# UrBAN: Urban Beehive Acoustics and PheNotyping Dataset

arXiv: https://arxiv.org/abs/2406.03657 · HTML: https://mlsanity.com/paper/2406.03657/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2024.

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

- [Queen Detection in Beehives via Environmental Sensor Fusion for Low-Power Edge Computing](https://mlsanity.com/paper/2509.14061/) (2025)
- [BeeVe: Unsupervised Acoustic State Discovery in Honey Bee Buzzing](https://mlsanity.com/paper/2605.07903/) (2026)

## Abstract (arXiv)

In this paper, we present a multimodal dataset obtained from a honey bee colony in Montr'eal, Quebec, Canada, spanning the years of 2021 to 2022. This apiary comprised 10 beehives, with microphones recording more than 2000 hours of high quality raw audio, and also sensors capturing temperature, and humidity. Periodic hive inspections involved monitoring colony honey bee population changes, assessing queen-related conditions, and documenting overall hive health. Additionally, health metrics, such as Varroa mite infestation rates and winter mortality assessments were recorded, offering valuable insights into factors affecting hive health status and resilience. In this study, we first outline the data collection process, sensor data description, and dataset structure. Furthermore, we demonstrate a practical application of this dataset by extracting various features from the raw audio to predict colony population using the number of frames of bees as a proxy.