ARIO - Automated Reconnaissance Image Organizer

Deploy

Database

Install SQL Server and create a database on Ubuntu

Classification server

Install TensorFlow on Ubuntu

Install packages

$ sudo apt install libopencv-dev python3-opencv
$ pip3 install opencv-python h5py Flask tqdm pandas sklearn

Clone the project

$ git clone http://hpcg.purdue.edu/git/civil.git

Install the classifier SimpleClassifier/model_weights.h5

Run the server

$ python3 classifier.py

Web server

Install Node.js on Ubuntu

Install .NET Core SDK 2.0.3 on Ubuntu

Install libgdiplus

$ sudo apt install libgdiplus

Clone the project

$ git clone http://hpcg.purdue.edu/git/civil.git

Change the connection string in appsettings.json

Server=xxx.xxx.xxx.xxx;Database=Sismique;User Id=SA;Password=Passw0rd;

Change the address of the classification server in ReportController.cs

Publish the application on Ubuntu

$ npm install
$ dotnet publish -c Release -r ubuntu.16.04-x64
$ cd bin/Release/netcoreapp2.0/ubuntu.16.04-x64/publish
$ dotnet Sismique.dll

[OPTIONAL] Change the .NET Core RID

Host the application on Ubuntu

Add a basic authentication

Development configuration for nginx: /etc/nginx/sites-available/sismique

server {
    listen 80 default_server;
    listen [::]:80 default_server;

    server_name _;

    auth_basic           "ARIO";
    auth_basic_user_file /home/mgaillard/civil/.htpasswd;

    client_max_body_size 16M;

    location / {
        proxy_pass http://localhost:5000;
        proxy_http_version 1.1;
        proxy_set_header Upgrade $http_upgrade;
        proxy_set_header Connection keep-alive;
        proxy_set_header Host $http_host;
        proxy_cache_bypass $http_upgrade;
    }
}

# Activate the new website
$ cd /etc/nginx/sites-enabled
$ sudo unlink default
$ sudo ln -s /etc/nginx/sites-available/sismique .
# Check syntax
$ sudo nginx -t
# Reload
$ sudo nginx -s reload

Upgrade to a newer version

$ git clean -dfx
$ git stash
$ git pull
$ git stash pop
$ dotnet publish -c Release -r ubuntu.16.04-x64

NSF Award Link

https://www.nsf.gov/awardsearch/show-award?AWD_ID=1835473

Please cite the following publication when use this work

@inproceedings{Dyke2021ARIO,
  author    = {Dyke, Shirley J. and Liu, X. and Choi, J. and Yeum, C. M. and Park, J. and Midwinter, M. and Hacker, T.
               and Chu, Z. and Ramirez, J. and Poston, R. and Gaillard, M. and Benes, B. and Lenjani, A. and Zhang, X.},
  title     = {Learning from Earthquakes Using the Automatic Reconnaissance Image Organizer (ARIO)},
  booktitle = {Proceedings of the 17th World Conference on Earthquake Engineering},
  year      = {2021},
  month     = sep,
  url       = {https://par.nsf.gov/servlets/purl/10308795}
}

Dyke, S. J., Liu, X., Choi, J., Yeum, C. M., Park, J., Midwinter, M., Hacker, T., Chu, Z., Ramirez, J., Poston, R., Gaillard, M., Benes, B., Lenjani, A., & Zhang, X. (2021, September). Learning from Earthquakes Using the Automatic Reconnaissance Image Organizer (ARIO). In Proceeding of the 17th World Conference on Earthquake Engineering. https://par.nsf.gov/servlets/purl/10308795