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!
- @shannondubay
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Panthera
Director of Conservation Technology at Panthera
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- @waltertortuga
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Universidad San Francisco de Quito
I'm a professor and researcher focusing on carnivore conservation in tropical landscapes.
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- @silvanasitayiari
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- @alekseisaunders
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Wildlife conservationist, ichthyologist, now pursuing a career in Software Engineering and Web Development
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Adventure Scientists is a 501(c)3 nonprofit organization based in Bozeman, MT that equips scientists and researchers with high-quality data collected from the outdoors that are crucial to addressing environmental challenges around the world.
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- @DanielHugelmann
- | He / Him
Hi, I'm the co-founder of OceanLabs Seychelles. We design and build environmental and marine remote sensing devices for conservation NGOs. As an engineer and avid diver, with a love for the environment, connecting conservation and technology was the natural thing to do!
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Background in Computer Science, Developing Acoustic AI Tech at Synature
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- @chmod000
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I build sensing and perceiving hardware that is designed to address issues that matter to me. That ranges from assistive technologies, to conservation ecology, and connecting individuals with place and each other.
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- 7 Groups
- @ARobillard
- | He/Him
A conservation data scientist and field ecologist with broad interest in the application of machine learning and population genetics to the conservation of threatened species. Alex has conducted field studies throughout central and south America, the Caribbean, and North America.
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- @nick56swim
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I am an IoT and embedded ML developer. I am also a nature enthusiast with keen interest in conserving the endangered species
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St. Lawrence University
Professor of Biology at St. Lawrence University
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- 13 Groups
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Interested in a PhD exploring the potential of AI? Please consider applying to Realize Lab at the University of Michigan CSE.
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This essay is the third in a series on the promise and challenges of using AI and machine learning to create a planetary environmental management system.
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Description | Activity | Replies | Groups | Updated |
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Some good points. Why not use a plane and fit it with high def camera + infrared sensor (+ possibly Lidar). You can fly higher and cover a much larger area in the same time. ... |
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AI for Conservation | 8 seconds ago | |
Thank you so much for your support. I am finding it really difficult to find the funding for the initial development. We need lots of engineering time to refine our detection and... |
+15
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AI for Conservation, Drones, Emerging Tech, Human-Wildlife Conflict, Wildlife Crime | 1 day 3 hours ago | |
Yes, this system is designed to be installed near farms. We also have the repeller system with audio & light, that is battery & solar powered. This system is a "last line... |
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AI for Conservation | 1 week 1 day ago | |
Yes, exactly! Alec and I are working together on this. |
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AI for Conservation | 1 week 5 days ago | |
Undoubted things will quickly evolve from just "straight" ChatGPTn, BARD, ClaudeAI, etc "standard" models, to more specialized Retrieval Augmentation Generation (RAG) , where... |
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AI for Conservation, Emerging Tech | 2 weeks 2 days ago | |
This is so cool! I am 1000% going to see if they want to come talk about it at Variety Hou! |
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AI for Conservation, Citizen Science | 2 weeks 3 days ago | |
Hi Sol,If the maximum depth is 30m, it would be worth experimenting with HydroMoth in this application especially if the deployment time is short. As Matt says, the air-filed case... |
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Acoustics, AI for Conservation, Data management and processing tools, Emerging Tech, Sustainable Fishing Challenges | 2 weeks 3 days ago | |
Online citizen science platforms like iNaturalist and Macaulay Library contain a wealth of images but are hard to search using text. We are... |
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AI for Conservation, Citizen Science | 2 weeks 5 days ago | |
We're seeking training data for AI for wolf ID - we at T4C manage 3 Wildbook platforms: Wild North, Whiskerbook and the... |
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AI for Conservation | 3 weeks ago | |
Hi Phani,An entry point might be to participate in a challenge related to conservation on:KaggleDrivenDataFruitPunchMax Planck Institute of Animal BehaviorYou could also reach out... |
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AI for Conservation | 3 weeks 6 days ago | |
[oops, the same reply got submitted twice and there doesn't seem to be a "delete" button] |
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AI for Conservation, Camera Traps | 3 weeks 6 days ago | |
Hi @zhongqimiao ,Might you have faced such an issue while using mega detectorThe conflict is caused by:pytorchwildlife 1.0.2.13 depends on torch==1.10.1pytorchwildlife 1.0.2.12... |
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AI for Conservation, Camera Traps, Open Source Solutions | 3 weeks 6 days ago |
Trialing Audiomoth to detect the hidden threats under the canopies of Belize
27 June 2017 12:00am
Pairing Scientists and Citizen Scientists with AI Assistants
18 May 2017 7:06pm
Machine learning, meet the ocean
10 May 2017 12:00am
Acoustics for Human-Wildlife Conflict Prevention, Anti-poaching, and more
27 April 2017 6:35pm
Welch Labs - Learning to see
31 March 2017 11:10am
15 Risks and Opportunities for Global Conservation
31 March 2017 12:00am
Conservation Leadership Programme 2017 Award
21 November 2016 12:00am
We Can Have Oceans Teeming with Fish with FishFace Technology
10 November 2016 12:00am
Tracking megafauna with satellite imagery
11 October 2016 5:08pm
Zoohackathon: 'END LOOP - Coding to end wildlife trafficking'
22 September 2016 12:00am
Video: Discover the SMART Approach
20 July 2016 12:00am
Passive Acoustic Monitoring: Listening Out for New Conservation Opportunities
29 June 2016 12:00am
Wildlife Crime Tech Challenge Accelerator Bootcamp
24 June 2016 12:00am
Digitising powerlines in bird migratory pathways
14 June 2016 8:53pm
Computer Vision to Identify Individual Animals
29 May 2016 4:52am
6 June 2016 11:17am
Hi Jason,
Thanks for sharing this demo, it's interesting to see the fluke id process in action. Is this part of the flukebook project? How do you see the project progressing - are there opportunities for people to get involved or challenges it would be helpful to get outside input on?
