Code combining preprocessing, data collection, training and inference to generate automated disaster reports.
- tar_main.py - File that consolidates relevant functions to produce a report
- date2template* - Files that do different collectiong/processing of USGIS data to be added to the briefings
- classifiers.py - Calls classifiers (regression, SVN, GAN, CNN) and runs a majority vote to determine the final classification for sentences according to 4 categories (buildings, infrastructure, resilience, other)
- resilience_curve.py - Generates resilience curves, and calculates t0 and t1 (to calculate recovery time for disaster)
- config.ini - Set of parameters to control briefing generation
- data - Folder containing log of earthquakes, tweets and news articles
Generating a report
To generate a report, run
python3 tar_main.py.
This would iterate through earthquakes listed in the earthquake log and output a report to the "reports" directory.
Generating a resilience curve
To do this, call the generateResilience function in resilience_curve.py. It takes the following parameters -
- ruptureTime - Reference time to when the earthquake happened (e.g. 2021-02-24 02:05:59)
- twitterFile - CSV with tweets for earthquake
- keywords - keywords to filter tweets by
An example call would be generateResilience("2021-02-24 02:05:59", "data/tweets/ArgentinaTweets.csv", ["electricity", "lights"])