# SmartWilds: Multimodal Wildlife Monitoring Dataset

arXiv: https://arxiv.org/abs/2509.18894 · HTML: https://mlsanity.com/paper/2509.18894/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 11 references that are in the map.
- First posted on arXiv 23 Sep 2025.

## Builds on (most cited first, 4 of 4)

- [Seeing biodiversity: perspectives in machine learning for wildlife conservation](https://mlsanity.com/paper/2110.12951/) (2021)
- [BuckTales : A multi-UAV dataset for multi-object tracking and re-identification of wild antelopes](https://mlsanity.com/paper/2411.06896/) (2024)
- [MammAlps: A multi-view video behavior monitoring dataset of wild mammals in the Swiss Alps](https://mlsanity.com/paper/2503.18223/) (2025)
- [The PanAf-FGBG Dataset: Understanding the Impact of Backgrounds in Wildlife Behaviour Recognition](https://mlsanity.com/paper/2502.21201/) (2025)

## Abstract (arXiv)

We present the first release of SmartWilds, a multimodal wildlife monitoring dataset. SmartWilds is a synchronized collection of drone imagery, camera trap photographs and videos, and bioacoustic recordings collected during summer 2025 at The Wilds safari park in Ohio. This dataset supports multimodal AI research for comprehensive environmental monitoring, addressing critical needs in endangered species research, conservation ecology, and habitat management. Our pilot deployment captured four days of synchronized monitoring across three modalities in a 220-acre pasture containing Pere David's deer, Sichuan takin, Przewalski's horses, as well as species native to Ohio. We provide a comparative analysis of sensor modality performance, demonstrating complementary strengths for landuse patterns, species detection, behavioral analysis, and habitat monitoring. This work establishes reproducible protocols for multimodal wildlife monitoring while contributing open datasets to advance conservation computer vision research. Future releases will include synchronized GPS tracking data from tagged individuals, citizen science data, and expanded temporal coverage across multiple seasons.