Earth Classification API

Basic Overview

A Python and Flask based REST API that serves a Keras/TensorFlow Convolutional Neural Network (CNN) model
trained to classify satellite image tiles into 17 different possible labels.
This API currently interfaces with a React and Leaflet front-end.

How It Works

When the user clicks on an area selected on the map, a POST request, containing the center latitude/longitude coordinate
for the area selected, is sent to the REST API. The API then searches a PostgreSQL database for the file location of a satellite image tile
containing the selected area. This image is then processed, in real time, through a Keras/TensorFlow ResNet50
model. This model makes a multilabel classification over 17 different labels returning a score between 0 and 1 for each label.
The resultant scores are filtered based on a cutoff value, and then returned as JSON to the front-end.

Data

The model was trained using the public dataset
from Planet that was part of their Kaggle competition in 2017.
This dataset consisted of ~42,000 image tiles of the amazon rainforest, all labeled.
The main labels that appear in the current implementation are defined as the following:

There are many more labels which can be found here.

Model Choice

A ResNet architecture was chosen for the CNN due to it’s
fast inference time, good accuracy and smaller model size. See this paper
for a comparison on all of these traits for the most common CNN architectures.