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!
- @eliminatha
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Passionate wildlife researcher dedicated to uncovering the secrets of the natural world via the lens of camera traps. With a sharp eye for detail and a strong commitment to wildlife conservation.
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- 4 Groups
- @Amitkaushik
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University of Georgia (UGA)
Environmental anthropologist; An interdisciplinary Ph.D. student, bridging conservation science, policies, and social justice
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- 1 Discussions
- 2 Groups
Data has been my passion and i enjoy working with data while bringing value to the business. Data engineer with 7+ years of experience Eager to support with expert analytical skills to advance the companys business operations and strategic initiative.
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- 9 Groups
- @kbubnicki
- | he/his
Ecologist, data scientist, and programmer with over 13 years of professional experience. Open source and Linux enthusiast. Researcher at the Mammal Research Institute, Polish Academy of Sciences, and CEO of the Open Science Conservation Fund.
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- 5 Groups
- @JoãoVieira
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Conservation biologist. Iberian wolf monitoring field technician. Master`s on bear`s movement ecology.
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- @shannondubay
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Panthera
Director of Conservation Technology at Panthera
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- 5 Groups
- @waltertortuga
- | She/Her/Hers
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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- 2 Groups
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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- 16 Groups
- @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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In this Conservation Tech Showcase case study from Happywhale, you’ll learn about how AI tools are helping researchers and citizen scientists identify and protect humpback whales.
16 May 2023
The new role will support the growth of BioSciences’ new People and Nature Laboratory at UCL East through conducting cross-disciplinary research, teaching, outreach, and entrepreneurial activities in applied ecological...
16 May 2023
Are you using or creating tech to protect wildlife? We want to support your organization through two $15,000 grants!
15 May 2023
How can you use tools like photo quadrats, AI, and MERMAID together? We share an example overview of how WCS Staff in Mozambique use image classification tools with MERMAID to integrate photo quadrat and other coral...
12 May 2023
We've now wrapped our 2023 AI for Conservation Office Hours, where we helped conservationists connect with AI experts to get tailored expert advice on AI and ML problems in their projects
11 May 2023
Why you should know about Kenya-founded data analytics company - Gro Intelligence, arguably the largest funded tech startup within the African startup ecosystem and the only platform that brings together live, global...
5 May 2023
3 year position - Trondheim, Norway
2 May 2023
Article on www.nationalgeographic.com
27 April 2023
The Project Manager will work to implement acoustic monitoring projects in countries worldwide with some of the biggest stakeholders in conservation tech and biodiversity monitoring!
12 April 2023
New paper in Journal of Animal Ecology
24 March 2023
💙 Exciting news from Appsilon! Our flagship project, Mbaza AI, is expanding its impact on nature and biodiversity conservation. We’ve teamed up with the 🦏 Ol Pejeta Conservancy to build a model for classifying images of...
16 March 2023
The Innovation in Practice edition of Methods in Ecology and Evolution is still seeking proposals about conservation technology
6 March 2023
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Description | Activity | Replies | Groups | Updated |
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Hi, this is pretty interesting to me. I plan to fly a drone over wild areas and look for invasive species incursions. So feral hogs are especially bad, but in the Everglades there... |
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AI for Conservation, Camera Traps, Open Source Solutions, Software and Mobile Apps | 1 month 1 week ago | |
Gotcha, well I look forward to seeing future iterations and following along with your progress!! |
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Autonomous Camera Traps for Insects, AI for Conservation, Emerging Tech, Open Source Solutions | 1 month 1 week ago | |
Hi everyone!@LashaO and @holmbergius from the Wild Me team at ConservationX Labs gave a superb talk at last month's Variety Hour,... |
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AI for Conservation, Camera Traps | 1 month 1 week ago | |
Thanks Carly! I will keep anyone interested in this project posted on this platform. Cheers |
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Acoustics, AI for Conservation | 1 month 1 week ago | |
Greetings Everyone, We are so excited to share details of our WILDLABS AWARDS project "Enhancing Pollinator Conservation through Deep... |
