Yu-Hsuan (Viola) Ho

Ph.D. Student in Civil Engineering at Texas A&M University

[Ali Mostafavi ✨️ @Mostafavi_Ali]

📢Latest paper from our lab led by @mtesparza1224 unveils the importance of reliable lowest floor elevation data in improving flood damage estimates. Traditional flood damage assessment models often rely on flood depth as the primary predictor. Our latest research introduces an alternative approach by statistically testing elevation data—obtained through our Elev-Vision AI model—as a predictor of damage.
We applied this method to two super-neighborhoods in Harris County, Meyerland and Edgebrook, analyzing the impact of Hurricane Harvey (2017). Our findings reveal that property elevation data significantly enhances damage assessment, particularly at smaller spatial scales.
The results suggest that cities and floodplain managers can leverage property elevation data for more rapid and precise post-disaster damage assessments. The regression models confirm that this approach provides robust insights, offering a valuable tool for disaster response and resilience planning.

https://t.co/qlBFyjcrz8

[Ali Mostafavi ✨️ @Mostafavi_Ali]

The latest paper from our lab led by @ViolaYuHsuanHo presents ELEV-VISION-SAM Model: An Integrated vision language and foundation model for automated estimation of building lowest floor elevation! The model and findings not only contribute to advancing street view image segmentation for urban analytics but also provide a novel approach for image segmentation tasks for other civil engineering and infrastructure analytics tasks.

https://t.co/JIEzgnqsim

[Ali Mostafavi ✨️ @Mostafavi_Ali]

📢The latest preprint from our lab led by @ViolaYuHsuanHo presents our Elev-Vision-SAM model for enhancing automated estimation of the lowest floor elevation of buildings by integrating vision language and foundation models on street view imagery. #AI #ResilienceTech
https://t.co/YHRcR480Tk

[Ali Mostafavi ✨️ @Mostafavi_Ali]

Our latest preprint led by @ViolaYuHsuanHo presents ML4EJ: an interpretable machine learning model for decoding the role of urban features in shaping disparities in environmental hazard exposures!
https://t.co/hlG90vZ3Do

[Ali Mostafavi ✨️ @Mostafavi_Ali]

New preprint from our lab led by Viola Ho presents ELEV-VISION: an image segmentation model for automated lowest flood estimation using streetview images. The models is highly scalable to map LFE as a key info for flood vulnerability assessment @ResilientTX @TAMUCVEN