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https://github.com/developmentseed/skynet-data

[DEPRECATED] Data pipeline for machine learning with OpenStreetMap
https://github.com/developmentseed/skynet-data

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[DEPRECATED] Data pipeline for machine learning with OpenStreetMap

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# skynet-data



A pipeline to simplify building a set of training data for aerial-imagery- and

OpenStreetMap- based machine learning.  The idea is to use [OSM QA

Tiles](https://osmlab.github.io/osm-qa-tiles/) to generate "ground truth"

images where each color represents some category derived from OSM features.

Being map tiles, it's then pretty easy to match these up with the desired input

imagery.



 - OSM QA tile data

   [copyright OpenStreetMap contributors](http://www.openstreetmap.org/copyright)

   and licensed under

   [ODbL](http://opendatacommons.org/licenses/odbl/)

 - Mapbox Satellite data can be

   [traced for noncommercial purposes](https://www.mapbox.com/tos/#[YmtMIywt]).

   

This repository is no longer under active development. We recommend using [Label Maker](https://github.com/developmentseed/label-maker) to prepare data instead. That repo contains [utility scripts](https://github.com/developmentseed/label-maker/blob/master/examples/skynet-train-data-prep.md) which can be used to replicate the workflow needed to prepare data for [skynet-train](https://github.com/developmentseed/skynet-train). 



## Quick Start



### Pre-built docker image



The easiest way to use this is via the

[`developmentseed/skynet-data` docker image](https://hub.docker.com/r/developmentseed/skynet-data):



First, create a `docker.env` file with the contents including your MapboxAccessToken:



```

MapboxAccessToken=YOUR_TOKEN

```



Then run:



```sh

docker run -v /path/to/output/dir:/workdir/data --env-file docker.env developmentseed/skynet-data download-osm-tiles



docker run -v /path/to/output/dir:/workdir/data --env-file docker.env developmentseed/skynet-data

```



The first line downloads the OSM QA tiles to

`/path/to/output/dir/osm/planet.mbtiles`.  If you've already got that

file on your machine, you can skip this.



The second builds a training set using the default options (Roads

features from OSM QA tiles, images from Mapbox Satellite).  To change

the data sources, training set size and other options, add the

relevant environment variables to the `docker.env` file , one per

line.



### Local docker image



You can also create the docker images yourself using

docker-compose. Similarly to the quick-start above, make sure your

`docker.env` file has your MapboxAccessToken and any other environment

variables you want to set. Then run:



```

docker-compose build

```



to build your local docker image, and 



```

docker-compose run data download-osm-tiles

docker-compose run data 

```



to download the OSM QA tiles, and run the data collection as specified

in `docker.env`. By default the collected data will be saved into the

`data` directory, but you can overide it by using `-v

/path/to/output/dir:/workdir/data` after `docker-compose run data`

similar to the pre-built instructions above.



## Variables



The `make` commands below work off the following variables (with

defaults as listed):



```

# location of image files

IMAGE_TILES ?= "tilejson+https://a.tiles.mapbox.com/v4/mapbox.satellite.json?access_token=$(MapboxAccessToken)"

# which osm-qa tiles extract to download; e.g. united_states_of_america

QA_TILES=planet

# location of data tiles to use for rendering labels; defaults to osm-qa tiles extract specified by QA_TILES

DATA_TILES ?= mbtiles://./data/osm/$(QA_TILES).mbtiles

# filter to this bbox

BBOX ?= '-180,-85,180,85'

# number of images (tiles) to sample

TRAIN_SIZE=1000

# define label classes output

CLASSES=classes/roads-buildings.json

# Filter out tiles whose ratio of labeled to unlabeled pixels is less than or

# equal to the given ratio.  Useful for excluding images that are all background, for example.

LABEL_RATIO ?= 0

# set this to a zoom higher than the data tiles' max zoom to get overzoomed label images

ZOOM_LEVEL ?= 17

```



You can override any of these parameters in your `docker.env` and make

a full training set using the instructions above.



## Details



### Install



 - Install [NodeJS v4.6.2](https://nodejs.org/dist/v4.6.2/)

 - Install [tippecanoe](https://github.com/mapbox/tippecanoe)

 - Install [GNU Parallel](https://www.gnu.org/software/parallel/)

 - Install [shuf](https://www.gnu.org/software/coreutils/)

 - Clone this repo and run `npm install`.  (Note that this includes a

   node-mapnik install, which sometimes has trouble building in bleeding-edge

   versions of node.)



### Sample available tiles



`make data/sample.txt`



This just does a simple random sample of the available tiles in the given

`mbtiles` set, using `tippecanoe-enumerate`. For more intelligent filtering,

consider using `tippecanoe-decode` to examine (geojson) contents of each tile.



### Labels



Build label images: `make data/labels/color` or `make data/labels/grayscale`.

Uses the `CLASSES` json file to set up the rendering of OSM data to images that

represent per-pixel category labels.  See `classes/water-roads-buildings.json`

for an example.  Rendering is with `mapnik`; see [the

docs](https://github.com/mapnik/mapnik/wiki/Filter) for more on `filter`

syntax.



### Images



Download aerial images from a tiled source: `make data/images`



Heads up: the default, Mapbox Satellite, will need you to set the

`MapboxAccessToken` variable, and will cost you map views!



### Preview



Preview the generated data by opening up `preview.html?accessToken=&prefix=/path/to/data` in a local web server.