Being confident in confidence scores: calibration in deep learning models for camera trap image sequences
Problem
Automated deep learning classifiers for camera trap images produce confidence scores that may not be interpretable as true probabilities, limiting their use in ecological tasks like occupancy estimation.
Why it matters
Ecologists process camera trap images at massive scale, and unreliable confidence scores could distort wildlife population estimates.
What the AI did
The study investigates whether deep learning species classifiers on a large European camera trap dataset are well calibrated, including predictions aggregated across image sequences, and tests a post-hoc temperature scaling method.
Results
not stated in source
Impact
The work could make automated species identification more trustworthy for downstream ecological tasks.
Record
| Venue | Remote Sensing in Ecology and Conservation |
|---|---|
| Published | 2024 |
| Work type | article |
| Authors | Gaspard Dussert, Simon Chamaillé‐Jammes, Stéphane Dray, Vincent Miele |
| Citations | 29 (OpenAlex, at import time) |
| References | 45 |
| Model or method | deep learning species classification models; temperature scaling (abstract truncated) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1002/rse2.412 |
| Open access | gold |
| Keywords | camera traps, deep learning, calibration, species classification, confidence scores |
Importance rating
real but narrower advance
Calibration affects reliability of camera trap species classification for ecological tasks like occupancy estimation; niche methodological advance, no scale quantified.