Answers.Body: <p>Welcome to AI.SE Hadrien!</p>
  
  <p>A possible approach is:</p>
  
  <ol>
  <li>Gather many <em>example</em> images of rooms for which you know the square footage. Record the square footage of each room together with each image.</li>
  <li>Pick a machine learning model that is well suited to learning relationships between images and numerical outputs, like a Convolutional Neural Network.</li>
  <li>Train a machine learning model using an <em>optimisation</em> algorithm, like <a href="https://en.wikipedia.org/wiki/Gradient_descent" rel="nofollow noreferrer">gradient decent</a>. In the case of training a CNN, this algorithm starts by setting up the network with randomly chosen connection strengths between different 'simulated' neurons. It then exposes the network to an input image, and observes the number the network outputs in response. The algorithm then makes small adjustments to the strengths of the connections in the network, so that if the network were exposed to the image a second time, it would output a number that was less wrong (i.e. closer to the correct square footage). By repeating this process many thousands of times, and with many images, the network eventually becomes quite good at guessing the square footage of new images that it hasn't seen before.</li>
  </ol>
  
  <p>In practice, the network is quite likely to pick up on <em>any</em> patterns in the images that correlate with square footage, not necessarily the ones you want. For example, it might pick up on, say, the fact that humans in the picture are usually a good object to guess at the scale of the rest of the room.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-03-13T02:58:19.773Z
Answers.LastActivityDate: 2019-03-13T02:58:19.773Z
Answers.UserDisplayName: John Doucette
Body: <p>I wonder if it would be possible to know the size of a room using image, I don't see anything about this subject, do you have some idea how it could be done?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-03-12T14:06:40.060Z
Comments.Text: Producing a floor plan (that's essentially what you want to do) using images is possible even if somewhat difficult, there are apps which do that. However, they work from collections of images taken from defined viewpoints, I don't think that working from just one image can work as there always are hidden areas that could have totally different geometry from the visible part of the room.
Comments.UserDisplayName: Hans-Martin Mosner
Tags: <machine-learning><deep-learning><computer-vision><papers>
Title: How could we estimate the square footage of a room from an image?
UserDisplayName: Hadrien Berthier
ThreadText: # How could we estimate the square footage of a room from an image?
  
  (Asked by: username_0 on 2019-03-12)
  
  I wonder if it would be possible to know the size of a room using image, I don't see anything about this subject, do you have some idea how it could be done?
  
  **Tags:** machine-learning, deep-learning, computer-vision, papers
  
  > **Comments:**
  > **username_1**: Producing a floor plan (that's essentially what you want to do) using images is possible even if somewhat difficult, there are apps which do that. However, they work from collections of images taken from defined viewpoints, I don't think that working from just one image can work as there always are hidden areas that could have totally different geometry from the visible part of the room.  
  
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  ## 1 answers
  
  ### Answer by username_2 on 2019-03-13. Score: 0 | Accepted answer
  
  Welcome to AI.SE username_0!
  
  A possible approach is:
  
  1. Gather many *example* images of rooms for which you know the square footage. Record the square footage of each room together with each image.
  2. Pick a machine learning model that is well suited to learning relationships between images and numerical outputs, like a Convolutional Neural Network.
  3. Train a machine learning model using an *optimisation* algorithm, like [gradient decent](https://en.wikipedia.org/wiki/Gradient_descent). In the case of training a CNN, this algorithm starts by setting up the network with randomly chosen connection strengths between different 'simulated' neurons. It then exposes the network to an input image, and observes the number the network outputs in response. The algorithm then makes small adjustments to the strengths of the connections in the network, so that if the network were exposed to the image a second time, it would output a number that was less wrong (i.e. closer to the correct square footage). By repeating this process many thousands of times, and with many images, the network eventually becomes quite good at guessing the square footage of new images that it hasn't seen before.
  
  In practice, the network is quite likely to pick up on *any* patterns in the images that correlate with square footage, not necessarily the ones you want. For example, it might pick up on, say, the fact that humans in the picture are usually a good object to guess at the scale of the rest of the room.
  
  
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