Body: <p>This is a problem I have been wondering about for a while.</p>
  <p>Suppose I have the following screenshot from Google Maps:</p>
  <p><a href="https://i.sstatic.net/IMhg1.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/IMhg1.png" alt="enter image description here" /></a></p>
  <p><strong>My Question:</strong> I want to find out a series of longitude/latitude coordinates that describe the perimeter of this red shape.</p>
  <p>I thought of the following approach to do this:</p>
  <ul>
  <li><p>Manually create a training dataset which contains screenshots from Google Maps with red highlighted regions along with their longitude/latitude coordinates</p>
  </li>
  <li><p>Make sure that each of the red shapes in each screenshot contains longitude/latitudes of some internal points: these can act as reference points</p>
  </li>
  <li><p>Use an algorithm like Convolution Neural Networks to &quot;learn&quot; the boundaries.</p>
  </li>
  </ul>
  <p>I am trying to find a feasible way to do this in Python - but I am not sure how to begin. Can someone please suggest if there is an easy way to do this using AI algorithms?</p>
  <p>Thanks!</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-08-03T07:32:36.830Z
Comments.Text: The original data that described the shape in vector format is only hidden from you because you are processing a screenshot, and not working within the mapping application. If you can solve that block, then there's no need to try to use ML to reverse engineer the original vector. However, I expect this is possible - could you perhaps clarify why access via the mapping app is not available?
Comments.UserDisplayName: Neil Slater
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-08-03T12:44:00.003Z
Comments.Text: @NeilSlater: thank you for your reply! I can use the actual source i.e. Google maps ... but i dont want to use the api.
Comments.UserDisplayName: stats_noob
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-08-03T12:57:57.233Z
Comments.Text: You are potentially making a *lot* of work for yourself due to reluctance to use the API. I don't think it changes any answer here, but I did want to check for an X/Y problem.
Comments.UserDisplayName: Neil Slater
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-08-03T13:00:52.900Z
Comments.Text: Could you clarify "Make sure that each of the red shapes in each screenshot contains longitude/latitudes of some internal points"? What format will this take? You will need at least two such reference points for each image. Ideally this would just be the two opposing corners of the rectangle, supplied independently of the image (i.e. no need to train something separately that would locate them and read off the numbers from pixel representations)
Comments.UserDisplayName: Neil Slater
Tags: <neural-networks>
Title: Using Neural Networks to Measure Distances on Google Maps
UserDisplayName: stats_noob
ThreadText: # Using Neural Networks to Measure Distances on Google Maps
  
  (Asked by: username_0 on 2023-08-03)
  
  This is a problem I have been wondering about for a while.
  
  Suppose I have the following screenshot from Google Maps:
  
  [![enter image description here](https://i.sstatic.net/IMhg1.png)](https://i.sstatic.net/IMhg1.png)
  
  **My Question:** I want to find out a series of longitude/latitude coordinates that describe the perimeter of this red shape.
  
  I thought of the following approach to do this:
  
  * Manually create a training dataset which contains screenshots from Google Maps with red highlighted regions along with their longitude/latitude coordinates
  * Make sure that each of the red shapes in each screenshot contains longitude/latitudes of some internal points: these can act as reference points
  * Use an algorithm like Convolution Neural Networks to "learn" the boundaries.
  
  I am trying to find a feasible way to do this in Python - but I am not sure how to begin. Can someone please suggest if there is an easy way to do this using AI algorithms?
  
  Thanks!
  
  **Tags:** neural-networks
  > **Comments:**
  > **username_1**: The original data that described the shape in vector format is only hidden from you because you are processing a screenshot, and not working within the mapping application. If you can solve that block, then there's no need to try to use ML to reverse engineer the original vector. However, I expect this is possible - could you perhaps clarify why access via the mapping app is not available?  
  > **username_0**: @NeilSlater: thank you for your reply! I can use the actual source i.e. Google maps ... but i dont want to use the api.  
  > **username_1**: You are potentially making a *lot* of work for yourself due to reluctance to use the API. I don't think it changes any answer here, but I did want to check for an X/Y problem.  
  > **username_1**: Could you clarify "Make sure that each of the red shapes in each screenshot contains longitude/latitudes of some internal points"? What format will this take? You will need at least two such reference points for each image. Ideally this would just be the two opposing corners of the rectangle, supplied independently of the image (i.e. no need to train something separately that would locate them and read off the numbers from pixel representations)  
  
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  ## 0 answers
  
  