The CCAI project is an interdisciplinary project aimed at creating images of accurate, vivid, and personalized outcomes of climate change. Our goal is to use cutting-edge machine learning techniques to produce images of how neighborhoods and houses will look like following the effects of global warming. By creating a more visceral understanding of the effects of climate change, we aim to strengthen public support for necessary actions and motivate people to make impactful decisions. As a prototype, we first focus on modeling flood consequences on homes.

For a more detailed motivation explanation, read through our 2 page executive summary.

This document has the following sections:

• How Does This Thing Work?
• Getting Started
• API Endpoints

There are also the following documents which may be useful depending on your objectives:

• If you're looking to build, run, extend, or test this codebase, you should check out the Contributor Guide.

This server is an API server written in Python using the Flask microframework. The server code is in ccai/ and the tests are in tests/. For more information on running the server, see the Contributor Guide.

For a thorough accounting of how to set up this repo and use the various developer tools that are setup, you can read the Contributor Guide. Alternatively, the following is a minimal set of commands that you can run to get up and running as quickly as possible:

Clone the repo:

mkdir -p ~/git
git clone https://github.com/cc-ai/floods-backend ~/git/floods-backend
cd ~/git/floods-backend

Install the large files (models) using Git LFS:

git lfs init
git lfs pull
git lfs checkout

Install the Python dependencies into your current Python environment (should be at least Python 3.7):

pip install -r requirements.txt

Run a development server:

make develop

The API server will now be available at http://127.0.0.1:5000. See the API Endpoints section for available endpoints.

Once you're running the webserver locally, there are a few API endpoints that are available.

To download an image of an address, you can use the /address/{address} endpoint. To download a picture of Mila, you could run the following curl command locally:

curl http://127.0.0.1:5000/address/6666%20St%20Urbain%20St%2C%20Montreal%2C%20QC%20H2S%203H1%2C%20Canada > mila.jpg
open mila.jpg

To download an unprocessed image as well as a flooded image and metadata, you can use the /flood/{model}/{address} endpoint. The model string must be a valid model that we have configured to flood images. At the time of this writing, the only supported model is munit. To download the content for Mila, you could run the following curl command locally:

curl localhost:5000/flood/MUNIT/6666%20St%20Urbain%20St%2C%20Montreal%2C%20QC%20H2S%203H1%2C%20Canada > images.json

This will return a response like:

{
    "original": "...",
    "flooded": "...",
    "metadata": {
        "monthly_average_precipitation": {
            "title": "Monthly Average Precipitation in 2050",
            "value": 9.5253
        },
        "relative_change_precipitation": {
            "title": "Relative Change in Precipitation by 2050",
            "value": 0.13491
        }
    }
}

Note that the "metadata" key may return arbitrary climate-related metadata about the image. The "original" and "flooded" keys will contain base64 encoded images.

There is a Prometheus compatible metrics endpoint at /metrics. A simple HTTP GET will return a plain text response with metrics in the standard Prometheus format:

curl http://127.0.0.1:5000/metrics