This is the official website for the Dense Street Imagery (DSI) academic project from Cornell Tech. Built with Astro, the site showcases our research on visual data collection from "digital eyes on the street" including dashcams and autonomous vehicle systems.
Dense Street Imagery (DSI) represents a breakthrough in visual urban data collection, combining advanced vehicle hardware, imaging technology, and networking capabilities to create dynamic, real-time depictions of city environments. Unlike traditional static snapshots from services like Google Street View, DSI leverages temporal density through networked dashcams and driver-assist systems to deliver fresh, continuous imagery at unprecedented frequency.
This project is under review for FAccT '25: ACM Conference on Fairness, Accountability, and Transparency.
All commands are run from the root of the project, from a terminal:
| Command | Action |
|---|---|
pnpm install |
Installs dependencies |
pnpm dev |
Starts local dev server at localhost:4321 |
pnpm build |
Build your production site to ./dist/ |
pnpm preview |
Preview your build locally, before deploying |
pnpm astro ... |
Run CLI commands like astro add, astro preview |
pnpm astro --help |
Get help using the Astro CLI |
This website is built using Astro, a modern static site generator that delivers excellent performance by shipping minimal JavaScript.
The site uses TailwindCSS for styling, a utility-first CSS framework that enables rapid UI development.
The website features several custom components:
- Stats highlight section showing the scale of digital eyes data
- Application cards for DSI use cases
- Comparison section between DSI and traditional street imagery
- Team member display grid with responsive layout
/src/pages/- Page templates including the main index/src/components/- UI components organized by function/src/layouts/- Layout templates for consistent page structure/public/- Static assets like team member images and logos
- Matt Franchi - Computer Science PhD Candidate
- Hauke Sandhaus - Information Science PhD Candidate
- Madiha Zahrah Choksi - Information Science PhD Candidate
- Severin Engelmann - Digital Life Initiative Postdoctoral Associate
- Wendy Ju - Associate Professor
- Helen Nissenbaum - Professor / Director of Digital Life Initiative