Everyday Geospatial Data Storages are deluged with millions of optical overhead imagery captured from airborne or space-borne platforms. Manual data interpretation on such a large amount of data becomes an intractable task, hence machine vision techniques must be employed if we want to make any use of the available data. In this project, we have made an application which would deal with semantic segmentation of high-resolution (aerial) images where a semantic class label is assigned to each pixel via supervised classification. Deep learning techniques have shown impressive performance, particularly for image processing. However, a major drawback of using deep learning techniques is that they are extremely data-hungry and using them leads us in exacerbating the limitations of supervised learning, to get enough annotated training data. But on the bright side, these techniques are immune to noise so instead of taking and annotating large datasets we can use publicly available datasets. Such publicly available datasets might contain errors but in return, a very high amount of data is available to us on which we can train our model. As deep learning models would require high computational power and in order to maintain high scalability we have tried to exploit the benefits of Cloud Computing and RESTful services (Representational state transfer). We have successfully built a REST API and deployed it on the institute's server. Our application segments each map image into various segments through which we can perform time series analysis on images and track development in various parts of our country. Moreover, our time series analysis would help us in tracking various environmental changes such as deforestation, afforestation, urbanization, etc and infrastructural changes such as Rural development. Also, we would provide additional support files to load all the detected classes as the layers in QGIS and improve segmentation parts through LabelMe.
Run this piece of code only if you have any other version of python installed One can check his/her python version by typing
python3 --versionon your terminal and if you get
Python 3.6.0
as your response you have python 3.6 installed as default. or else install python 3.6 as follows
sudo apt install build-essential checkinstall
sudo apt install libreadline-gplv2-dev libncursesw5-dev libssl-dev libsqlite3-dev tk-dev libgdbm-dev libc6-dev libbz2-dev
wget https://www.python.org/ftp/python/3.6.0/Python-3.6.0.tar.xz
tar xvf Python-3.6.0.tar.xz
cd Python-3.6.0/
./configure
sudo make altinstallsudo apt install python3-pip
sudo apt install python3-venvpython3.6 -m venv Map-Seg
cd Map-Seg
source bin/activategit clone https://github.com/parshwa1999/Map-Segmentation.gitbrowse to directory by typing
cd Map-Segmentationpip install -r requirements.txt
pip freeze > requirements.txtcd Achilles
python3 manage.py migrate
python3 manage.py makemigrations
python3 manage.py migratepython3 manage.py runserverlet the entire server run.
To visit application page click here and to visit admin page click here
Username: root
Password: 11to1or11
For further details one can refer my Report and Presentation
