“My research work primarily addressed the limitation of current approach in studying landscape preferences by using advanced data science techniques. As a part of this work, a novel framework is created for identifying urban green stormwater storm water infrastructure (GI) designs (wetlands/ponds, urban trees, and rain gardens/bioswales) from high-resolution Google Earth images using state of the art computer vision and machine learning methods. The GI identification framework was also validated as an approach to for collecting landscape preference data for towards improving the understanding of what specific features are most desired. Previous research has shown that high-preference green settings are correlated with improved human health and wellbeingwell being. We also further curated social media data using Twitter, Flickr, and Instagram to analyze GI preferences using qualitative codebook analysis and natural language processing techniques. The models and findings are implemented as Brown Dog services allowing others to leverage these tools as opposed to having to re-implement these capabilities within their research when using similar datasets”
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