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!
No showcases have been added to this group yet.
- @CathyNj
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Catherine Njore is a seasoned Cartographer with over 18yrs experience and specializing in children cartography. She recently designed a Cartography: Fun with Maps Program(CFMP); a program that assists children to learn how to draw, read and use maps effectively.
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Hello, I am a PhD student studying birds and box turtles!
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I am a conservation technology advisor with New Zealand's Department of Conservation. I have experience in developing remote monitoring tech, sensors, remote comms and data management.
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- @daviana
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PhD student at Stanford University studying marine migratory species and human-wildlife interactions through biologging, remote sensing, and participatory science.
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- @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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Experienced Biologist with a Master of Science degree and 15 years of international experience in the fields of nature resource management, ecological restoration, and human rights in agriculture, forestry, and cattle ranching for sustainable production and sourcing
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- @DeepakSathyanarayan
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Deepak is an experienced professional who has served in multiple leadership roles in technology, science, product development, and engineering programs. He has proven expertise in shaping and executing technology strategies across defense, space, health, and environment sectors.
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- @emilydorne
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DrivenData
Lead Data Scientist and developer of zamba, a tool for automatic species classification from camera traps
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Technical Specialist at FFI: loves data management, maps, remote sensing & GIS for conservation
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- @erinconnolly
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University College London (UCL)
PhD Student at UCL's People and Nature Lab. Studying human-livestock-wildlife coexistence in the Greater Maasai Mara Ecosystem, Kenya.
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| Description | Activity | Replies | Groups | Updated |
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| 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... |
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AI for Conservation | 6 years 5 months ago | |
| In case you missed it, last year the British Ecological Society published A Guide to Reproducible Code in Ecology and Evolution... |
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AI for Conservation | 7 years 4 months ago | |
| 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,... |
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AI for Conservation | 7 years 9 months ago | |
| Hi Kate, It's really exciting to hear that you've now launched the challenge, congratulations on getting to this point! It's going to be interesting to... |
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AI for Conservation | 7 years 10 months ago | |
| If you're interested, you can check out the live recordings from past events (links below take you to the videos): August: The Blockchain The Blockchain's potential... |
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AI for Conservation | 7 years 10 months ago | |
| 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... |
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AI for Conservation | 7 years 11 months ago | |
| 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? |
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AI for Conservation | 7 years 11 months ago | |
| Here' s a press release we put out today talking about how we're using a deep convolutional neural network to find and curate whale... |
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AI for Conservation | 8 years 2 months ago | |
| For those interested: in the acoustics channel I have posted news on our sound event recognition sensor, using conv-net: https://... |
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AI for Conservation | 8 years 3 months ago | |
| 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... |
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AI for Conservation | 8 years 4 months ago | |
| Hello all, I'm looking at using a combination of machine learning and satellite imagery to identify and count individual animals,... |
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AI for Conservation | 8 years 10 months ago | |
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Carly, that would be great! Thanks! I work with soundscapes and love the work of Rainforest Connection! I'll send you an email (@CUNY) to coordinate!
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Definitely interested! I'm in the ecoacoustics/acoustic monitoring space, working at Rainforest Connection and Arbimon.