With new technologies revolutionizing data collection, wildlife researchers are becoming increasingly able to collect data at much higher volumes than ever before. Now we are facing the challenges of putting this information to use, bringing the science of big data into the conservation arena. With the help of machine learning tools, this area holds immense potential for conservation practices. The applications range from online trafficking alerts to species-specific early warning systems to efficient movement and biodiversity monitoring and beyond.
However, the process of building effective machine learning tools depends upon large amounts of standardized training data, and conservationists currently lack an established system for standardization. How to best develop such a system and incentivize data sharing are questions at the forefront of this work. There are currently multiple AI-based conservation initiatives, including Wildlife Insights and WildBook, that are pioneering applications on this front.
This group is the perfect place to ask all your AI-related questions, no matter your skill level or previous familiarity! You'll find resources, meet other members with similar questions and experts who can answer them, and engage in exciting collaborative opportunities together.
Just getting started with AI in conservation? Check out our introduction tutorial, How Do I Train My First Machine Learning Model? with Daniel Situnayake, and our Virtual Meetup on Big Data. If you're coming from the more technical side of AI/ML, Sara Beery runs an AI for Conservation slack channel that might be of interest. Message her for an invite.
Header Image: Dr Claire Burke / @CBurkeSci
Explore the Basics: AI
Understanding the possibilities for incorporating new technology into your work can feel overwhelming. With so many tools available, so many resources to keep up with, and so many innovative projects happening around the world and in our community, it's easy to lose sight of how and why these new technologies matter, and how they can be practically applied to your projects.
Machine learning has huge potential in conservation tech, and its applications are growing every day! But the tradeoff of that potential is a big learning curve - or so it seems to those starting out with this powerful tool!
To help you explore the potential of AI (and prepare for some of our upcoming AI-themed events!), we've compiled simple, key resources, conversations, and videos to highlight the possibilities:
Three Resources for Beginners:
- Everything I know about Machine Learning and Camera Traps, Dan Morris | Resource library, camera traps, machine learning
- Using Computer Vision to Protect Endangered Species, Kasim Rafiq | Machine learning, data analysis, big cats
- Resource: WildID | WildID
Three Forum Threads for Beginners:
- I made an open-source tool to help you sort camera trap images | Petar Gyurov, Camera Traps
- Batch / Automated Cloud Processing | Chris Nicolas, Acoustic Monitoring
- Looking for help with camera trapping for Jaguars: Software for species ID and database building | Carmina Gutierrez, AI for Conservation
Three Tutorials for Beginners:
- How do I get started using machine learning for my camera traps? | Sara Beery, Tech Tutors
- How do I train my first machine learning model? | Daniel Situnayake, Tech Tutors
- Big Data in Conservation | Dave Thau, Dan Morris, Sarah Davidson, Virtual Meetups
Want to know more about AI, or have your specific machine learning questions answered by experts in the WILDLABS community? Make sure you join the conversation in our AI for Conservation group!
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WILDLABS Tech Tutors: Season Two
24 November 2020 12:00am
WILDLABS: Building a Better World with ‘Bad’ Data
17 November 2020 12:00am
Weekly Event: OTN Virtual Study Hall
16 November 2020 12:00am
Google unveils search engine for open data
6 September 2018 4:26pm
20 May 2020 10:24am
Hi all,
I'm fairly new to conservation technology and just getting acquainted with the extent and problems in the field. Data aggregation, standardisation and storage keep popping up as chronic problems across a lot of areas. Data seems to exist in sort of silos with different filing and access arrangements between them.
I would be interested to hear: Has the google dataset search improved drastically since its inception? Are there alternative solutions out there, or are there efforts to create them?
For example, from the bioacoustics meetup the other day, the vast datasets from the Australian Acoustic Observatory and Cornell Bioacoustics Centre don't seem to show up on google dataset search.
Andy the ARIES team you mentioned released a preview video of the interface with their ecosystem services modelling software. It seems really cool, is this something that would be useful for researchers outside of strict ecosystem services e.g. distributions of particular species temporally and spatially? What do you think of their software?
I'm not certain I'm asking the right questions here, but I'd be curious to hear your thoughts on any of this if you have any time.
15 November 2020 1:04am
This is great, thanks for sharing.
Using Computer Vision to Protect Endangered Species
10 November 2020 12:00am
Hackathon Opportunity: Vaquita Hacks
10 November 2020 12:00am
Upcoming AI for Conservation Events and Webinars
29 October 2020 10:57am
29 October 2020 11:02am
AI for Climate Forum: Lightning Talks
Bonnie Lei, Microsoft AI for Earth - 4pm GMT, October 30
Register here: https://us02web.zoom.us/webinar/register/WN_wO6ek5dTSMOmYqeMCDbkoQ
As part of the AI for Climate Forum 2020, we will host a series of Lighting Talks with industry leaders, academic representatives and researchers, NGO leaders, and policymakers; that will share ideas about the future of conservation and the harnessing of exponential technologies in the fight against climate change.
