Machine Learning for the Fusion of Remote Sensing and Tweets Data for Green Space Analysis
Mohamed Ibrahim (Ph.D. Student)
There is increasing evidence that people with higher access to urban green spaces have better mental health and well-being. This project aims to examine the impact of urban green spaces on mental health, and what features play the biggest role. The combination of airborne or space-borne remote sensing images and geo-tagged social media data can enhance the measurement of the quantity and quality of urban green spaces. Remote sensing imagery can allow us to identify and classify green spaces. Social media data can represent human activity near the identified green spaces. We hope to gain insight into qualitative characteristics of urban green spaces that could be useful for city planners and has a positive impact on citizens' mental health.
|Primary Host:||Xiaoxiang Zhu (Technical University of Munich)|
|Exchange Host:||Devis Tuia (EPFL)|
|PhD Duration:||01 October 2019 - 30 September 2023|
|Exchange Duration:||01 September 2020 - 31 December 2020 - Ongoing|