# Understanding Natural Disaster Scenes from Mobile Images Using Deep Learning
## 6 Conclusions  
In this work, the authors explore the feasibility of a quantitative understanding of disaster scenes from mobile images. To deal with the significant complexity and uncertainties in disaster-scene images, this paper proposes adopting advanced deep-learning models to identify both hazard-type and damage-level embedded in images.  
The authors develop three deep-learning models for two disaster-scene understanding tasks: hazard-type identification and damage-level estimation. The following conclusions are resolved by assessing the performance of the two tasks based on quantitative performance measures. First, the performance of the models demonstrates that disaster scenes in mobile RS practice can be modeled, and predictive models with acceptable performance are feasible. Second, it is concluded that hazard-type can be identified with high accuracy due to the underlying abundant visual characteristics. On the other hand, relatively modest performance is observed when the models predict damage level. Empirical explanations are provided, including that the proposed damage-level scaling is agnostic to hazard types and possesses much inter- and intra-class variations. Last, it is observed that the Faster R-CNN architecture with a Resnet-50 CNN as the feature extract excels with the highest performance in this effort.  
With these conclusions, the authors expect that higher-performance predictive models for disaster-scene learning can be developed by enhancing data volume, veracity, and better-suited deep-learning architectures. The proposed concept of mobile imaging-based disaster-scene understanding and the developed frameworks in this paper can  
Figure 11: UAV image prediction: (**a**) hazard-type classification by \(M_{HT}(FRC,RN)\); (**b**) damage-level classification by \(M_{DL}(FRC,RN)\).  
facilitate the automation of imaging activities conducted by either professionals or the general public as smart and mobile devices become ubiquitous, enabling data-driven resilience of disaster response.  
Conceptualization, Z.C.; methodology, S.T. and Z.C.; software, S.T.; validation, S.T. and Z.C.; formal analysis, S.T.; investigation, S.T. and Z.C.; resources, Z.C.; data curation, Z.C.; writing--original draft preparation, S.T.; writing--review and editing, Z.C.; visualization, S.T.; supervision, Z.C.; project administration, Z.C.; funding acquisition, Z.C. Both authors have read and agreed to the published version of the manuscript.  
This material is partially funded under the National Science Foundation (NSF) under Award Number IIA-1355406 and the A.37 Disasters of the National Aeronautics and Space Administration (NASA) Applied Sciences Disaster Program. Any opinions, findings, and conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of NSF or NASA.  
Not applicable.  
Note applicable.  
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. The semantic disaster-scene data is publicly shared [37].  
The authors declare no conflict of interest.