The CITIES’ Post-graduation Research Fellowship in CITIES at NYU Abu Dhabi is designed to support exceptional NYUAD graduating seniors with a demonstrated interest in academia, by providing a
prestigious one-year fellowship. This highly competitive Fellowship Program aims to retain outstanding
academic talent within the region, and contribute to the growth and development of research in the
UAE. It is an excellent opportunity for students to explore research directions and graduate school
possibilities, specifically in the area of urban science, spanning disciplines from Engineering, through
Computer Science, Sociology, History and the Arts.
The aim of this multidisciplinary study is to develop approaches to optimize the material and texture of concrete seawall bricks that are being increasingly used to protect cities against heavy waves, sea storms, and rising sea levels in order to enhance the diversity and abundance of intertidal organisms. Natural shorelines are increasingly replaced with the artificial infrastructure needed to support growing economies and populations. This coastal urban development brings additional pressures on the marine fauna inhabiting the coastal zone. In this study, the changes in the intertidal inhabitant’s coverage and composition over time will be tracked to understand which treatment (material and surface texture) best enhances marine biodiversity. This might potentially help to develop more sustainable urban shorelines that mitigate the ecological effects of coastal urban infrastructure and strengthen local biodiversity.
The purpose of this study is to quantify and analyze the predictive power of models trained solely on global open datasets versus those trained on global and local datasets for predicting urban changes. Such a study bears significance on an international level, for non-governmental organizations, governments as well as intergovernmental organizations given the increasing rates of urbanization in developing regions and the existence of a prominent data divide. It is a step towards answering the larger question “How do insights from open global datasets aid developing cities and countries? Can they help address the development gap or will the data divide contribute to it?”. In order to achieve the goal of this study, this research aims to train and compare models on datasets available globally, specifically satellite imagery by the European Space Agency’s Sentinel II, the voluntary data initiative by OpenStreetMap, and the Twitter API, against models trained on local granular datasets in addition to these globally available datasets. It will compare and quantify the difference in accuracy of these models in predicting the spatio-temporal changes in urban environments.