Add explanation of the script

Author: drewbo

README.md

@@ -1,7 +1,7 @@
 # Label Maker
 ## Data Preparation for Satellite Machine Learning
 
-The tool downloads [OpenStreetMap QA Tile]((https://osmlab.github.io/osm-qa-tiles/)) information and satellite imagery tiles and saves them as an [`.npz` file](https://docs.scipy.org/doc/numpy/reference/generated/numpy.savez.html) for use in Machine Learning training.
+The tool downloads [OpenStreetMap QA Tile]((https://osmlab.github.io/osm-qa-tiles/)) information and satellite imagery tiles and saves them as an [`.npz` file](https://docs.scipy.org/doc/numpy/reference/generated/numpy.savez.html) for use in machine learning training.
 
 ![example classification image overlaid over satellite imagery](examples/images/classification.png)
 _satellite imagery from [Mapbox](https://www.mapbox.com/) and [Digital Globe](https://www.digitalglobe.com/)_

examples/README.md

@@ -5,6 +5,7 @@
 - [Creating a Neural Network to Find Populated Areas in Tanzania](walkthrough-classification-aws.md): Build a classifier using Keras on AWS
 - [Creating a building classifier in Vietnam using MXNet and SageMaker](walkthrough-classification-mxnet-sagemaker.md): Build a classifier on AWS SageMaker
 - [A building detector with TensorFlow API](walkthrough-tensorflow-object-detection.md): Use the TensorFlow Object Detection API for detecting buildings in Mexico City.
+- [Preparing data for `skynet-train`](skynet-train-data-prep.md)
 
 ## Example Nets
 

examples/skynet-train-data-prep.md

@@ -0,0 +1,19 @@
+# Using `label-maker` with `skynet-train`
+
+## Background
+
+[`skynet-data`](https://github.com/developmentseed/skynet-data/) is a tool developed specifically to prepare data for [`skynet-train`]((https://github.com/developmentseed/skynet-train/)), an implementation of [SegNet](http://mi.eng.cam.ac.uk/projects/segnet/). `skynet-data` predates `label-maker` and prepares data in a very similar way: download OpenStreetMap data and satellite imagery tiles for use in Machine Learning training. Eventually, `skynet-data` will be deprecated as most of it's functionality can be replicated using `label-maker`.
+
+## Preparing data
+
+`skynet-train` requires a few separate files specific to [`caffe`](https://github.com/BVLC/caffe). To create these files, we've created a [utility script](utils/skynet.py) to help connect `label-maker` with [`skynet-train`](https://github.com/developmentseed/skynet-train/). First, prepare segmentation labels and images with `label-maker` by running `download`, `labels`, and `images` from the command line, following instructions from the [other examples](README.md) or the [README](../README.md). Then, in your data folder (the script uses relative paths), run:
+
+```bash
+python utils/segnet.py
+```
+
+This should create the files (`train.txt`, `val.txt`, and `label-stats.csv`) which are needed for running `skynet-train`
+
+## Training
+
+Now you can mount your data folder as shown in the [`skynet-train` instructions](https://github.com/developmentseed/skynet-train/#quick-start) and training should begin.