Project UMEED

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Project UMEED aims to deliver a quality solution to the ongoing and potentially irregular urban

development problem in Karachi, Pakistan, and various other cities in the world. Our program is equipped to predict city expansion and

development areas, while taking note of different variables such as forest cover, soil

quality, pollution, and topography—ultimately addressing 10 key solutions to establish better urban

planning—and fixing the problem that inspired us to create this solution in the first place.

As inspired by the current NASA SpaceApps theme“The Power of Ten”, the project is focused on proving

the following 10 solutions:

1. Adequate Settlements

2. Consistent Transportation

3. Sustainable Urbanization

4. Protection of heritage

5. Protection from disasters

6. Air Quality and Waste Management

7. Open public spaces

8. Clean Water and Sanitation

9. Affordable and Clean Energy

10. Good Health and Well-being

In addition, we want to emphasize the fact that some cities, specifically Karachi in this case, do not have

much data on disaster-prone areas. We believe that urban risk planning and disaster resilience are the

backbones of any city. After all, it doesn’t matter how many buildings you erect, if people didn’t plan them

against disasters, it will all come crashing down.

Project UMEED hopes to lessen this danger by including risk-planning in its program and providing

everyone a medium that allows them to easily access its contents without paywalls or data crunching, as

well as giving opportunities for public and private sectors to develop the city to become safer and more

resilient.

Our Project, which currently is in Pilot Stage, uses the service of Google Earth to display and visualize its datasets due to several limitations, including the KMZ file for our project which requires to be downloaded and opened in the Google Earth Pro Desktop Application We plan to transfer our project to a Web Application afterward, accessible to any device, with its own standalone domain without the use of any third-party service for the sake of Accessibility.

The project was created with the help of the following programs and sources:

Data sources, collection, and sorting:

1. NASA Earthdata (for data)
2. NASA SEDAC (for data)
3. QGIS (for collection and sorting of data)

To predict future Urban Expansion Probabilities:

1. MOLUSCE Plugin

For the A.I. Model:

1. Python

For the website and web app:

1. HTML
2. CSS
3. JavaScript

• Landsat 8/7 Infrared Surface Reflectance from NASA Earthdata Worldview

1. For identifying areas with forest, trees and vegetation

• NASA ASTER Global Digital Elevation Model

1. For determining slope-specific variables for our AI Algorithm
2. For predicting Landslide Risk
3. For predicting Earthquake Risk

• NASA EOSDIS Calibrated Radiance and Solar Radiance

1. For determining the Solar Radiation received by Karachi
2. To determine cooler areas specific variables for our AI Algorithm

• NASA Terra/MODIS Aerosol Optical Depth

1. For making the air pollution layer
2. To use as an environmental variable in our AI Algorithm

• NASA AIRS/IR (AQUA) Carbon Monoxide

1. For making the air pollution layer
2. To use as an environmental variable in our AI Algorithm

• NASA MODIS Corrected Reflectance Imagery
• NASA VIIRS Corrected Reflectance Imagery
• NASA MODIS Chlorophyll-a Concentration

1. For making the Ocean Pollution Layer

• ESA Copernicus Human Settlement Layer

1. For obtaining 2015 and 2019 population and urban datasets
2. For predicting the 2035 layer with AI Algorithm

While considered as one of the world’s megacities, Karachi, Pakistan—the pilot area of this project, as well

as my hometown—is infamous for its urban planning, or at least its lack thereof.

Since I was a kid, I have seen a variety of events that have proved that urban management is the most

important part of city maintenance and future prosperity, in Karachi and other cities of the world. Karachi

has been the victim of poor urban planning and ad hoc governing since the Partition of India, wherein 75%

of the 340,000 Muslims who migrated to Pakistan headed to Karachi due to its business opportunities.

Since then, the population boom caused urban planning and development to go haywire.

