Smart Navigation App that steers you clear during disasters
Our application plays an important role in disaster management, relief and recovery.
It can be used for the following purposes :
- It can be used by Motorists to find damage road and take a safe route which is not damaged, thus it saves lives of the motorists.
- It can be used by Emergency responders to efficiently operate their disaster relief operations by avoiding the damaged roads and reach people in need quickly.
- It can be used by Government Officials to reconstruct and repair damaged roads thus helping in disaster recovery.
The solution we propose is feasible and efficient in the following ways :
- The application depends only on satellite images which is readily available during disasters
- The detection can be updated in real time using the post disaster images taken over time
The detection is composed of the following four phases
- Data Collection
- Training an Deep Neural Network for Road Segmentation
- Generating Road Segements from Pre and Post Disaster Satellite Images
- Find the difference between the pre and post disaster road segments to detect damaged Road
The dataset which we have used is the Massachusetts Roads Dataset https://www.cs.toronto.edu/~vmnih/data/.
It consists of input images and target Maps
- Input Images : It consists of high resolution satellite images
- Target Maps : The corresponding maps for the target images The following files are used for data collection :
- download.py : to scrap the data from https://www.cs.toronto.edu/~vmnih/data/
- createDataset.py : to create a trainDataset.csv file from input images and target maps
The features consists of pixels of input image and label consists of 1 or 0 indicating whether the pixel belongs to road or not. This can be calculated from the target map as follows:
If the corressponding pixel for the input satellite image pixel in the target map value is (255,255,255)
then
it belong to road
else
it does not belong to a road
The Deep Neural Network DNNClassifier from tensorflow is used for performing the binary classification. The model's accuracy is 82.5%.
- train.ipynb : Trains the Deep Neural Network.
- Model : The model is saved in the 'model' folder.
The pre and post disaster satellite images were taken from Digital Globe Open Data program. classify.ipynb : It takes in the trained model and segments the roads from pre and post disaster satellite images
damageRoadDetector.ipynb : The two road segments are compared and the difference between the roads is detected as damaged road.
