# Understanding Natural Disaster Scenes from Mobile Images Using Deep Learning
## 5 Discussion
### Suggestions for Future Research  
Tornado-scene images in this work should be augmented to include more complexity. It is noticed that when predicting the tornado scenes, very high accuracy is achieved (with an \(AA\) up to 0.973 and an \(F_{1}\) score of 0.970; Table 2). The prediction accuracy is high on earthquake-scene prediction (\(AA\) = 0.873; \(F_{1}\) score = 0.994); then the least on tsunami-scene (\(AA\) = 0.867; \(F_{1}\) score = 0.849). This ranked trend in prediction accuracy essentially coincides with the scene complexity, from low to high: Tornado-wind, Tsunami-wave, and Earthquake-shaking scenes. Essentially, although the disaster-scene database created in this work contains images for events around the world, they are still limited in terms of diversity. Specifically, the tornado-scene images came from a single event and were taken within residential areas from one town (Moore, Oklahoma). Regardless of the relative complexity, nearly all buildings in images are residences captured from their front view. More diversity is in the earthquake scenes from urban settings of two quite different countries, Haiti and New Zealand. Similarly, the tsunami images came from two countries as well, coastal towns in Japan and Indonesia. These distinctions in imagery-scene complexity in terms of their sources and characteristics explain the observed performance. It is suggested that a different source for tornado-scene images may be included to augment the scene complexity. More flexible bounding-box-based annotations may be designed for very complex disaster-scene images. When the tsunami-wave scene is classified, relatively lower performance is observed with all the three models used. The authors speculate that by annotating bounding boxes and focusing on building objects, the tsunami-specific visual cues, particularly flood-borne debris, tend to be missed by the bounding boxes. The authors argue that this can reduce prediction performance about the tsunami scenes. As observed in Figures 6(d) and 6(d), bounding boxes are predicted differently from the human annotation. This is inevitably due to arbitrary and subjective variations in a subjective human-based process. The models learn the most plausible bounding box statistically in this tsunami-scene image. For damage-level prediction, however, the model chooses the bounding box with the damage-level of the maximum score value, and a much different box is predicted yet with a correct prediction (relative to the mislabeled label by a human analyst). This implies the disadvantage of the proposed simplified annotation process for the human analyst in this work. It is suggested that for very complex disaster scenes, such as hydraulic hazards induced disasters (e.g., flooding, tsunami, and storm surges), multiple and different bounding boxes can be used for characterizing the hazard-type and building damage. This treatment is left for future research. Hierarchical and fine-grained semantic labeling should be pursued in future research. Related to hazard types, a hierarchical scheme may be designed based on event type; for example, for an earthquake-event scene, the hazard labels may include: shaking, land sliding, liquefaction, ground sinking; whereas related to disaster types, specific damage types may be classified according to element types and location; for example, for buildings, these may include structural beam, wall, or foundation damage and nonstructural-element damage. Nonetheless, this fine-grained labeling demands a much larger-scale database and a more sophisticated model. Data imbalance in disaster-scene images is intrinsic. First, it is due to the nature of hazards in their frequency; for instance, the returning period of a strong earthquake is much more extended than a windstorm. Second, after an intense event, crowdsourced images from the general public are more focused on severely damaged buildings due to physiological preferences.