guest talk- roof classification
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Deep Learning : Roof Style Classification
problem
Kishore Kumar MohanM.S in Information Systems Student
Under the guidance ofProf. Sri Krishnamurthy
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Roof style Classification using convolutional neural network
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Objective: To classify various roof styles using satellite aerial images captured from Google Earth and to benchmark accuracy obtained using regular neural nets and convolutional nets.
Problem Type: Supervised Classification
Quick Overview of Data:
Roof type 01- Flat: Roof type 02- Gable:
Roof type 03- Hip: Roof type 04- Gambrel:
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Business Impact:
- Underwriting heavily relies on roof type for certain states. Third party data
vendors like Black knight http://www.bkfs.com/data-and-
analytics/Pages/default.aspx
- Infact some insurance companies to make data collection easier provides
discount on premium for clients if they choose roofing type of their choice.
https://www.statefarm.com/insurance/home-and-
property/homeowners/discounts/roofing-materials
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Packages Used
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Python Library – Tensorflow (Bitfusion – Amazon AWS), Opencv
Why Opencv and not scipy - Improved Gaussian blur performance.
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Approaches and Reading Images
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- Regular Net
- Regular Net with edges detected
- Convolutional Neural Net
Reading Images for all 3 approaches:
Regular Net (1x16384)
Regular Net – Edges(1x16384)
CNN(128x128x3)
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Regular Net – Code and Math
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Condition for matrix multiplication (X.W): we should have "mxn" and
"nxj" format to get "mxj" form. We break the image to 1x16384
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Convolutional Neural Net – Code and Layers
7Optimizer – SGD with learning rate = 0.001
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Activation functions - CNNRelu Logit
- Imitates Biological neural nets. Activation occurs only when the input signal strength is greater than threshold
- Fine values between 0 and 1. Less computational complexity than softmax
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Accuracy and Analysis
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Summary
• Reading images for a specific architecture
• Why opencv?
• Regular Net – Math and code
• Feeding images after extracting features has zero impact
• Convolutional neural network and activation functions
• Accuracy and Analysis
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