The MLAI Meetup is a community for AI researchers and professionals which hosts monthly talks on exciting research. Our format is:
6:00 - 6:20: Socializing
6:20 - 6:40: Announcements and AI news
6:40 - 7:40: Talk(s) and Q&A
7:40 - 8:00 Networking
8:00: Head to the nearest pub for dinner
Shinjita Das: When Maps Aren’t Enough: Using Spatial AI to Measure the Green We See
Talk description: A tree can cool a street, appear clearly on a map – and still be completely invisible from someone’s window. That gap between what exists in a city and what people actually see and experience has shaped much of my research journey. When I first started working with geospatial science, I was fascinated by the idea that spatial data could reveal patterns in cities that are difficult to notice on the ground. During my Master’s research, I used deep learning to detect individual urban trees and examined their contributions to urban cooling. That was my first real encounter with machine learning, and it changed the way I thought about maps: they were no longer just tools for showing where things are, but platforms for asking what else we could learn from the environment.
That curiosity led me further into GeoAI. If machine learning could help identify trees and quantify their environmental role, could similar computational approaches help us understand how people actually experience greenery? This question followed me into my PhD, where my toolkit expanded from 2D GIS and spatial statistics to 3D city modelling, computer vision and emerging GeoAI approaches. At the same time, the research question became more human. Instead of simply asking “Where is the green?”, I began asking, “Who can actually see it and how does the view impact mental health?”
In this talk, I’ll trace that journey through examples from my research: from detecting trees and measuring their cooling effects to reconstructing urban environments in 3D and modelling greenery from the perspective of apartment resid