# Street View Data Collection Design for Disaster Reconnaissance

arXiv: https://arxiv.org/abs/2308.06284 · HTML: https://mlsanity.com/paper/2308.06284/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2023.

## Built on by (most cited first, 2 of 2)

- [Recov-Vision: Linking Street View Imagery and Vision-Language Models for Post-Disaster Recovery](https://mlsanity.com/paper/2509.20628/) (2025)
- [Open-source data pipeline for street-view images: a case study on community mobility during COVID-19 pandemic](https://mlsanity.com/paper/2401.13087/) (2024)

## Abstract (arXiv)

Over the last decade, street-view type images have been used across disciplines to generate and understand various place-based metrics. However efforts to collect this data were often meant to support investigator-driven research without regard to the utility of the data for other researchers. To address this, we describe our methods for collecting and publishing longitudinal data of this type in the wake of the COVID-19 pandemic and discuss some of the challenges we encountered along the way. Our process included designing a route taking into account both broad area canvassing and community capitals transects. We also implemented procedures for uploading and publishing data from each survey. Our methods successfully generated the kind of longitudinal data that can be beneficial to a variety of research disciplines. However, there were some challenges with data collection consistency and the sheer magnitude of data produced. Overall, our approach demonstrates the feasibility of generating longitudinal street-view data in the wake of a disaster event. Based on our experience, we provide recommendations for future researchers attempting to create a similar data set.