This is my Internship project ppt on Road Network Extraction Using Satellite Imagery. In this project, A robust and efficient method for the extraction of roads from a given set of satellite images is explained. In this work, we implement the U-Net segmentation architecture on the Mnih et. al.Massachusetts Roads Dataset for the task of road network extraction.

1. Presented By
(Chakrapani , Sumit Raj & Himanshu Ranjan)
Department of Computer Science and Engineering
Gaya College of Engineering, Gaya
Under Supervision
Of
( Dr. SRIPARNA SAHA)
Associate Professor
Department of Computer Science and Engineering
Indian Institute of Technology Patna

2. Outline :
 Introduction
 Working Diagram
 Apply Module
 Requierd Data
 Result Analysis
 Conclusion
 Comparison
 References.

3. Introduction:
 In today’s world of the growing population, the
need for urban planning is very high.
 In this project, A robust and efficient method for
the extraction of roads from a given set of
satellite images is explained.
 Roads play a vital and important role in urban
planning and thus, its extraction can be of great help.
 Most of these methods can be divided into two
categories: road area extraction and road centerline
extraction.

4.  There are many methods to extract roads but the main
disadvantage of them is the difficulty to provide the best
parameters for a particular image.
 Road extraction explained in this paper depends only on
the color of the road. The advantage of this method is that
road images from any type of satellite can be used provided it
has more than 0.5m resolution.
 In this work, we implement the U-Net segmentation
architecture on the Mnih et. al.Massachusetts Roads Dataset
for the task of road network extraction.

5. Apply Module:
Fastai=0.7.0resnet34 :- The fastai library simplifies
training fast and accurate neural nets using modern best practices.
Adam optimization :- Adam is a
replacement optimization algorithm for stochastic gradient
descent for training deep learning models.
 U-Net 34 :- A U-Net is a convolutional neural network
architecture that was developed for biomedical image
segmentation
Pytorch=0.4.1 :- A rich ecosystem of tools and libraries
extends PyTorch and supports development in computer vision,
NLP and more.
Torchvision=0.2.0 :- The torchvision package consists of
popular datasets, model architectures, and common image
transformations for computer vision.

6.  Relu activation function:- A neural network without an activation
function is essentially just a linear regression model. The activation function
does the non-linear transformation to the input making it capable to learn and
perform more complex tasks.
Matplotlib :- Matplotlib is a Python 2D plotting library which produces
publication quality figures in a variety of hardcopy formats and interactive
environments across platforms.
Numpy: NumPy is a general-purpose array-processing package. It provides
a high-performance multidimensional array object, and tools for working with
these arrays.

7. Requierd Data

8. Working Diagram :
I n this work, we implement the U-Net segmentation architecture on the
Mnih et. al. Massachusetts Roads Dataset for the task of road network
extraction.
Here network was trained on GPU(AWS P2.x instance) in 0.28 seconds
and on CPU in 6 seconds.

9. Result Analysis :
 In this work, we implement the U-Net segmentation architecture on the
Mnih et. al.Massachusetts Roads Dataset for the task of road network
extraction.
 The trained network achieves a mask accuracy of 96% on the test set.
Precision Score 97.82
Recall Score 96.50
F1 Score 97.04

10. Results of the implemented system on one of the Massachusetts Road
Dataset test set image :
Original Image Mask generated by the
system
Mask overlaid on
original

11. Comparison
Model Breakeven Point
Mnih -CNN 0.8765
Mnih-CNN+CRF 0.8904
Saito-CNN 0.9047
U-Net 0.9531
ResU-Net 0.9687

12. Conclusion
 The algorithm introduced is an automatic one. It requires only very little
interaction from the users.
 The algorithm was implemented to detect roadways from satellite images .
 The important and key parameter of this algorithm is the color of the
roads in the database. Different types of roads can be extracted based on this
algorithm.
 Since extraction is solely based on color, some of the barren lands and
small areas of parking lots are also being extracted. This is because the
locations also have the same pixel intensity values as that of roads.
 Artificial intelligence methods could be included to remove the unwanted
objects that are being extracted.
 The algorithm implemented is fast, robust and easy to understand and
implement.

13. References
 Olaf Ronneberger, Philipp Fischer, Thomas Brox. U-Net:
Convolutional Networks for Biomedical Image Segmentation. Medical
Image Computing and Computer-Assisted Intervention (MICCAI),
Springer. 2015.
 Volodymyr Mnih. Machine Learning for Aerial Image Labeling.
University of Toronto. 2013.
 Jeremy Howard and others. fastai. GitHub. 2018.
 G. Cheng, Y. Wang, Y. Gong, F. Zhu, and C. Pan, “Urban road
extraction via graph cuts based probability propagation,” in ICIP, 201