Hello @robbiemp  

My method may not be the best at the moment, but I’ll share the results from my previous tests. Here’s what I’ve tested: “DIY using SDR connected to a smartphone as a radio telemetry receiver.”

I have a VHF receiver that works with SDR and an Android smartphone. I’ve tested it with a VHF tag that I built myself, "My VHF Telemetry Tag Building Project From Scratch."

It depends on whether you need data or just audio. If you need data, SDR may not perform very well. But if you’re just after audio signals, it can work similarly to a regular VHF receiver. By using a Yagi antenna and connecting the SDR to the smartphone, it can work for any frequency range you want. I used 148-151 MHz, but you can use more than that.

Please understand that it works similarly to a commercial VHF receiver, but it may not be as good as the ones available in the market due to various limitations. However, it can still be used. I tested it with a Yagi antenna that I made myself (but if you already have an antenna, you can use it too), and I was able to detect my VHF tag from a distance of about 1.2 kilometers and 800 meters for the VHF tag I received from @Rob_Appleby  . This is just a rough test.

If you need a receiver that can operate across a wide range of frequencies, I think the RTL-SDR would be a good option as well.

This may not be the best method, but it works just fine. Thank you, and if you have any questions, don’t hesitate to ask me.

A good candidate for low power hibernation and processing power is the Teensy 4.1 from PJRC, which is an ARM Cortex M7. standard clock is 600 MHz and there is provision for 16 MB PSRAM. It hibernates at 0.1 ma. What is more important than the specs, there is a very active (the best IMHO) community Search | Teensy Forum with direct connection to the developer (Paul Stoffregen). For a simple recorder consumption is 50% higher than RP2040 (Teensy running at 24 MHz and RP2040 running at 48 MHz, but RP2040 is M0+ and not a M7). 

Hi Raza,

I manage a lot of snow leopard camera trap data also (I work for the Snow Leopard Trust). I am not aware of any AI solutions for snow leopards that can do all the work for us, but there are 'human-in-the-loop' approaches, in which AI suggests possible matches and a human makes the actual decision. Among these, are Hotspotter and the algorithm in WildMe. 

You can make use of Hotspotter in the software TrapTagger. I have found this software to be very useful when identifying snow leopards. Anecdotally, I think it improves the accuracy of snow leopard identifications. But, like I said, you still have to manually make the decisions; the results from Hotspotter are just a helpful guide.

The other cutting edge solutions mentioned here (e.g. MegaDescriptor, linked above) will require a massive dataset of labelled individuals. And considerable expertise in Python to develop your own solution. I had a quick look at the paper at they were getting around 60-70% accuracy for leopards, which is a much easier species than snow leopard. So I don't think this approach is useful, at least for now. Unless I've misunderstood something (others who deeply understand this, please chime in and correct me!).

Incidentally, I did try to gain access to WildMe / Whiskerbook last year but wasn't successful gaining an account. @holmbergius can you help me out? That would be appreciated, thanks!

Best of luck Raza, let me know if I can help more,

Ollie

An example of Hotspotter doing quite a good job, even with blurry images, to successfully draw attention to matching parts of the body. This is a screenshot from TrapTagger, which very helpfully runs Hotspotter across your images.

Note that I wouldn't necessarily recommend ID'ing snow leopards from such poor imagery, but this just demonstrates that Hotspotter can sometimes do quite well even on harder images.

Hi Raza,

As @ollie_wearn suggests, if think traptagger will be the easiest for you: 

Read " Sign In - TrapTagger " on

Sign In - TrapTagger

You just have to follow the tutorial there:

Read "TrapTagger Tutorial - YouTube" on YouTube

TrapTagger Tutorial - YouTube

The full TrapTagger tutorial. It is recommended that you start here, and watch through all the videos in order. You can then revisit topics as needs be.

YouTube

The person in charge of Traptagger is also very responsive.

best,

Great resources being shared! Darwin Core is a commonly used bio-data standard as well.  

For bioacoustic data, there are some metadata standards (GUANO is used by pretty much all the terrestrial ARU manufacturers). Some use Tethys as well.

Recordings are typically recorded as .WAV files but many store them as .flac (a type of lossless compression) to save on space. 

For ethics, usually acoustic data platforms with a public-facing component (e.g., Arbimon, WildTrax, etc.) will mask presence/absence geographical data for species listed on the IUCN RedList, CITES, etc. so that you're not giving away geographical information on where a species is to someone who would use it to go hunt them for example. 

Hello, I am experienced in conservation data strategy. If you want to have a conversation you can reach me at SustainNorth@gmail.com.

Bird Monitoring Data Exchange is a standard often used for birds data.