Cheers,
Stephanie
TEAM Network and Wildlife Insights
28 April 2016 12:00am
Is Google’s Cloud Vision useful for identifying animals from camera-trap photos?
20 April 2016 12:00am
ContentMine: Mining Helpful Facts for Conservation
5 April 2016 12:00am
Disruptive Technology: Embracing the Transformative Impacts of Software on Society
10 March 2016 12:00am
Ecotech Grants from the Captain Planet Foundation
18 February 2016 12:00am
Upcoming GIS and Remote Sensing Courses
9 February 2016 12:00am
[ARCHIVED] Job: ML developer at Skytruth
3 February 2016 1:22pm
Report outlines 2016's most pressing conservation issues
3 February 2016 12:00am
Wildlife Crime Tech Challenge: Winners Announced!
22 January 2016 12:00am
Introductions
10 December 2015 8:13pm
10 December 2015 8:41pm
To start things off...
I'm David J Klein. My background is in deep learning, machine learning, neuroscience, neuromorphic computing, and signal processing. I've been doing the startup thing Silicon Valley for the last 11 years after being in academia for a while. I've worked on products ranging from speech recognition systems, to cloud-based deep learning platforms. These days, some use the blanket term "AI".
For the last several years I've been developing software for Conservation Metrics which gives their analysists the ability to use deep learning to process large volumes of audio and image data from remote sensors in order to monitor population density changes of endangered species, detect collisions of birds and bats with infrastructure, and find rare and elusive species.
More broadly, I'm interested in integrating many disparate sensing domains from eDNA, to land-based sensors, to GIS data in order to provide tools to conservation scientists and ecologists that will enable them to develop a higher resolution understanding of the health of ecosysems around the globe and their response to positive or negative human interventions.
I'm looking forward to interacting with you all. Please let me know what other questions you have for me, and other ways I can help.
Regards,
David
17 January 2016 9:08pm
Hi,
I am jason Holmberg from WildMe.org. I am one of the developers of Wildbook (wildbook.org), an open source data management platform for wildlife research. I'm using ML as part of the IBEIS.org project to boost and metascore multiple computer vision algorithms for individual humpback and sperm whales. David, I would love to speak offline if you have the time: [email protected].
Cheers,
Jason
Google Releases Tensor Flow
18 November 2015 12:10am
20 December 2015 7:05pm
"TensorFlow, you see, deals in a form of AI called deep learning. With deep learning, you teach systems to perform tasks such as recognizing images, identifying spoken words, and even understanding natural language by feeding data into vast neural networks. "
Would this be applicable to an acoustic monitoring network? For example. my research has shown tigers have unique, identifiable vocalizations down to the individual and sex. If this software is applied to my recording network for tigers, would it be able to automatically recognize and categorize these individuals?
For example: when it hears Tiger 108, it would know and then input that it heard Tiger 108 at a particular time and date.
11 January 2016 12:38pm
The catch will be (and for any neural network or AI type learning I would expect the same) the training phase. If you are able to tell the sounds apart or identify a specific sound as belonging to a certain individual, the AI should afterwards be able to automatically identify the critical factors needed to distinguish the voices of the individuals. But it will need enough input from each individual as well as the different vocalizations used by tigers. AFAIKT it will be able to do this automatically afterwards, but I am not sure if (a) you will get enough identifiable vocalisations and (b) with a wide enough range of typical tiger vocalisations for it to be really reliable. Training on zoo animals might work? I am also interested in this, but for jackals instead of tigers.
11 January 2016 2:30pm
I'd like to suggest our open source package Wildbook (http://www.wildbook.org) as a base data management platfor for this. I agree with the above that there are a number of challenges around the vocalizations themselves, but having the identity information in a good database and data model is a great foundation. That's what we're doing for our computer vision/deep learning project at www.IBEIS.org.
Our non-profit WildMe.org is running both. Feel free to contact us with questions. We have played with time series matching (often used for speech recognition)...but actually for whale flukes. Would be happy to discuss potential for audio ID.
Deep Learning Image Recognition of Species In Global Wildlife Crime Reporting
31 December 2015 7:28pm
Big Data and Conservation: Deluge or Drought?
22 December 2015 12:00am
Cheap Space, DIY Imaging and Big Data
21 December 2015 12:00am
The Impact of the Internet of Things
10 December 2015 12:00am
31 March 2017 11:45am
Ah! Thanks for posting this Tom. It's such a well designed, simple to understand video series, and the backing track is utterly delightful.
Given the growing applications of machine learning for conservation, I've been wondering if a 'machine learning 101 for conservation' webinar or article might be a worthwhile resource to look into for our community. In looking for a link to put in here to a UCL course I know exists on this topic, I actually just came across this article: A PRACTICAL GUIDE TO MACHINE LEARNING IN ECOLOGY. Seems that Jon Lefcheck had the same thought as me and got right down to it.
If you're interested in more introductory, practical resources on machine learning, do let me know below! Also, if you know of any other go to tutorials that you've found useful, please share them.
Steph