The leading with AI, was an idea ignited from the "leading with Artificial intelligence lab", sponsored by GIZ, led by Global leadership academy (GLAC) and ITCILO. The blog started in 2020.
The AI leadership academy is an initiative aiming to inspire articles from different topics. we want to spread the knowledge that enables different stakeholders from different sectors to play a role in leading sustainable development with AI.
Each session is no longer than 1 hour allowing the knowledge sharing as well as discussion.
In the thrive to achieve the goal, the founders, have invited Nishant Yadav to share his experience in the field of AI and Environment.
Topic Summary: Urban air quality (AQ) estimation is critical for devising air pollution mitigation strategies. Traditional methods rely on AQ monitoring stations on the ground and statistical models. However, many of the top polluted cities are in developing regions that may lack adequate cover-age of such stations. Advances in machine learning (ML) combined with the availability of high-resolution satellite imagery at a global scale offer an alternative solution. Yet, generalization to data-poor regions remains a challenge. Here we propose a novel ML modeling approach combining elements of domain-adversarial transfer learning and semi-supervised learning for AQ mapping over developing cities lacking sufficient training data. We show that models trained on data-rich cities such as Los Angeles and New York can be transferred to cities such as Accra in Ghana, Africa, with a low RMSE. This work demonstrates the utility of ML meth-ods in deriving predictive information from satellite imagery over regions with limited ground data, suggesting many potential applications across scientific domains.
Speaker's Biography: Nishant Yadav is a 3rd year Ph.D. student in Interdisciplinary Engineering at Northeastern University, Boston, US. His research is at the intersection of machine learning and environmental engineering, focusing on applying computer vision methods to satellite imagery for deriving predictive insights. As part of his Ph.D., he is currently interning at NASA Ames Research Center - where his research includes developing high-resolution air quality (AQ) maps (using satellite imagery) for regions lacking adequate ground station networks. At other times, discussions on making artificial intelligence (AI) more explainable and equitable keep him excited.
We Look forward to seeing you there to share the knowledge and lead with AI.
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