# Analysis of remote sensing imagery for disaster assessment using deep learning: a case study of flooding event

## Abstract

This paper proposes a methodology that integrates deep learning and machine learning for automatically assessing damage with limited human input in hundreds of thousands of aerial images. The goal is to develop a system that can help automatically identifying damaged areas in massive amount of data. The main difficulty consists in damaged infrastructure looking very different from when undamaged, likely resulting in an incorrect classification because of their different appearance, and the fact that deep learning and machine learning training sets normally only include undamaged infrastructures. In the proposed method, a deep learning algorithm is firstly used to automatically extract the presence of critical infrastructure from imagery, such as bridges, roads, or houses. However, because damaged infrastructure looks very different from when undamaged, the set of features identified can contain errors. A small portion of the images are then manually labeled if they include damaged areas, or not. Multiple machine learning algorithms are used to learn attribute–value relationships on the labeled data to capture the characteristic features associated with damaged areas. Finally, the trained classifiers are combined to construct an ensemble max-voting classifier. The selected max-voting model is then applied to the remaining unlabeled data to automatically identify images including damaged infrastructure. Evaluation results (85.6% accuracy and 89.09% F1 score) demonstrated the effectiveness of combining deep learning and an ensemble max-voting classifier of multiple machine learning models to analyze aerial images for damage assessment.

## Keywords

Spatiotemporal data Image classification TensorFlow Machine learning Deep learning Damage assessment## Abbreviations

- ML
Machine learning

- DL
Deep learning

- CAP
Civilian Air Patrol

- AI
Artificial intelligence

- CNN
Convolutional neural network

- RNN
Recurrent neural network

- MLP
Multilayer perceptron

- SVM
Support vector machine

- RBF
Radial basis function

- DT
Decision tree

- NB
Naive Bayes

- KNN
k-Nearest neighbors

- RF
Random forest

- GB
Gradient boosting

- GBC
Gradient boosting classifier

- LR
Logistic regression

- LDA
Linear discriminant analysis

- NN
Neural networks

- USGS
United States Geological Survey

- USGS HDDS
USGS Hazards Data Distribution System

## Notes

### Acknowledgements

This work was partially supported by the Office of Naval Research (ONR) award no. N00014-16-1-2543 (PSU no. 171570) and by the NVIDIA Corporation. We acknowledge Dr. Davide Del Vento from NCAR CISL and Dr. Chuck Pavloski at the Penn State Institute for CyberScience (ICS). The authors wish to thank Elena Sava for useful discussions and for providing the data and initial results relative to the Texas flood event.

### Compliance with ethical standards

### Conflict of interest

The authors declare that they have no conflict of interest.

### Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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