Next Guests:
October 30: Bonnie Lei, Head of Global Strategic Partnerships at AI for Earth/ Microsoft
November 6: Jenny Lawton, Startup Founder, and Innovation Expert
Tech Tutors: How do I train my first machine learning model?
18 June 2020 1:19pm
8 October 2020 9:45pm
Hi Wildlabbers,
Just popping in to share this very cool primer for beginners to embedded machine learning from our tutor Daniel Situnayake! If you're interested in learning more about the basics of TinyML, this is a great resource.
-Ellie
Training Opportunity: HarvardX TinyML Course
30 September 2020 12:00am
Webinar: Advances in Fisheries Electronic Monitoring
21 September 2020 12:00am
Tech Tutors: Review Session
3 September 2020 12:00am
Sustainable Fishing Challenges: Fishing Gear Innovations
19 August 2020 12:00am
Challenge: ElephantEdge
11 August 2020 12:00am
Event: StreamingScience's #Tech4Wildlife Thursdays
3 August 2020 12:00am
Automated Fish Identification and Abundance Using Artificial Intelligence
28 July 2020 12:00am
BearID To Go
21 July 2020 12:00am
Competition: Cornell Birdcall Identification
8 July 2020 12:00am
Tech Tutors: How do I get started using ML for my camera traps? Building Accurate Project-Specific Models
25 June 2020 12:02am
25 June 2020 6:01pm
26 June 2020 2:32pm
Hi everyone!
We've now posted Sara's session to our youtube channel, and I've also popped it up the top of this thread.
The collaborative notes worked really well! I've now updated them to capture what happened in the chat - it should be a helpful companion to go alongside the recording. The notes have links, projects, and key discussions we saw in the chat, and summarise the questions Sara coverd in the discussion as well as the Qs we weren't able to get to (40mins overtime was our limit!). If your question was one of the outstanding ones and you'd like to have it answered, please drop it in the discussion below.
The notes now also have the participant check ins (such an awesome range of places, projects and interests!) - I'm sharing these as seeing what other people are doing might help you connect with each other. If you see someone you want to connect with, try and find them using our member direcyour people tab. If you can't, email Ellie and she will see if that person is happy to hear from you before connecting you.
Reminder, registration is open for Carlos' tutorial next week: How do I perform automated recordings of bird assemblages? Register here.
Thanks everyone!
Steph
2 July 2020 8:58pm
Great talk! I thoroughly enjoyed it. Some high schoolers have done small AI projects(s) and have interest in the wildlife.
What resources would you all suggest to further develop high schooler’s interest in AI?
How do I scale up acoustic surveys with Audiomoths?
25 June 2020 12:00am
How do I perform automated recordings of bird assemblages?
19 June 2020 12:00am
How do I train my first machine learning model?
9 June 2020 12:00am
How do I get started using Machine Learning for my camera traps?
9 June 2020 12:00am
Innovator Interview: Hack the Poacher
4 June 2020 12:00am
The Perfect Paw Print: Collecting Data with FIT
3 June 2020 12:00am
Competition: 2020 Hackaday Prize
26 May 2020 12:00am
WILDLABS Tech Tutors: Season One
19 May 2020 12:00am
Get To Know FIT
6 May 2020 12:00am
Competition: iWildCam 2020
4 May 2020 12:00am
Training Course: Quantitative Analysis of Marine & Coastal Drone Data
29 April 2020 12:00am
Call for Submissions – Arm Research Summit 2020
24 April 2020 12:00am
10 September 2018 7:57am
I agree – this is a very welcome development, and it’s early days; I’m sure it will improve rapidly though. Whilst recognising that there are lots(!) of excellent data repositories out there already, with necessarily specialist functionality, there’s long been a need for something that can overarch these effectively, a ‘discovery portal of discovery portals’. Hopefully this can help do that.
After a cursory look, a couple of things struck me, from a user perspective: 1 –definitely some odd/limited search results at the moment, but as noted it’s early days – it’ll snowball as data owners get on board and standards adjust accordingly. 2 – more search tools would be beneficial e.g. date range tools, a map/bounding box search tool (cf Microsoft’s FetchClimate tool).
I also wanted to understand a bit more behind how it’s working – I assumed markup but wondered what ‘semantic web’ stuff this is drawing on. This article gives a bit more info, but I wonder how different it is to other efforts in this regard, e.g. how ARIES team have been developing semantic based tools to find best available datasets for ecosystem service modelling.
Final thought – it raises interesting questions and challenges about how to ensure things like quality and suitability are going to be measured objectively. It seems like this is an issue to be tackled as the tool develops and data owners engage more as it grows…