# Understanding Natural Disaster Scenes from Mobile Images Using Deep Learning
## 5 Discussion  
### Justification of Accuracy  
First, to enlighten the discussion regarding the accuracy of image-based damage classification, the authors retrieve relevant accuracy results from the literature. It is noted that again there are no similar efforts that use mobile RS images. Ref. [43] reviewed many efforts, where categorical structural damage was classified using traditional RS images. First, the average or overall accuracy ranged from 70% to 90%, depending on the availability and quality of the data; higher rates were usually a result of possessing high-quality pre- and post-event data. Ref. [44] conducted damage classification using synthetic aperture radar (SAR) images before and after the 2011 Tohoku Earthquake and Tsunami in Japan. With a traditional machine-learning framework, they reported an average accuracy of 71.1% using the F-1 score. Ref. [45] proposed a deep-learning-based damage detection workflow using the same type of data over the tsunami-related damage as in [44]. They reported  
Figure 10: Performance of \(M_{DL}(FRC,RN)\) in terms of: (**a**) Recall-Precision Curve; (**b**) ROC Curve. a damage-level recognition accuracy of 74.8% over three damaging classes. Ref. [46] identified the significance of fusing the Digital Elevation Model (DEM) with SAR data for damage mapping. In terms of four levels of building damage for an Indonesia tsunami, they reported a higher overall accuracy (>90%) yet a low average accuracy (around 67%). Given such comparison, the authors of this paper argue that towards an image-based classification of structural damage for built objects, an OA measurement of 62.6%) as reported by the model \(M_{DL}(FRC,RN)\) is not surprisingly low. To further explain this, three vital differences between traditional RS images and mobile RS images are argued below, which render mobile RS-based damage detection more challenging. 1. Bitemporal GIS-ready RT images vs. non-structured mobile images. In traditional RT images, the bitemporal pairs are usually both ortho-rectified and co-registered; therefore, bitemporal pixels for the same objects may only subject to misalignment of a few pixels, which significantly constrains the degree of errors. In the case of mobile images, there are no bitemporal pairs, and the damage is opportunistically captured from an arbitrary perspective of a built object. This difference leads to overall much more uncertainties in mobile RS images towards damage interpretation. 2. Bounded vs. unbounded scene complexity. In traditional RT images, most multi-story building objects in the nadir or off-nadir views only show the roof level damage with minimal building elevation coverage. This implies that the structural characteristics are primarily in terms of lines, edges, and corners at the roofs of buildings, which are low-level visual features. When damage occurs, these low-level features are distorted or modified. In mobile images, however, the scene complexity is mounted dramatically, and the involved features are at a high-level, including parts of objects, adjacent objects, and potential relations between adjacent parts or objects. 3. Hazard specific vs. hazard-agnostic damage-level. In most RT-based damage-level classification, the hazard-type comes from a single event, and the damage for built objects is extracted from bitemporal images. In this work, treating the damage-level agnostic to hazard-type leads to significant intra-class variations, which are much less significant when using traditional RS images. Secondly, it is explainable that hazard-type prediction models perform better than the damage-level models in this work. For this performance disparity, three empirical reasons are speculated herein. First, visual clues that imply hazard-type in disaster-scene images are more abundant and distinct than those possibly exploitable for discriminating damage-level. Second, the damage-level semantically imply an increasing order of damage severity.