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Distributed street level image image processing

Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public. LICENSE.

end to end flow

Sampling database


A MySQL database utilising Geospatial extensions holds all of the sample points in the following basic schema:

mysql> desc sample_points;
| Field           | Type                 | Null | Key | Default           | Extra                       |
| id              | bigint(20)           | NO   | PRI | NULL              | auto_increment              |
| city            | varchar(255)         | NO   |     | NULL              |                             |
| road_name       | varchar(255)         | NO   |     | NULL              |                             |
| osm_way_id      | bigint(20)           | NO   |     | NULL              |                             |
| sequence        | int(11)              | NO   |     | NULL              |                             |
| bearing         | double               | NO   |     | NULL              |                             |
| geom            | point                | NO   | MUL | NULL              |                             |
| sample_order    | int(11)              | NO   |     | NULL              |                             |
| sample_priority | int(11)              | NO   |     | NULL              |                             |
| ts              | timestamp            | NO   |     | CURRENT_TIMESTAMP | on update CURRENT_TIMESTAMP |
| cam_dir         | enum('left','right') | NO   |     | NULL              |                             |
| predicted       | tinyint(1)           | YES  |     | 1                 |                             |
| value           | double               | YES  |     | 0                 |                             |
13 rows in set (0.00 sec)

The table consists of 1 row per sample: 2 rows for each left and right reading at each coordinate.


Field Meaning
id A unique ID assigned to each sample point
city One of the 112 major towns and cities the sample belongs to
road_name Name of the road the sample is on
osm_way_id Open Street Map road network way id the sample belongs to
sequence Order of this sample w.r.t the point along the way id/road
bearing Heading in degrees of this point w.r.t previous and next point
geom Coordinates of this sample, stored as a WKT POINT(lng lat)
sample_order Order in which this sample should be taken
sample_priority Sampling priority for this point (lowest = highest priority)
ts Timestamp for when this point was sampled or interpolated
cam_dir Sample direction - currently looking left or right w.r.t bearing
predicted True if this point has been interpolated/predicted, False if an actual reading has been made
value The green percentage value for this sample point

Initial data load

The code in infrastructure/mysql will import the initial (un-sampled) dataset into mysql. It expects as input the csv files for each city generated by the trees-open-street-map module. During import, sample points will be assigned a sampling priority as described below.

Sampling priority

There are 418,941 unique osm way ids/roads for the 112 major towns and cities in England and Wales. If sampling at 10 metre intervals, this will result in 8,315,247 coordinates, or 16,630,494 sample points if obtaining a left and right image at each point.

A key requirement of this project is a complete dataset

Missing points have been interpolated/predicted using IDW (details below). Since it has been assumed that points closer to one another will exhibit a higher correlation, it makes sense to sample points which are maximally further appart. A naive sampling scheme would simply sample random points across a city in a uniform fashon. It has been assumed that by guiding the sampling process as to maximise information gain, we can obtain a higher accuracy dataset faster than a random sampling method: In a random sampling scheme, it is possible to sample 2 adjacent points which will do little to reduce the overall error if sampling points are spatialy correlated.

In the case of sampling points along a road, this corresponds to a 1- dimensional maximally distant sampling problem. For example, given a road indexed by (pending) sample points:

[01 02 03 04 05 06 07 08 09 10 11 12 13 14 15]

If restricted to 1 sample per day, we could sample each point at random for 15 days, or proceed from lest to right. Instead, it is desirable to sample points with maximum information gain as their sampled values will be propogated to neighbours via the inverse distance weighting scheme. As such, the 15 indices should be visited according to this schedule:

[08 04 12 02 06 10 14 01 03 05 07 09 11 13 15]

Where the middle (08) "pivot" point is visited first, point 04 second and so on. The idea is to assign a sampling priority to each point which corresponds to a "fold depth" if the list were to be continuously folded in half like a piece of paper:

"Continuously folding paper"

To solve this problem, each paper fold can be seen as a depth in a binary tree. As such, the algorithm used to assign sampling priority to points traverses the list as if it were a tree in breadth first search order.

         02                      03
   04          05          06          07
08    09    10    11    12    13    14    15

For more details, also see the unit test.

Points on each road are assigned a priority using this script before database insertion.

Image processing API

Image processing pipeline

The image processing pipeline is divided by a number of network based FIFO queues and worker processes. The objective is to separate the image sampling and processing logic whilst keeping track of sampling state. The end-to-end pipeline allows for complete control and flexibility over the sampling process.

Layer 1

This layer consumes unsampled data points from the image processing API and either: Places image download jobs directly onto the image_download_jobs queue (option 1) or sends jobs to individual queues for each city (option 2).

Option 1: Quota based data load

N pending sample points are selected from the image processing API ordered by their sampling priority.

Option 2: City queue data loader

The data loader module is run on an ad-hoc basis. It consumes from the image processing API and inserts a batch of work onto the first layer of queues.

City queues and "Jobs"

At this level, there is 1 (persitent) queue per city. This allows for maximum flexibility and easy monitoring of image processing status. Each queue contains a number of jobs, where each job is defined as follows:

  "id": 123,
  "city": "Cardiff",
  "road_name": "Clydesmuir Road",
  "osm_way_id": "24005721",
  "sequence": "000057",
  "latitude": 51.491386,
  "longitude": -3.141202,
  "bearing": 46.48,
  "cam_dir": "left"

The job structure is used throughout the processing pipeline.

