Detecting Swimming Pools using Satellite Imagery and Deep Learning¶

• 🔬 Data Science
• 🥠 Deep Learning and Object Detection

Table of Contents

Prerequisites¶

• Please refer to the prerequisites section in our guide for more information. This sample demonstrates how to do export training data and model inference using ArcGIS Image Server. Alternatively, they can be done using ArcGIS Pro as well.
• If you have already exported training samples using ArcGIS Pro, you can jump straight to the training section. The saved model can also be imported into ArcGIS Pro directly.

Introduction and objective¶

Deep Learning has achieved great success with state of the art results, but taking it to the field and solving real-world problems is still a challenge. Integration of the latest research in AI with ArcGIS opens up a world of opportunities. This notebook demonstrates an end-to-end deep learning workflow in using ArcGIS API for Python. The workflow consists of three major steps: (1) extracting training data, (2) train a deep learning object detection model, (3) deploy the model for inference and create maps. To better illustrate this process, we choose detecting swmming pools in Redlands, CA using remote sensing imagery.

Part 1 - export training data for deep learning¶

Import ArcGIS API for Python and get connected to your GIS¶

Prepare data that will be used for training data export¶

To export training data, we need a labeled feature class that contains the bounding box for each object, and a raster layer that contains all the pixels and band information. In this swimming pool detection case, we have created feature class by hand labelling the bounding box of each swimming pool in Redlands using ArcGIS Pro and USA NAIP Imagery: Color Infrared as raster data.

Now let's retrieve the NAIP image layer.

Specify a folder name in raster store that will be used to store our training data¶

Make sure a raster store is ready on your raster analytics image server. This is where where the output subimages, also called chips, labels and metadata files are going to be stored.

Export training data using arcgis.learn¶

With the feature class and raster layer, we are now ready to export training data using the export_training_data() method in arcgis.learn module. In addtion to feature class, raster layer, and output folder, we also need to speficy a few other parameters such as tile_size (size of the image chips), strid_size (distance to move in the X when creating the next image chip), chip_format (TIFF, PNG, or JPEG), metadata format (how we are going to store those bounding boxes). More detail can be found here.

Depending on the size of your data, tile and stride size, and computing resources, this opertation can take 15mins~2hrs in our experiment. Also, do not re-run it if you already run it once unless you would like to update the setting.

Now let's get into the raster store and look at what has been generated and exported.

We can also create a image layer using one of this images and look at what it looks like. Note that a chip may or may not have a bounding box in it and one chip might have multiple boxes as well.

Part 2 - model training¶

If you've already done part 1, you should already have both the training chips and swimming pool labels. Please change the path to your own export training data folder that contains "images" and "labels" folder.

Visualize training data¶

To get a sense of what the training data looks like, arcgis.learn.show_batch() method randomly picks a few training chips and visualize them.