My name is Vinay (Linkedin profile) and I have just started a company in Biodiversity conservation from Bangalore, India.
Brief about Urvara.Life
"Earth's biodiversity - our strongest defence against climate change - is vanishing at an alarming rate. Current financial flows are accelerating biodiversity loss. For every $1 we invest in protecting nature, we spend $35 degrading it. At Urvara.Life, we're exploring a radical solution: what if we could convert biodiversity into an investable asset class? Imagine a world where you could IPO a forest, where preserving nature generates competitive returns! We're starting with the foundation: developing a cutting-edge Biodiversity MRV (Measurement, Reporting & Verification) platform that combines eDNA, Bioacoustics, and Camera traps to measure ecosystem health – to make nature’s value visible and tradeable."
What do I seek?
- I am currently with Center for Wildlife studies (CWS) India on executing 10 pilot projects in India - using multiple technologies like eDNA, Bioacoustics and Camera Traps. I need specific help on the post-processing of Camera trap images.
- I am looking for a solution that is trained on Indian images for automated processing of images captured via camera traps.
- I looked up your Wildlabs website and found
- Megadetector for broadly classifying correct and blank images
- AddaxAI - camera trap image analysis with AI species recognition models based around the MegaDetector model
- CameraTrapAI - AI models trained by Google to classify species in images from motion-triggered wildlife cameras
- Wanted your professional opinion on which is the best tool available for Indian images with high precision and recall.
- And what is the best way to integrate these tools into my workflow?
8 April 2025 6:29pm
[Full disclosure: this question was also posted to the AI for Conservation Slack, and I'm copying and pasting my answer from there to here.]
The first thing I would recommend is putting a very fine point on what you mean by "automated". There are close to zero cases where camera trap image processing is fully automated in the sense of every image being classified to species level without human intervention, and the cases where that happens tend to be pretty simple cases with a small number of very stable cameras, or a very small number of species (e.g. in semi-captive environments). That doesn't mean automation is impossible, but total automation with 100% accuracy is *probably* impossible, so you have to pick the compromises you're comfortable with. I recommend thinking about what you care about most and how much human time you can afford to get there... e.g., maybe you want to make sure you achieve 90% recall on species x/y/z, and you don't care what happens to the other species, and you can have humans spending k hours per month on image review.
Once you have goals that are as quantitative as possible and you're focused on efficiency, rather than total automation, it becomes easier to evaluate existing systems, whether or not they are even aware of your specific species (this is not always a requirement for an AI system to help you meet your goals, as long as that AI system has seen species that are visually similar to your species).
To help people get pointed in the right direction wrt choosing a system, I usually start with this series of questions:
http://lila.science/camera-trap-questions
Maybe those questions are useful just to get you thinking about your system, but if you want to talk through the implications of some of the answers, feel free to reply here or to email me at [email protected] .
Dan Morris