Introduction: Due to the increasing green house gases in our environment the danger of global warming increases day by day and make the lifes on this planet very much affected. Now-a-days, The Environment is getting polluted and there is no ecological balance in the Environment. So there are a lot of harmful and dangerous gases were getting increased and they are ringing the danger bells to all of us. Even many cities and countries are worrying about this issue and getting much affected by these harmful pollutants. This problem got raised due to the lack of less tress and got increased with Industries which releases poisonous gases every day. This results in reducing the Density of the Trees and we people should take some charges and making the environment in a cleaned manner. That's our duty. In this case, We are going to build one project that will help us to monitor and examine the Density of trees, the Air Quality Index, Etc. We will get some good results and outcomes from this project. So that we observe and maintain the density of trees in some areas.
Main Objective: This project basically helps in determining the air quality index of the particular region we are monitoring by determining the density of trees in that particular region. The project allows the user to choose any particular location through a google earth interface and for that particular region the user will get all the data like air quality index and a layout where a map will show the density of trees in that particular area.
Methodology: The purpose of this project is to create a model that can predict the health of individual trees and based on that we will predict the air quality index of that particular region according to the density of trees. the task is approached by using Deep Learning Model where images of trees are collected using Google Satellite Imagery and then we recall data of collected images like pixels of the trees and size of the trees. the collected images should be in High resolution which will provide absolute data features of the trees required. Once you have the imagery, you'll create training samples and convert them to a format that can be used by a deep learning model. To provide your deep learning model with the information it needs to extract in the image, we'll create features for a number of trees to teach the model what the size, shape, and spectral signature of trees may be. These training samples are created and managed through the Label Objects for Deep Learning tool. To make sure capturing a representative sample of trees in the area, we'll digitize features throughout the image. These features are read into the deep learning model in a specific format called image chips.