Human inspired deep learning to locate and classify terrestrial and arboreal animals in thermal drone surveys
Abstract Drones are an effective tool for animal surveys, capable of generating an abundance of high‐quality ecological data. However, the large volume of ecological data generated introduces an additional problem of the requisite human resources to process and analyse such data. Deep learning model...
Saved in:
| Main Authors: | Kal Backman, Jared Wood, Maquel Brandimarti, Chad T. Beranek, Adam Roff |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-06-01
|
| Series: | Methods in Ecology and Evolution |
| Subjects: | |
| Online Access: | https://doi.org/10.1111/2041-210X.70006 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Comparing the cost‐effectiveness of drones, camera trapping and passive acoustic recorders in detecting changes in koala occupancy
by: Chad T. Beranek, et al.
Published: (2024-07-01) -
Motion feature extraction using magnocellular-inspired spiking neural networks for drone detection
by: Jiayi Zheng, et al.
Published: (2025-01-01) -
Hybrid Swarm Intelligence and Human-Inspired Optimization for Urban Drone Path Planning
by: Yidao Ji, et al.
Published: (2025-03-01) -
Survey on Anti-Drone Systems: Components, Designs, and Challenges
by: Seongjoon Park, et al.
Published: (2021-01-01) -
DroneSilient (drone + resilient): an anti-drone system
by: Meghna Manoj Nair, et al.
Published: (2024-10-01)