Construction and land development has continued to be largely uncoordinated in the city, often creating

satellite towns disconnected from the city, as well as leapfrogging vacant lots. I have experienced the

various Monsoon storms and intense rainfall that devastated Karachi over the years. These disasters had

destroyed our roads, buildings, caused thousands of deaths, and cost the city millions in damages each

year.

After some research, I found out that cities with proper urban management tend to remain safe from such

events. And thus, along with help from my teammates, I had determined myself to help my city—and

eventually all the cities of the world—become properly planned and managed urban centers that mankind

can make the most out of.

We used various forms of geospatial data from publicly accessed sources and websites such as NASA

Earthdata, NASA SEDAC, and other Space Agency Data Repositories, gathered and collected them in

hierarchical layers using GIS Software and used satellite imagery from various past years in an AI

Algorithm that helped us determine future probabilities for urban expansion.

Then, we developed our own AI Model using Python and added other variables to consider such as Forest

Cover, Soil Quality, Pollution, and Topography of the area to predict a recommended visualization of how

and where to develop and expand our cities, ultimately addressing the 11th SDG, “Sustainable Cities And

Communities” and fixing our problem that inspired us to create the Solution. We then developed KMZ files to be viewed in Google Earth for our web-

app using HTML, CSS, and JavaScript Plugins to visualize all the data we gathered in a publicly-accessible

and user-friendly way.

Several setbacks have hindered us in the process of making this project, namely:

1. Lack of GIS data from official sources in the area;

2. Lack of risk-specific layers like river networks, liquefaction prone areas, etc.;

3. Lack of Drone data;

4. Large dataset causing problems in the Web-app;

5. The selected city has no official repositories; and

6. Heavy lag during development due to data in GB’s.

We are proud to say that our best achievement is that we were able to complete our project on time and

have it judged by one of the world’s most prestigious space agencies. However, in addition to this, the

following achievements have been met by the team:

• Creating layers from scratch using different NASA sources because of the lack of local data.
• Obtaining sufficient data from NASA SEDAC, Earthdata, POWER, ESA, INPE, JAXA and various other public repositories that have rendered our project suitable to be implemented practically
• Creating KMZ files for our Pilot Project to be viewed by the Judges and the Public using Google Earth
• Creating basic skeleton of our Webapp
• Obtaining a domain and a website that could help visualize our project

• Landsat 8/7 Infrared Surface Reflectance from NASA Earthdata Worldview

1. For identifying areas with forest, trees and vegetation

• NASA ASTER Global Digital Elevation Model

1. For determining slope-specific variables for our AI Algorithm
2. For predicting Landslide Risk
3. For predicting Earthquake Risk

• NASA EOSDIS Calibrated Radiance and Solar Radiance

1. For determining the Solar Radiation received by Karachi
2. To determine cooler areas specific variables for our AI Algorithm

• NASA Terra/MODIS Aerosol Optical Depth

1. For making the air pollution layer
2. To use as an environmental variable in our AI Algorithm

• NASA AIRS/IR (AQUA) Carbon Monoxide

1. For making the air pollution layer
2. To use as an environmental variable in our AI Algorithm

• NASA MODIS Corrected Reflectance Imagery
• NASA VIIRS Corrected Reflectance Imagery
• NASA MODIS Chlorophyll-a Concentration

1. For making the Ocean Pollution Layer

• ESA Copernicus Human Settlement Layer

1. For obtaining 2015 and 2019 population and urban datasets
2. For predicting the 2035 layer with AI Algorithm

• OSM Pakistan GIS Data

1. For obtaining roads layer
2. For obtaining railways layer
3. For identification of Buildings and Structures

• QGIS

1. For sorting and organizing layer downloaded from NASA and Space Agencies
2. For making layers that were not available
3. For classification and extraction of data from layers
4. For exporting data to Google Earth Format

• MOLUSCE

1. For primary prediction of Human Settlement Layers
2. For obtaining AI Model type choice

• Google Earth

1. For viewing the final data

#urbandevelopment, #nasaearthdata, #nasasedac, #esacopernicus, #qgis, #drones, #satellites

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