High-Level Project Summary
Our cities are the frontier where the battle to achieve sustainability would be won or lost. This requires an evidence-based approach to local decision-making and resource allocation, which can only be possible if current gaps in urban data are bridged. We want to use Earth Observation to provide timely and spatially disaggregated information to solve the Urban Heat Islands problem with ecological redevelopment actions. Once the aforementioned areas have been identified, AI-based software can choose the most effective green solutions.
Link to Project "Demo"
Link to Final Project
Detailed Project Description
The problem: local environmental sustainability
We need an evidence-based approach to local decision-making and resource allocation to achieve environmental sustainability.
Urban data is fundamental to do so.
Our solution
We want to use Earth Observation to provide timely and spatially disaggregated information to solve the Urban Heat Islands problem with ecological redevelopment actions.
Our AI will take as input the necessary datasets (including heat maps and city plans) and criteria established by scientific studies on how the Urban Heat Island problem can be solved.
Once the areas and the necessary datasets and scientific criteria have been identified, our AI will choose the most efficient and effective green solutions for any specific area from that data.
Possible solutions include:
- Use of light-coloured concrete and white roofs
- Planting trees
- Green roofs
- Low Emission Zone (LEZ) implementation
Finally, the possible solutions will be instantly reported on an online dashboard, accessible by any public employee responsible for the environment.
Results
- Cleaner air
- Engine of cooling
- Reduced carbon footprint
- Reduced energy costs
- Better storm water management
- Lower level of stress
Space Agency Data
The focal point of our project is the data relating to cities. The NASA Global Urban Heat Island Data Set (CIESIN_SEDAC_SDEI_UHI2013) of 2013 helped us better analyze the phenomenon, as well as being able to act as a fundamental part of our project.
The Urban Heat Island (UHI) effect represents the relatively higher temperatures found in urban areas compared to surrounding rural areas owing to higher proportions of impervious surfaces and the release of waste heat from vehicles and heating and cooling systems. Paved surfaces and built structures tend to absorb shortwave radiation from the sun and release long-wave radiation after a lag of a few hours. The Global Urban Heat Island (UHI) Data Set, 2013, estimates the land surface temperature within urban areas in degrees Celsius (average summer daytime maximum and average summer nighttime minimum) as well as the difference between those temperatures and the temperatures in surrounding rural areas, defined as a 10km buffer around the urban extent. Urban extents are from SEDAC�s Global Rural-Urban Mapping Project, Version 1 (GRUMPv1), and land surface temperatures are from SEDAC�s Global Summer Land Surface Temperature (LST) Grids, 2013, which are derived from the Aqua Level-3 Moderate Resolution Imaging Spectroradiometer (MODIS) Version 5 global daytime and nighttime Land Surface Temperature (LST) 8-day composite data (MYD11A2). For most regions, the UHI data set provides the average daytime maximum (1:30 p.m. overpass) and average nighttime minimum (1:30 a.m. overpass) temperatures in urban and rural areas, and the urban-rural temperature differences, derived from LST data representing a 40-day time-span during July-August (Julian days 185-224) in the northern hemisphere and January-February (Julian days 001-040) in the southern hemisphere. LST grid cells with missing values resulting from high cloud cover in tropical regions were filled with daytime maximum and nighttime minimum LST values from April-May 2013 in the northern hemisphere and December 2013-January 2014 in the southern hemisphere, where available. Some data gaps remain in areas where data were insufficient (e.g., Central Africa).
Hackathon Journey
First Day
After the initial meeting (Saturday, 9:00), we instantly planned a meeting to discuss on problems of urban development that we could potentially solve through the use of drones and satellites.
After a couple of hours of brainstorming, we decided to proceed with our initial idea: solve the Urban Heat Islands problem.
We spent a few hours to better know the problem we were going to face and its possible solutions.
We also considered using AI to solve this problem: nowadays, AI is really powerful and it could be useful in evidence-based decision making.
Then, we discussed the possible solutions that our AI could suggest to the public employees: from LEZ to green roofs, and so on.
Second Day
Then, we split our tasks:
- Creation of the online dashboard design
- Creation of a Powerpoint layout from scratch
- Script of the presentation
- Search of datasets
- Search of images
We had the last meeting to discuss the final project. We really enjoyed working together to this project, and we strongly believe we deserve to pass the selection.
References
Introduction
We used some simple tools (such as Google Meet and PowerPoint) and some other advanced tools (i.e. Photoshop) in order to reach our goal: a simple and powerful solution.
Tools used
- Microsoft PowerPoint
- Adobe Photoshop
- Google Maps
- Open Street Map
- Google Meet
Tags
#Software #AI #UHI #EO #Urban #City #Satellites #Maps
Global Judging
This project has been submitted for consideration during the Judging process.