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AI for Conservation, Autonomous Camera Traps for Insects | 1 month 3 weeks ago | |
EcoAssist is an application designed to streamline the work of ecologists dealing with camera trap images. It’s an AI platform that... |
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Software and Mobile Apps, AI for Conservation, Camera Traps | 1 month 3 weeks ago | |
We could always use more contributors in open source projects. In most open source companies Red Hat, Anaconda, Red Hat and Mozilla, people often ended up getting hired largely... |
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Acoustics, AI for Conservation, Conservation Tech Training and Education, Early Career, Marine Conservation | 2 months ago | |
Hi @timbirdweather I've now got them up and running and winding how I can provide feedback on species ID to improve the accuracy over time. It would be really powerful to have a... |
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Acoustics, AI for Conservation, Citizen Science, Emerging Tech | 2 months 1 week ago | |
Really interesting project. Interesting chip set you found. With up to around 2mb sram that’s quite a high memory for a ultra low power soc I think.It might also be... |
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Acoustics, AI for Conservation | 2 months 2 weeks ago | |
Thank you so much for your encouraging words! I'm thrilled to hear that you enjoyed our conversation, and I truly appreciate your support in spreading the word about my survey... |
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Acoustics, AI for Conservation | 3 months ago | |
Perfect thanks! I am still a novice using Python but my wife can help me! |
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AI for Conservation, Camera Traps, Human-Wildlife Conflict | 3 months 1 week ago | |
Hi everyone! My name is Leah Govia and I am a PhD candidate at the University of Guelph, Canada. My research explores what people... |
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Ethics of Conservation Tech, Conservation Tech Training and Education, AI for Conservation | 3 months 2 weeks ago |
Deep Learning Project Repository
10 December 2015 7:53pm
9 October 2016 12:12am
Wildbook / IBEIS. Open-source effort to combine web-based mark-recapture database with ML/CV photo detection and identification. http://wildbook.org
[ Full disclosure: I am a member of the non-profit team working on this project! ]
2 September 2017 7:40am
Hypraptive and Brown Bear Research Network collaboration to develop a deep learning, brown bear face identification system: BearID Project.
[Disclosure: I am a member of hypraptive, and maintain the hypraptive blog]
MIT's SLOOP: machine learning (ML) animal image recognition
27 July 2017 2:04am
27 August 2017 7:20am
It looks like they haven't updated for a couple of years do you know if it is still active or are they changing to a different system like tensor flow?
From the Field: Developing a new camera trap data management tool
7 July 2017 12:00am
Leverage Space Technology for Wildlife Protection with the European Space Agency Kick-start Grant
5 July 2017 12:00am
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
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
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.
5 August 2016 2:38pm
NOAA Right Whale Recognition Competition, January 2016
364 teams | $10,000 prize
https://www.kaggle.com/c/noaa-right-whale-recognition
Competition Details:
With fewer than 500 North Atlantic right whales left in the world's oceans, knowing the health and status of each whale is integral to the efforts of researchers working to protect the species from extinction.
Currently, only a handful of very experienced researchers can identify individual whales on sight while out on the water. For the majority of researchers, identifying individual whales takes time, making it difficult to effectively target whales for biological samples, acoustic recordings, and necessary health assessments.
To track and monitor the population, right whales are photographed during aerial surveys and then manually matched to an online photo-identification catalog. Customized software has been developed to aid in this process (DIGITS), but this still relies on a manual inspection of the potential comparisons, and there is a lag time for those images to be incorporated into the database. The current identification process is extremely time consuming and requires special training. This constrains marine biologists, who work under tight deadlines with limited budgets.
This competition challenges you to automate the right whale recognition process using a dataset of aerial photographs of individual whales. Automating the identification of right whales would allow researchers to better focus on their conservation efforts. Recognizing a whale in real-time would also give researchers on the water access to potentially life-saving historical health and entanglement records as they struggle to free a whale that has been accidentally caught up in fishing gear.
From what I can gather, the winning solution was submitted by deepsense.io. They've written a full blog post about it here:
http://deepsense.io/deep-learning-right-whale-recognition-kaggle/