OSMDeepOD is a project about object detection from aerial imagery using open data from OpenStreetMap (OSM). The project uses the open source software library TensorFlow, with a retrained Inception V3 neuronal network.

This work started as part of a semester thesis autumn 2015 at Geometa Lab, University of Applied Sciences Rapperswil (HSR).

The simplest way to use the detection process is to clone the repository and build/start the docker container.

git clone https://github.com/geometalab/OSMDeepOD.git
cd OSMDeepOD/docker/
sudo docker build . -t osmdeepod
sudo docker run -it --name osmdeepod -v ./:/objects osmdeepod bash

After the previous shell commands you have started a standalone instance of OSMDeepOD and you are connected to it. If you have a nvida GPU and nvidia-docker installed, you could use the "nvidia-docker" command to run the container for automatically usage of the GPU¹.

To start the detection process use the src/role/main.py² script.

1. Use the manger option to select the detection area and start the detection with the --standalone parameter.

python3 main.py --config ./config.ini manager 9.345101 47.090794 9.355947 47.097288 --standalone

After the detection process has finished a "detected_nodes.json" file will appear with the results. If you like to use OSMDeepOD in a more parallel and distributed way have a look at the https://github.com/geometalab/OSMDeepOD-Visualize repository. There you have got the ability to use redis as a message queue and you can run many OSMDeepOD instances as workers.

The configuration works with an INI file. The file looks like the following:

[DETECTION]
Network = /path/to/the/trained/convnet
Labels = /path/to/the/label/file/of/the/convnet
DetectionBarrier = 0.99
Word = crosswalk
Key = highway
Value = crossing
Zoom = 19
Compare = yes
Orthofoto = other
FollowStreets = yes

[REDIS]
Server = 127.0.0.1
Port = 40001
Password = crosswalks
BboxSize = 2000
Timeout = 5400

Some hints to the config file:

• "Word" is the key value of the labels file
• "Key" and "Value" builds the search Tag for OSM
• "Compare" means compared to OSM tagged Nodes
• The section REDIS should be self explanatory, this is not necessary in the standalone mode
• "BboxSize" is the size in meters of the split large Bbox
• "Timeout" after the expired time the job does fail

To use your own Orthofotos you have to do the following steps:

1. Add a new directory to src/data/orthofoto
2. Add a new module to the directory with the name: <your_new_directory>_api.py
3. Create a class in the module with the name: <Your_new_directory>Api (First letter needs to be uppercase)
4. Implement the function def get_image(self, bbox): and returns a pillow image of the bbox
5. After that you can use your api with the parameter --orthofots <your_new_directory>

If you have problems with the implementation have a look at the wms or other example.

During this work, we have collected our own dataset with swiss crosswalks and non-crosswalks. The pictures have a size of 50x50 pixels and are available by request.

Picture 3: Crosswalk Examples

Picture 4: No Crosswalk Examples

• Python

  At the moment, we support python 3.5

• Docker

  In order to use volumes, I recommend using docker >= 1.9.x

• Bounding Box of area to analyze

  To start the extraction of crosswalks within a given area, the bounding box of this area is required as arguments for the manager. To get the bounding box the desired area, you can use https://www.openstreetmap.org/export to select the area and copy paste the corresponding coordinates. Use the values in the following order when used as positional arguments to manager: left bottom right top

• http://wiki.hsr.ch/StefanKeller/SA_BA_Gamified_Extraction_of_Crosswalks_from_Aerial_Images
• www.hsr.ch
• www.osm.org
• www.maproulette.org

• 1: The crosswalk_detection container is based on the nvidia/cuda:7.5-cudnn4-devel-ubuntu14.04 image, may you have to change the base image for your GPU. ↩
• 2: For more information about the main.py use the -h option. ↩

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