High-Level Project Summary
According to the Sustainable Development Goals, a call for action by all countries to promote prosperity while protecting the planet, we decided to create "Dragon air platform". A platform which is a tool for anyone interested in building in a smart and informed way and which will allow to map data locally, approaching a single problem with real data, via an OpenSource-based Quadcopter that allows us to do AIR POLLUTION REAL-TIME MAPPING.The complete end-to-end solution: Our Platform powered by a high accuracy predictive algorithm for regional and local data visualization + Open source Quadcopter + On-board Modular Sensing module for different types of pollution
Link to Project "Demo"
Link to Final Project
Detailed Project Description
Problems that were had with CONVENTIONAL DRONES AND OTHER RESOURCES:
The data that were had already exists and does not represent information as crucial for decision-making, although very specific, depending on of the given situation
Satellite data is often inaccurate and almost not on real time.
Unable to attack multiple issues at once with lack of data
You cannot attack different problems with the same data
Satellites provide us with trends and geolocation, but data in real time, it is almost impossible for an open source satellite, or public
We must attack a solution at least on the ATHMOSPERIC side (UAVS)
Problems of the chosen variable:
pollution
Both climate and pollution are dynamic, requiring near real-time capture.
There is not yet a generalized solution for the mapping of pollution parameters, nor for data management for risk management and decisionmaking; much less specialized training modules.
There is no precise data processing for the mapping and determination of risk areas with pollutants in large cities.
Satellite data do not have concrete models and data for “hyperlocal” zones, which are crucial for determining a construction zone, nor are they 100% accurate.
They are very general oriented to precise geographical areas
SOLUTION
Create a platform that coexists with what we already have.
An Unmanned Aerial Vehicle with an open-source platform equipped with a Sensing module that maps, acquires, processes and feeds the existing algorithm, for the most accurate data output in the case of air pollution
All this in order to collect the measurements through theUAS, analyze them, process them and throw them for visualization with the powerful algorithm that we already have to be able to see high precision data on the concentration of polluting particles in a specific given area, this information being critical. for stakeholders and their planning
DATA ACQUISITION AND DATA PROCESSING
Real-time data monitoring will be generated as well as update models that are constantly updated, in order to provide accurate information, multilayer mappings, mapping of Parts per Million of Carbon Dioxide, Sulfur Dioxide, Ozone, Carbon Monoxide, Dioxide Nitrogen and highly volatile particles, as well as their ratios with Pure Oxygen and Nitrogen, in addition to not only having an urban and planning purpose, but also for risk prevention and scientific studies
Sensing module
It will have capacity for 3 or more transducers, 1 for each type of pollutant
Modular, minimalist, made of lightweight materials
Control Stages and Integrated Data Processing Computers
Weight of 400 gr minimum, 600 gr maximum
Jupyter Script for Cities Sustentability Insight leveraging the GHS Urban Centre Database 2015
Space Agency Data
World view
Department of Economic and Social AffairsSustainable Development
Giovanni The Bridge Between Data and Science
The Earth Observations Toolkitfor Sustainable Cities andHuman Settlements
Urbanization and Human Settlements
Recursos del challenge DRONES AND SATELLITES FOR URBAN DEVELOPMENT
EO Dashboard
Hackathon Journey
We analyzed all the information we had so we could come with the best solution.
We had infinite calls and we learn from each other since we have different fields and expertise.
We defined the solutions and we all worked together to have the last result.
References
UAS:
Arduino,
Ardupilot,
PX4,
Proteus
Data visualization
OpenCV
Python
WebODM
QGIS
https://www.opendronemap.org/webodm/
https://www.qgis.org/es/site/https://earthdata.nasa.gov/
https://earthdata.nasa.gov/learn/pathfinders/sdg-data-pathfinders/sdg-11-data-pathfinderhttps://eo4sdg.org/wp-content/uploads/2021/01/EO_Compendium-for-SDGs-compressed.pdf
Data Analysis
Python,
pandas library,
matploblit library,
numpy library,
folium libary and
the GHS Urban Centre Database 2015
Mechanical Design
SolidWorks 2021
The British Geological Survey
Susdrain - The community for sustainable drainage
https://www.mdpi.com/1660-4601/17/1/108/htm
https://www.bgs.ac.uk/geology-projects/suds/https://www.bgs.ac.uk/download/guide-to-suds-for-developers/https://www.susdrain.org/https://www.sciencedirect.com/science/article/pii/S0303243420302555https://www.mdpi.com/1660-4601/17/1/108/htmhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4500194/https://www.apta.com/research-technical-resources/mobility-innovation-hub/first-last-mile-solutions/https://www.apta.com/research-technical-resources/mobility-innovation-hub/mobility-action-plans/https://crowdsourced-transport.com/working-pages/improve-public-transport/https://www.transportation.gov/mission/health/Expand-Public-Transportation-Systems-and-Offer-Incentives
UX and Design
Figma
Procreate
Imovie
A Illustrator
Team's Communication
Notion
Signal
Discord
Tags
#drones #uxplatform #urbandevelopment #smartcities
Global Judging
This project has been submitted for consideration during the Judging process.