Answers.Body: <p>You need to know the basic mechanics of CNN , you can't simply finetune a pretrained net (VGGNET) everytime. VGGnet is trained on imagenet dataset so most of the features in it are not so relevant for your task (recognising buildings from aerial view). so it is much better to train a cnn from scratch. You can't simply say that it fails to classify residential , non-residential . It is best to find what could be done by trying,</p>
  
  <p><em>My issue is that non-residential buildings are mostly larger than residential houses, now I want to use this property to distinguish between residential and non residential.</em></p>
  
  <p>Most of the times it is better to observe the features recognised by the net and then come with an approach , rather than enforcing your views.</p>
  
  <p>You may find this helpful <a href="https://www.cs.toronto.edu/~vmnih/docs/Mnih_Volodymyr_PhD_Thesis.pdf" rel="nofollow noreferrer">https://www.cs.toronto.edu/~vmnih/docs/Mnih_Volodymyr_PhD_Thesis.pdf</a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-07-01T10:35:48.490Z
Answers.LastActivityDate: 2018-07-01T10:35:48.490Z
Answers.UserDisplayName: thecomplexitytheorist
Body: <p>I want to train a CNN (Vggnet) to identify different types of buildings from aerial images. </p>
  
  <p>However seeing that a CNN "ignores" size, e.g. the same type of dog in one image can be large and small in another image but will still be classified as a dog.</p>
  
  <p>My issue is that non-residential buildings are mostly larger than residential houses, now I want to use this property to distinguish between residential and non residential. Is this even possible?</p>
  
  <p><a href="https://i.sstatic.net/5hV2J.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/5hV2J.jpg" alt="enter image description here"></a><a href="https://i.sstatic.net/e5T9L.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/e5T9L.jpg" alt="enter image description here"></a></p>
  
  <p>Thanks</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2018-07-01T18:32:12.870Z
Comments.Text: CNN doesn't  ignore size at all,who told you.Can you give us a hint!
Comments.UserDisplayName: quintumnia
Tags: <convolutional-neural-networks>
Title: Using CNN to identify buildings from aerial images
UserDisplayName: Jcstay
ThreadText: # Using CNN to identify buildings from aerial images
  
  (Asked by: username_0 on 2018-07-01)
  
  I want to train a CNN (Vggnet) to identify different types of buildings from aerial images.
  
  However seeing that a CNN "ignores" size, e.g. the same type of dog in one image can be large and small in another image but will still be classified as a dog.
  
  My issue is that non-residential buildings are mostly larger than residential houses, now I want to use this property to distinguish between residential and non residential. Is this even possible?
  
  [![enter image description here](https://i.sstatic.net/5hV2J.jpg)](https://i.sstatic.net/5hV2J.jpg)[![enter image description here](https://i.sstatic.net/e5T9L.jpg)](https://i.sstatic.net/e5T9L.jpg)
  
  Thanks
  
  **Tags:** convolutional-neural-networks
  
  > **Comments:**
  > **username_1**: CNN doesn't  ignore size at all,who told you.Can you give us a hint!  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2018-07-01. Score: 0 | Accepted answer
  
  You need to know the basic mechanics of CNN , you can't simply finetune a pretrained net (VGGNET) everytime. VGGnet is trained on imagenet dataset so most of the features in it are not so relevant for your task (recognising buildings from aerial view). so it is much better to train a cnn from scratch. You can't simply say that it fails to classify residential , non-residential . It is best to find what could be done by trying,
  
  *My issue is that non-residential buildings are mostly larger than residential houses, now I want to use this property to distinguish between residential and non residential.*
  
  Most of the times it is better to observe the features recognised by the net and then come with an approach , rather than enforcing your views.
  
  You may find this helpful <https://www.cs.toronto.edu/~vmnih/docs/Mnih_Volodymyr_PhD_Thesis.pdf>
  
  
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