LORD'S EYE

https://docs.google.com/presentation/d/1rqvTc-bbkNMk_eUF5rsDc-ri-9sL3fCKBj5gmqRdiVQ/edit#slide=id.p1

https://github.com/Shivani1502/UrbanDevelopment-nasa

Emergency services & Municipal Corporation / Gov

1. Provide data of unsafe zones
2. Provide probability of a zone/area turning into unsafe
3. Imposing challans to commuters not following traffic rules (hence preventing accidents &
4. unsafe areas)

Motivation -

1. Incentivising on capturing unsafe zones
2. Compensation on challans for identifying unsafe zones & providing solutions / helping to
3. solve the problem.
4. Incentivising shop owners for using video feed whenever required for defining unsafe
5. zones

Business Model -

1. Recommendation of less unsafe user routes
2. More/less premium based on unsafe zone
3. Strategic location & facts to municipal & road maintenance team.
4. White labeling
5. Data insights to Target Audience
6. Selling datasets & insights to Autonomous vehicle manufacturing companies 

Tools -

1. Android Studio
2. Machine Learning
3. Blockchain technology
4. JavaScript
5. Firebase cloud
6. Web Scrapping

Nasa Space Apps Partner helps us in the getting the following details -

1. The app captures driving data like speed, braking, etc
2. Recognise driving habits and gives feedback on the behaviour
3. Gives helpful tips for safer driving
4. Incase of an accident, distress signal can be sent to the nearest emergency services
5. like police station, hospitals, fire station, etc
6. Image Processing can be used to detect the probability of the collision of car with the
7. vehicle in front and can be alerted about the imminent collision. 

We have used NASA Earth Observations, MODIS, Landsat and USGS Earthquake dataset.

We have used the Earth Observations Datasets from NASA Earth observations and Nasa GIBS.

For average land and sea surface temperature we use the MODIS Datasets.

For chlorophyll, NO2 and CO Datasets we use GIBS.

We have used the USGS earthquake data to create the earthquake visualiser where the user can query over a period and can arrange them according to magnitude or according to time.

For the Aerial labels we use the bing map data and for tectonic plates we use the tectonic plates layers.

The Hackathon Journey was amazing and it provided me immense pleasure for big part of such a great Hackathon , as it has been organized by NASA .

How We Developed This Project

After seeing some sample sites provided by the Challenge resources, we realised we can build an upgraded version of these sites, keeping the overall view concise but informative and interactive at the same time. Basically we aimed at serving the huge data within your grip.

But first we needed to create the globe, so we used nasa worldwind for it.

Tech stack used:

->Python

->Javascript

->Flask

->HTML

->CSS

->Bootstrap

->jQuery templates

->requireJS

->plotlyJS

->Heroku

Then we use HTML and Javascript for making the frontend. At first for the globe we generate it using worldwind web.

For styling we use Bootstrap templates and CSS. We also used jQuery for seamless loading of the Worldwind globe. We use requireJS for some cdn functionalities.

For backend we choose python and flask microframework, as its easy to create and powerful at the same time.

Then we deploy our project with the help of heroku. We create an automatic deploy or CI/CD (Continuous Integration and Continuous Delivery) for our master branch. Which means whatever changes we push to the master branch get deployed in the site hassle free.

The problems which we faced are making the website interactive for every users and breaking down the data to small sections along with the entire data to be served at the same site.

We have solved this problem to certain extent. The only thing left for us to do is to add more data and some query parameters.

For Datasets and Resources:

1) NEO: https://neo.sci.gsfc.nasa.gov/

https://earthdata.nasa.gov/collaborate/open-data-services-and-software/api

https://search.earthdata.nasa.gov/search

https://cdn.earthdata.nasa.gov/eui/latest/docs/

https://neo.sci.gsfc.nasa.gov/wms/wms?SERVICE=WMS&REQUEST=GetCapabilities&VERSION=1.3.0

2) USGS: https://en.wikipedia.org/wiki/United_States_Geological_Survey

https://www.usgs.gov/natural-hazards/geomagnetism/science/webservice?qt-science_center_objects=0#qt-science_center_objects

https://earthquake.usgs.gov/earthquakes/map/?extent=14.26438,-125.94727&extent=56.51102,-64.07227 (this is a sample extent shown here)

https://earthquake.usgs.gov/earthquakes/feed/v1.0/geojson.php

3) MODIS: https://en.wikipedia.org/wiki/Moderate_Resolution_Imaging_Spectroradiometer

https://lpdaac.usgs.gov/products/mod11a1v006/

http://dx.doi.org/10.5067/TERRA/MODIS_OC.2014.0

http://dx.doi.org/10.5067/TERRA/MODIS_OC.2014.0

4)Landsat:https://en.wikipedia.org/wiki/Landsat_program#:~:text=The%20Landsat%20program%20is%20the,launched%20on%20February%2011%2C%202013.

https://earthexplorer.usgs.gov/inventory/documentation/json-api

5) WorldWind Web:https://en.wikipedia.org/wiki/NASA_WorldWind

https://link.springer.com/article/10.1186/s40965-017-0016-5

For techstacks we used:

1) python: https://www.python.org/doc/

2) Boostrap: https://getbootstrap.com/docs/4.5/getting-started/introduction/

3) jQuery: https://api.jquery.com/

4) heroku: https://devcenter.heroku.com/categories/python-support

#lordsEye #spaceappstwentyone #oneappforyou

This project has been submitted for consideration during the Judging process.