Ghostbusters 2.0| Quiet Planet

Quiet Planet

The COVID-19 outbreak and the resulting social distancing recommendations and related restrictions have led to numerous short-term changes in economic and social activity around the world, all of which may have impacts on our environment. Your challenge is to use space-based data to document the local to global environmental changes caused by COVID-19 and the associated societal responses.

KROWNOS

Summary

Krownos is a social network that focuses in trace and predicts how human activities changing environmental factors using two main sources the human activity shared in the platform what we call komunes which is interpreted based in how good is for the environment applying human activity recognition and the space data that allow us to know the performance of the user's city.The idea is to create a different kind of challenge across different countries that will help to sustain the decrease of greenhouse gas emission and other human factors that change the environment.

How We Addressed This Challenge

Latin America is well known for the countries in the development and the microclimates based on human activities and the urban morphologies that decrease the comfort for the people that live in their cities.

After the quarantine start in the middle of March, it's visible using the space data, that the rate of greenhouse gas emission is been decreased compared to the last 5 years, in the Panam City. Compared to other cities without quarantine like Managua in Nicaragua, that remains the same also bigger than before in the increased loop.

So the social effect of the COVID-19 is helping to decrease the Carbon footprint but when the quarantine ends its gonna be the same that be before, or maybe worse. 

So we propose the creation of a challenge that measures how good a city of the country is dealing with their carbon footprint, using a social network that will trace the human activity and will relate with the Space-data of the emissions differents cities across the world staring with Central America. 

How We Developed This Project

When we see the actual data of the NO2 emission and CO2 emission compared from the last five years, we see that we are in a better position than before, but we the humans will not stay quiet for long so. We decide to measure the performance of all the individuals using what the like the most, share what they are doing. 

With this share, it can be text, video, or image, we well before human activity recognition using models based on tensor flow and keras. 

Also, every user of the system is related to city, that will perform as well as the data that we extract from satellites. 

The issue is that the dataset is huge, and we can follow many variables so we pick an as  good start point

rainfall information and NO2 information, cloud, and radiation information, all with their respective coordinated.

We run out of time in the end, to create the main challenge, but the users that share information in the systems, are already been part of the human activity recognition models, and every hour the system is been updating its data, what it's less than what we do from start. 


Project Demo
Project Code
Data & Resources

1. Nickolay A. Krotkov, Lok N. Lamsal, Sergey V. Marchenko, Edward A. Celarier, Eric J.Bucsela, William H. Swartz, Joanna Joiner and the OMI core team (2019), OMI/Aura NO2 Cloud-Screened Total and Tropospheric Column L3 Global Gridded 0.25 degree x 0.25 degree V3, NASA Goddard Space Flight Center, Goddard Earth Sciences Data and Information Services Center (GES DISC)

2. NO2 and CO2  information https://aura.gesdisc.eosdis.nasa.gov

3. JAXA GSMaP (Global Satellite Mapping of Precipitation)

Tags
#airquality #prediction #social network #carbon footfootprint #reiforcement learning
Global Judging
This project was submitted for consideration during the Space Apps Global Judging process.