wind blowing and shearing. In sum, professionals often act as a detective while conducting a forensic engineering process. In this process, they record the consequential evidence of damage in built objects using digital images. They further look for cues that cause the damage, namely the causal evidence of hazardous factors, which may co-exist in the same image of damaged objects or are recorded in different images. Second, this cognitive process continues in written reports by professionals, where the images are often showcased with captions or descriptions. Domain knowledge is more involved in this process, wherein the engineers tend to use a necessary number of images to analyze the common evidence of hazards and damage in images, then remark the intensity of the hazard and the degree of damage rationally, and even further, infer the underlying contributing factors, such as structural materials and geological conditions. Indeed, this has been called a _learning from disaster_ in engineering communities as demonstrated in many disaster reconnaissance reports (e.g., [23]). This cognitive understanding and learning process rarely occurs when the crowd conducts it as they lack domain knowledge. Also, even it is conducted by professionals, they may seek to excessively record disaster scenes yet without describing and reporting all images. As mentioned earlier, digital archives and social networks to this date have stored a colossal volume of images recording extreme events in recent years, which are not exploited or analyzed in the foreseeable future. This accumulation will inevitably be explosive with the advent of ubiquitous use of personal RT devices, for example, body-mounted or flying micro-UAV cameras. In this paper, inspired by the practical cognitive process of disaster-scene understanding, the authors argue an identifiable causality pair in a disaster-scene image, the hazard applied to and the damage sustained by built objects in images. This identifiable causality, more specifically termed _disaster-scene mechanics_, gives rise to the fundamental research question in this effort: does a computer-vision-based identification process exist that can process and identify hazard and damage related attributes in a disaster-scene image? To accommodate a computer-vision understanding process, the attributes are reduced to be categorical. As such, two identification tasks are defined in this paper:  
1. Given a disaster-scene image, one essential task is to recognize the contextual and causal property embedded in the image, namely the _Hazard-Type_. The ensuing identification is to estimate the _damage-level_ for an object (e.g., a building).