Predicting Category 5 Hurricane Damage to Buildings

Requirements

Python 3.9+
conda

Getting started

The main source codes are in the src/data_loading, with scripts to download data from DigitalGlobe.

Setup:

In this directory, create a virtual environment by running:
```
make create_environment
```
This should give you everything you need in an virtual environment which can then be activated by:

conda activate ./env

Workflow:

Go to DigitalGlobe https://www.maxar.com/open-data
Select an event of interest, for example hurricane irma: https://www.maxar.com/open-data/hurricane-irma
Select "File List" at the bottom of the page. Copy and paste the file to data/raw/digital-globe-file-list in the format of "{hurricane-name}_file_list.txt" (There are already two such file lists available, which are irma_file_list.txt, test_file_list.txt)
For start, run the following commands in the terminal: python src/data_loading/patch_utils.py. There should be a prompt asking you to input a name for the hurricane. Press Enter straight away and the program should use the default testing data (which is smaller in size).

Or you can type a hurricane name like irma or test or test2.

The testing links can be found in data\processed\digital-globe-file-lists-tidied

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make init` or `make lint-requirements`
├── README.md          <- The top-level README for developers using this project.
|
├── data               <- Directory containing test data, and where new data should be placed
├── requirements       <- Directory containing the requirement files.
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data_loading   <- Scripts to download or generate data
│   │
│   ├── preprocessing  <- Scripts to turn raw data into clean data and features for modeling
|   |
│   ├── models         <- Scripts to train models and then use trained models to make
│   │                     predictions
│   │
│   └── tests          <- Scripts for unit tests of your functions
│
└── setup.cfg          <- setup configuration file for linting rules

Code formatting

To automatically format your code, make sure you have black installed (pip install black) and call black . from within the project directory.

Project template created by the Cambridge AI4ER Cookiecutter.

SkirOwen / predicting-cat-5-damage-to-buildings