Layer 2

Job scheduler

This (optional) module sits between the initial backlog* queues and the current work queue(s). It is both a consumer and producer of jobs. It's purpose is:

  1. Control the job processing bandwidth. E.g., shovel a max quota of jobs onto the current work queue per day.

  2. Control the processing progress for each city. E.g., the city queues may be consumed on a round robin basis, in which case smaller cities will complete first. Or, jobs may be selected using a weighted priority scheme, in which case the number of jobs selected from each city is proportionate to the size of the city.

  3. Processing flexibility. Jobs can be shoveled to different queues on an ad -hoc basis or if processing restrictions change (e.g., different quota, the addition of extra processing nodes or some form of load balancing proxy.

As such, the job shovel can be thought of as a job scheduler.

Note that this layer can by skipped if the data_load/ has not been used in Layer 1.

Layer 3

Image downloader

The process blocks on the image_download_jobs queue until a new job arrives. Note that this behaviour can follow a scheme - see image_download/ for details.

Upon receiving a new image download job, the process will make a request for a street-level image at a specific location and direction according to the parameters in the download job. In addition, a second request is made to the street-view API for associated image meta data which includes the month/year in which the image was obtained.

Each downloaded image is stored locally (although could be pushed to an external store) and then an image processing job is generated and pushed to the image_processing_jobs queue where it will live until consumed by workers in layer 4.

Having consumed the image download job if everything went as planned, the job is deleted from the incoming queue. Otherwise, the job is marked as "buried" as not to loose any samples and to assist in debugging/error reproduction. Using a queue administration interface (see below), it is then possible to flush or move buried jobs back onto the active queue after having resolved the issue.

Layer 4

Image processor

This layer consists of 1 or more processes dedicated to image processing. At an abstract level, an image processing process is both a consumer and a producer. Image processing jobs are consumed from the image_processing_jobs queue, processed and then the result is pushed to the image processing API. As such, the image processing stages can be divided up into 3 sub-steps:


The process blocks on the image_processing_jobs queue according to a consumption scheme which defines how much work the process will do before self terminating. Specifically, the worker process will consume tasks until:

  • Forever: Block on queue forever, processing jobs when they arrive.
  • Queue is empty: Drain the queue until it is empty.
  • Maximum work load: Do N many jobs as long as there exists work.

The format of a consumed job is exactly the same as a JSON image download job as described above. Upon receiving a job, the image processor will obtain the corresponding image from the image store:


Note that the image could be located locally or as a network resource.


The next step is to quantify the amount of vegetation present in the image.

Currently, the image is first converted to the L*a*b* colour space. Lab is better suited to image processing tasks since it is much more intuitive than RGB. In Lab, the lightness of a pixel (L value) is separated from the colour (A and B values). A negative A value represents degrees of green, positive A, degrees of red. Negative B represents blue, while positive B represents yellow. A colour can never be red and green or yellow and blue at the same time. Therefore the Lab colour space provides a more intuitive separability than RGB (where all values must be adjusted to encode a colour.) Furthermore, since lightness value (L) is represented independently from colour, a 'green' value will be robust to varying lighting conditions.

The actual amount of vegetation present in the scene is defined to be the ratio of green pixels in the overall image. Although it is possible to plug-in other metrics at this stage.


The results from image processing are then pushed to the image processing API. Currently, the amount of green is pushed via a HTTP POST request to the following end-point:


With the following from:


Having pushed the image processing results, the worker then deletes the job from the queue and then repeats the cycle.

Running the image processor

Whilst the image processing script is invoked from a higher level script, it is possible to run it independently by invoking the script, which in turn will invoke:


Where WORKER_NAME = name of process, SCHEME = -1, 0, or N according to the consumption scheme described above, SRC_QUEUE = name of the incoming job queue and SRC_STORE = (local) directory where downloaded images have been stored.

Sample point interpolation

For green percentage, it has been assumed that there exists a spatial relationship between each point: It is likely that points closer together are likely to be similar. Furthermore, it has been assumed that this relationship is stronger for points on the same road and even stronger still for points on the same side of the road. This is typically the case for roads with rows of trees, stretches of park/woodland and for linear features such as hedgerows.

When interpolating/predicting missing points there exist many schemes (for example mean N-nearest neighbour). However, given the above assumption, missing sample points along a road are predicted according to the Inverse Distance Weighting of sampled values of the nearest left and right points: predicted = (1-w)*left + w*left where w = [0, 1]: distance along line between left and right sample points. -- See code+unit tests in interpolator/ for details.


Besides Beanstalkd's protocol, there exist a number of queue monitoring and admin tools. Aurora is a nice (web based) tool which allows for monitoring and manipulation of queue states - E.g., the ability to move jobs around queues. And for the CLI, beanwalker is a nice Go based tool. Both tools have been used in this project.



  • beanstalkd for queues.
  • Mysql (with GIS Spatial extentions)


pip3 install -r requirements.txt


Open Government license v3 OGL v3.


Distributed Google street view image processing




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