Predicting urban heat islands in Calgary
07-14, 10:30–11:15 (Europe/Dublin), Liffey Hall 2

This talk explains how geospatial Python libraries can help us understand and predict Land Surface Temperature in urban areas using historical openly available satellite images and urban morphological data. This makes data science a powerful tool to plan and design urban areas while reducing the impact of urban warming.


Dealing with extreme heatwaves can be challenging, it has become the necessity to understand the land surface temperature (LST) change and its driving factors to reduce the impact and achieve more sustainable planning methods for city growth.

This module will help you understand how to calculate LST from the openly available satellite imageries and merge it with urban morphological factors (like building height, building count, FSI, building block coverage, etc.) to predict the temperature trend and mitigate the impact.

We will demonstrate an end-to-end methodology using geospatial Python libraries to understand the use of spatial regression methods taking into account the variation over time. This talk will also throw light upon:

  • Getting the large imagery datasets into DL friendly format
  • Spatial aggregation of different variables
  • Understanding correlation between variables for feature engineering
  • Application & comparison of different regression methods on the same data
  • Future scope

We'll also showcase the geo-visualization portal we created and the technologies used, how you can use Python to convert large GeoJSON output to light vector tiles, and create a seamless experience for the user through an intuitive front-end.


Expected audience expertise: Domain

none

Expected audience expertise: Python

some

Abstract as a tweet

Geospatial Python libraries can help us predict land surface temperature in urban areas with openly available satellite images. Deep learning gives us a powerful tool to reduce the impact of urban warming.

Anand is a co-founder of Gramener, a data science company. He leads a team that automates insights from data and narrates these as visual data stories. He is recognized as one of India's top 10 data scientists, and is a regular TEDx speaker.

Anand is a gold medalist at IIM Bangalore and an alumnus of IIT Madras, London Business School, IBM, Infosys, Lehman Brothers, and BCG.

More importantly, he has hand-transcribed every Calvin & Hobbes strip ever and dreams of watching every film on the IMDb Top 250.

He blogs at https://s-anand.net. His talks are at https://bit.ly/anandtalks