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.
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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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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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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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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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Gabriela A. Valencia-Macias, B.Sc. (Universidad Veracruzana), is achieving a master's degree in science at the Ecology Institute (INECOL A.C). She is Colombian-Mexican and has focused her studies on the conservation and biodiversity of amphibians.
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Jacqueline Ramos explains how artificial intelligence can help to track and prevent chimpanzee trafficking across the globe.
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27 July 2018
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WILDLABS Admin
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| 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... |
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AI for Conservation | 9 years 2 months ago | |
| Hi folks! The team at Skytruth (responsible for Global Ocean Watch) are looking for a machine learning developer. It might be of... |
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AI for Conservation | 9 years 6 months ago | |
| 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... |
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AI for Conservation | 9 years 6 months ago | |
| 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... |
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AI for Conservation | 9 years 7 months ago | |
| Over the past year the open data GDELT Project (http://gdeltproject.org/), which monitors local news coverage worldwide in... |
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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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Hi Donya! You might check out the Conservation Tech Directory to see what projects/organizations/tools best align with your interests and skills.
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That sounds like a really neat project! Do fish get re-caught often enough that individual ID is useful? Is sample bias (more data from popular spots) an issue?
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Definitely interested! I'm in the ecoacoustics/acoustic monitoring space, working at Rainforest Connection and Arbimon.