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!
Botswana Predator Conservation Trust
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Quantitative ecologist @ Biotope
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Savanna Ecologist, Field Station Director and Head of Conservation Tech Projects for Organization for Tropical Studies (OTS).
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Ecologist & Conservationist. Women for the Environment, Africa Fellow. National Geographic Explorer.
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Nature lover from MA
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Latin American environmental scientist.
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PhD student at UC Davis studying gorilla communication & movement
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Project planet is working to mitigate conflict between farmers and forest elephants in Gabon, Central Africa.
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Wild Me
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I am a AI researcher passionate about wildlife conservation.
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We demonstrate the power of using passive acoustic monitoring & machine learning to survey species, using ruffed lemurs in southeastern Madagascar as an example.
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Description | Activity | Replies | Groups | Updated |
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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 | 1 month 1 week ago | |
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 2 weeks 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 2 weeks 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 4 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 1 week 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 2 weeks 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 3 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 |
Meet the WILDLABS TECH HUB Winners
13 May 2019 12:00am
Call for Papers: Data Mining and AI for Conservation Workshop at KDD 2019
15 April 2019 12:00am
Huge appetite for data trusts, according to new ODI research
15 April 2019 12:00am
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26 March 2019 10:12am
WILDLABS Virtual Meetup: Low Cost, Open-Source Solutions
18 March 2019 12:00am
#Tech4Wildlife Photo Challenge: Our Favourites from 2019
3 March 2019 12:00am
Conserving the Sumatran and Javan Rhino
15 January 2019 7:39pm
24 February 2019 1:28am
Hi Claire,
At the BearID Project, we are working on a similar problem for brown bears. We are currently using machine learning methods developed for human facial recognition (like Google FaceNet). We got some ok initial results, but now we are running up against small data issues. The method for human faces were trained with millions of images of hundreds of thousandes on individuals. We have a few thousand images of about a hundred individuals. We plan to investigate other methods in the future.
It will be great to keep in touch to see what methods you will be using.
Ed
24 February 2019 1:38am
Hi Colin,
At the BearID Project, we are working on a similar problem for brown bears. We are currently using machine learning methods developed for human facial recognition (like Google FaceNet). We got some ok initial results, but now we are running up against small data issues. The method for human faces were trained with millions of images of hundreds of thousandes on individuals. We have a few thousand images of about a hundred individuals. We plan to investigate other methods in the future.
The last time I talked to WildMe, the identification algorithms were based on matching unique patterns. We didn't think this would be directly applicable for brown bears as they don't have a lot of clearly identifiable markings. Have you developed other identification algorithms?
Ed
Responsible AI for Conservation?
11 February 2019 6:22pm
21 February 2019 8:17am
Hi Jaishanker
Absolutely - the overlap between image-based and sound-based analyses is increasing, and consistent terminology will no doubt help us share info.
Are you using ML in SODA for automated identification of sounds? If so, how are you determining if a given classifier is performing well?
Thanks
Ollie
21 February 2019 11:08am
Hello Ollie,
SODA is a recently launched suite. It is in the development phase. We have with us call libraries with multiple (40+) calls for 10- 12 species of birds. A research scholar is on the job for classifying at the species level.
Our interest is equally on separating the different sonic components (as stated in https://www.wildlabs.net/community/thread/666). It is different from the link shared in my previous reply. This is where I see the confluence of objectives.
As a TEAM, I believe, we can address the individual objectives faster.
regards
jaishanker
21 February 2019 9:13pm
Hi Ollie,
Great article, thank you! I mostly work with responsible AI in other contexts, at Doteveryone.org.uk and the Trust & Technology Initiative at the University of Cambridge, so don't have much to offer here, although I am very interested in the topic. I appreciate your point that many of the consumer data issues highlighted in the 'popular' responsible AI discourse aren't relevant to conservation (some of us have been gathering 'responsible tech' / 'ethical tech' resources in a shared doc, and there's essentially nothing there for conservation specifically - https://docs.google.com/document/d/1SN6hYeKe3eRK6x9D0Sr7GpCA4nirpyo3u68xG1A6NDs/edit ). However there might be some links with humanitarian data practices, which are touched on by the Responsible Data folks at https://responsibledata.io and https://www.fabriders.net/data-literacy-consortium/ or in this recent article https://asecondmouse.wordpress.com/2019/02/20/instability-forecasting-models-seven-ethical-considerations/
Best,
Laura
Using Swiss AI and Drones to Count African Wildlife
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ChimpFace: Facial recognition to combat wildlife trafficking
6 February 2019 12:00am
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31 January 2019 12:00am
The Ecosulis Rewilding Tech Challenge
14 January 2019 12:00am
WILDLABS Virtual Meetup: Big Data in Conservation
27 November 2018 12:00am
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22 November 2018 12:00am
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22 November 2018 12:00am
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12 November 2018 12:00am
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15 October 2018 12:00am
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1 October 2018 12:00am
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20 August 2018 12:00am
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11 June 2018 12:00am
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23 April 2018 12:00am
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19 April 2018 12:00am
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4 December 2017 12:00am
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30 November 2017 12:00am
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22 April 2016 8:38am
30 October 2017 11:46am
Hey Steffen,
I know you've had a student working on this challenge for the past year - how is this project progressing? If you (or your student) have a moment, it would be great to hear an update.
@mmckown shared an in depth write up of one their projects that I thought might be relevant, as it seemed they were tackling something similar to what you are looking into? His team at Conservation Metrics (which presumably included @kleinsound) partnered with Microsoft to automate counts of Red-legged Kittiwakes with ML. I know it's not the exactly the same problem you're looking into, however the post covers their end-to-end flow for object detection, so might have some useful ideas/approaches that may have relevance for your work.
Bird Detection with Azure ML Workbench
Introduction
Estimation of population trends, detection of rare species, and impact assessments are important tasks for biologists. Recently, our team had the pleasure of working with Conservation Metrics, a services provider for automated wildlife monitoring, on a project to identify red-legged kittiwakes in photos from game cameras. Our work included labeling data, model training on the Azure Machine Learning Workbench platform using Microsoft Cognitive Toolkit (CNTK) and Tensorflow, and deploying a prediction web service.
In this code story, we’ll discuss different aspects of our solution, including:
- Data used in the project and how we labeled it
- Object detection and Azure ML Workbench
- Training the Birds Detection Model with CNTK and Tensorflow
- Deployment of web services
- Demo app setup
Steph
23 January 2019 10:12pm
Hello Claire,
Engineer at Wild Me here. We would love to start a conversation about a Wildbook for rhinos.
Lets talk about citizen science and computer vision for identification possibilities. I'm curious about your current data set and the identification tools you are using as a starting point. I'm happy to talk here, or you can email our team at [email protected].