Steph, yes, I gathered as much myself, but I still wanted to put it on the agenda. It links to the bigger problem in nature conservation, and especially funding : all projects are required to be effective and successful, scalable, profitable and so on (not to mention the paper trail that applicants need to produce to prove it ). It makes sense from the donors' perspective, because if a project is not at least one of these, then it is a waste of money an how do they explain that to the public and the donors' donors. On the other hand, the IMHO ( H for honest ) over emphasis on this requirement is counterproductive and it does not fit in the world of tech development.

To start with the latter, as we all know, in the world of tech VC and startups, people are very used to the idea that a tech invention is not successful. I've read that if you haven't had two failed startups behind you, you're a better contender with your next one. 9 Out of 10 will fail and the remaining one will more than make up for the loss, is the idea. This way of looking at projects fits very well in this tech oriented forum.

Then the counter productive part : with a funding climate like that, who is going to take risks with tech development for nature conservation? Seriously, anybody reading this: if you raise your hand, please do get in touch, because I am curious how you look at the issue and how you deal with it. Whereas I do agree that nature conservation as a whole needs to be successful, I don't believe that the sponsors' stress on it for individual projects is successful itself. Nature's degradation has developed so far ( call me a pessimist ), that we need to take risks and find out if ( and hope that ) what works for silicon valley can work for nature.

Kudos for your attempt at the 'Technical difficulties'. I didn't know about it. Maybe we ( but I mean you, of course  ;-) can tinker with the formula. Ask people to talk about how they overcame problems in some detail, because then it is a success story again. ( They could earn a badge! ) And if the recording is an issue, then don't record, or work on anonymity with temporary accounts, TOR, voice scramblers and the camera off. Maybe people are willing to write about their failures instead of doing a live presentation. That way, one has absolute control over anonymizing individuals and hiding the project from being identified. Little risk of a slip of the tongue. 

Thanks for your thoughts on the importance of sharing more about failure in our unique sector. You bring up some very valid points @Frank_van_der_Most  and I think the perspective of 'learning by doing' is quite essential. I've shared below some stories I've covered from our East Africa community that touch on this and the perspective that has emerged has been on learning from these experiences and developing with more insight to solve conservation challenges more effectively. I hope you will find these interesting and useful.

Hi @Frank_van_der_Most,
great point and I will address this in my little talk next week.

Greetings from Austria,
Robin

LOL, GIFs make communication so much cooler!!! I wonder if we have a tech4wildlife GIF? That would be super cool! 

Amazing, very well put Talia! 

Thanks @Freaklabs, I think you'll really enjoy getting involved with this too as we're looking for input from makers in the community to get the most from the approach and to capture features and usability ideas from a large number of people.

I've a new modular drop-off tag build using @Rob_Appleby's original SensorDrop board that I think would be great for this project too to see if we can drop different compartments, or do various different timed events with the one TimeLord board.

Most importantly, we have to make it play a MIDI version of the DoctorWho theme song when you arm the device. That has to be the #1 feature if you ask me!

 

Reminds me that we should look at both terrestrial and marine applications when we get stuck in to the demo builds to make sure we cover use cases 

'Most importantly, we have to make it play a MIDI version of the DoctorWho theme song when you arm the device. That has to be the #1 feature if you ask me!' 

Seconded!

Would also add zamba and Zamba Cloud into the list of tools for running AI models (the former being with Python code, the latter being with a GUI). For the reader at home (since I know Dan knows), these are tools for species classification on camera trap videos. We've got pretrained models trained on over 250,00 videos covering 32 African forest species, 11 European species, as well as a blank / nonblank detector.

There is also the ability to train your own models for new species or locations. And we're super excited to extend this custom model training functionality to support camera trap images thanks to our WILDLABS award (more details here).

Thanks Dan for explaining and clarifying, and Ed and all others for contributing. As a somewhat technical person (IT business consultant) with no formal environmental education, but with a passion to understand and protect the environment, this is a topic I've been exploring and struggling with...

There are really skilled technical experts in this community that love to design and improve solutions, but it's not always easy for potential end users to understand and use said solutions without significant learning curves. And often solutions are designed from a technical perspective, and does not always consider end users' level of experience, knowledge and actual on the ground needs. This is not just a problem in the concervation technology space, but overall tech industry.

To clarify this for myself, I would love to see a summary of how conservation tech solutions fit into a camera trap process that include the following areas - and especially which tools fit where:

1. Plan/Design: scope, requirements, timeframes, data needs, overall outcome. Also influences the selection of tools to use in the overall process.
2. Capture/execute : both hardware (cameras, audio recorders, sensors, etc), and software (managing hardware lifecycle tracking, capturing trail camera data e.g. image file management, etc) solutions
3. Process/Analyse: solutions for initial processing/culling, detailed analysis, and anything classification related
4. Present/Display: how best to communicate, display and present the data gathered, analysed and classified...

If there is a resource that already do this comparison and sumarization of tools' capabilities please help me find it - otherwise it might be topic to be explored further...

Saul Greenberg is a great guy! He has made a few very useful videos regarding the Timelapse/Megadetector integration:

https://grouplab.cpsc.ucalgary.ca/grouplab/uploads/Publications/Publications/2021-05-ImageRecognition-Video.mp4

and a tutorial: 

https://saul.cpsc.ucalgary.ca/timelapse/uploads/Videos/Video-TimelapseImageRecognitionGuide.mp4

Not sure what happened to the excerpt I wanted to post. Apperently the Alaska study authors had some scepticism themselves.

I would be extremely worried about catching wrong species in many cases - with potentially detrimental effects.

Might very well be so! I highly encourage you to take up the challenge!