mapillary / mapillary/mapillary-python-sdk

[Feature] Discussion over having a visual representation of the results

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feature
Dominant language
Python
Stars
66
Forks
23
Avg merge
1d 20h
Merged PRs (30d)
11

Description

Is your feature request related to a problem? Please describe.
It is not really as much of a problem, as it is that of a feature request. It would be great if there was a way of visualizing results from mapillary via a bounding box. This can help in geospatial analysis for enthusiasts in data science or for developers who like to perform visual analysis in Jupyter notebooks. The results can look something similar to this,

image

image

image

The above images were selected from the Getting Started with the new Mapillary API v4 post on Mapillary - Blog.

Describe the solution you'd like
One-liners, much like in libraries, for example, in networkx, and matplotlib, where there is a very easy of quickly generating a graph to see what the results look like. More code like the discussion is to be had later in the future, but this can serve as a ground basis for starting from somewhere

Describe alternatives you've considered
We can use something like the Folium library that helps in performing GeoSpatial analysis.

Additional context
This idea was slightly discussed in the 1st Maintainer Meeting on the 24th of June

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start by reviewing the Mapillary API v4 context and the maintainer-meeting discussion referenced in the issue, then inspect how bounding-box results are currently exposed by the Python SDK. The issue does not name files or tests; the scope and desired visualization interface need agreement before implementation, with a working notebook-oriented visualization as the likely completion criterion.

Written by the indexing model from the issue text.

Assessment

Tech stack
jupyter, matplotlib, python
Domain
data, data-visualization
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
25/100

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