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DeepOSM: Detect roads and features in imagery with neural nets using OpenStreetMap

DeepOSM

Classify roads and features in satellite imagery, by training neural networks with OpenStreetMap (OSM) data. DeepOSM lets you:

  • Download a chunk of satellite imagery
  • Download OSM data that shows roads/features for that area
  • Generate training and evaluation data
  • Display predictions of mis-registered roads in OSM data, or display raw predictions of ON/OFF

Running the code is as easy as install Docker, make dev, and run a script.

Contributions are welcome. Open an issue if you want to discuss something to do, or email me .

Default Data/Accuracy

By default, DeepOSM will analyze about 200 sq. km of area in Delaware. DeepOSM will

  • predict if the center 9px of a 64px tile contains road.
  • use the infrared (IR) band and RGB bands.
  • be 75-80% accurate overall, training only for a minute or so.
  • use a single fully-connected relu layer in TensorFlow .
  • render, as JPEGs, "false positive" predictions in the OSM data – i.e. where OSM lists a road, but DeepOSM thinks there isn’t one.

DeepOSM: Detect roads and features in imagery with neural nets using OpenStreetMap

Background on Data – NAIPs and OSM PBF

For training data, DeepOSM cuts tiles out of NAIP images , which provide 1-meter-per-pixel resolution, with RGB+infrared data bands.

For training labels, DeepOSM uses PBF extracts of OSM data, which contain features/ways in binary format that can be munged with Python.

The NAIPs come from a requester pays bucket on S3 set up by Mapbox , and the OSM extracts come from geofabrik .

Install Requirements

DeepOSM has been run successfully on both Mac (10.x) and Linux (14.04 and 16.04). You need at least 4GB of memory.

AWS Credentials

You need AWS credentials to download NAIPs from an S3 requester-pays bucket. This only costs a few cents for a bunch of images, but you need a credit card on file.

export AWS_ACCESS_KEY_ID='FOO' export AWS_SECRET_ACCESS_KEY='BAR' 

Install Docker

First, install a Docker Binary .

I also needed to set my VirtualBox default memory to 4GB, when running on a Mac. This is easy:

  • start Docker, per the install instructions
  • stop Docker
  • open VirtualBox, and increase the memory of the VM Docker made

Run Scripts

Start Docker, then run:

make dev

Download NAIP, PBF, and Analyze

Inside Docker, the following Python scripts will work. This will download all source data, tile it into training/test data and labels, train the neural net, and generate image and text output.

The default data is six NAIPs, which get tiled into 64x64x4 bands of data (RGB-IR bands). The training labels derive from PBF files that overlap the NAIPs.

python bin/create_training_data.py python bin/train_neural_net.py 

For output, DeepOSM will produce some console logs, and then JPEGs of the ways, labels, and predictions overlaid on the tiff.

Jupyter Notebook

Alternately, development/research can be done via jupyter notebooks:

make notebook

To access the notebook via a browser on your host machine, find the IP VirtualBox is giving your default docker container by running:

docker-machine ls  NAME      ACTIVE   DRIVER       STATE     URL                         SWARM   DOCKER    ERRORS default   *        virtualbox   Running   tcp://192.168.99.100:2376           v1.10.3

The notebook server is accessible via port 8888, so in this case you’d go to: http://192.168.99.100:8888

Readings

Also see a work journal here .

Papers – Relevant Maybe

Papers – Not All that Relevant

Papers to Review

Recent Recommendations

Citing Mnih and Hinton

I am reviewing these papers from Google Scholar that both cite the key papers and seem relevant to the topic.

Original Idea

This was the general idea to start, and working with TMS tiles sort of worked (see first 50 or so commits), so DeepOSM got switched to better data:

DeepOSM: Detect roads and features in imagery with neural nets using OpenStreetMap

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