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!
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- @Namitha
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Physics PhD candidate enthusiastic about conservation and ecology
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Founder & CEO of we.are.tohorā, creating a community that cares about whales, the environment and each other.
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- @Dragonoeede
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- @rosco
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Ross is a specialist in the capture and analysis of remotely sensed data for conservation.
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- @Gathoni
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PhD Candidate at Nottingham Trent University, UK. Ecologist with a passion for wildlife conservation and conservation technology with foundational training in BSc and MSc Rangeland Management (Ecology option). Current project: Use of acoustics to monitor ecosystem restoration.
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- @leonardoscarpa
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I'm Leonardo, I'm a biologist, and I'm passionate about movement ecology. Another of my hobbies is GIS. I have a master's degree in this topic, which I complement with the use of technology such as drones, camera traps and acoustic receivers
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Society for Conservation GIS
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- @Thomas_Luypaert
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Norwegian University of Life Sciences (NMBU)
Ecological monitoring enthusiast with a focus on ecological and conservation research
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Working in relation to ecosystem services
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Sharing failure, tech support for conservation, roaming mentors, conservation tech hype cycles and developing new road maps - participants in our tech workshops at ICCB 2019 shared an abundance of ideas for how to shape...
21 August 2019
In this case study, Cooper Oelrichs of Save Indonesian Endangered Species Fund (SIES) breaks down his proposal for the development and training of an automated rhino identification system from limited camera trap data.
27 July 2019
The WILDLABS TECH HUB is supporting technology solutions tackling the illegal wildlife trade, in collaboration with the Foreign & Commonwealth Office, Digital Catapult, Satellite Applications Catapult, Amazon Web...
4 June 2019
To further their missions, LDF and Microsoft are collaborating on the AI for Earth innovation grant to support applicants in creating and deploying open source machine learning models, algorithms, and data sets that...
4 June 2019
Traditionally, illegal wildlife trade thrived in physical markets. But today it has also moved online. In China, more than half of the trade in elephant ivory items happens on e-commerce platforms. Enrico Di Minin and...
31 May 2019
This webinar recording will provide a brief overview of current SMART functionality, highlight case studies of large scale and innovative SMART deployments, and detail how SMART is embracing and leveraging new...
21 May 2019
In February, we released an open call for the WILDLABS TECH HUB, offering 3 months of support for solutions using technolgy to tackle the illegal wildlife trade. We were overwhelmed by an incredible 37 submissions,...
13 May 2019
To realise the potential benefits of data for our societies and economies we need trustworthy data stewardship. We need to establish different approaches to deciding who should have access to data, for what purposes and...
15 April 2019
Happy World Wildlife Day! To celebrate, this week we've asked our community to share photos showing how they are using tech in the field or the lab, using the #Tech4Wildlife hashtag.
3 March 2019
After a promising first run in Namibia, a Swiss project could aid savanna conservation using drones and automatic image analysis.
18 February 2019
Jacqueline Ramos explains how artificial intelligence can help to track and prevent chimpanzee trafficking across the globe.
6 February 2019
The Open Data Institute and the Office for AI are partnering to explore how data trusts could help to solve environmental, societal and industry challenges by enabling increased access to data while retaining trust....
31 January 2019
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Description | Activity | Replies | Groups | Updated |
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Hi everyone! We are spreading the word about a free, open-source tool called Zamba that automatically detects and... |
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AI for Conservation | 2 years 9 months ago | |
Hi Wildlabbers, This week's Tech Tutor Jamie MacAulay is talking about how to use and analyze large acoustic data using... |
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Acoustics, AI for Conservation, Marine Conservation | 2 years 10 months ago | |
Hi Wildlabbers, We're getting ready for tomorrow's episode with Tech Tutor Lily Xu, who'll talk to us about how machine... |
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AI for Conservation, Protected Area Management Tools, Wildlife Crime | 2 years 11 months ago | |
Looks good, but what is the added value compared to other examples as Obsidentify and Plantnet? |
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AI for Conservation | 2 years 11 months ago | |
https://www.dryad.netJob description"Dryad is an environmental IoT startup based in Berlin-Brandenburg. Our mission is to develop a large-... |
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AI for Conservation | 2 years 11 months ago | |
Deep Learning Engineer at Pachama - https://jobs.lever.co/pachama/81716dd9-8019-4916-add2-fcfaae426331 Embedded Software Engineer... |
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AI for Conservation | 2 years 11 months ago | |
Hi everyone, We're excited to welcome Nicole Flores to Tech Tutors to walk us through getting started with Wildlife Insights! ... |
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AI for Conservation, Camera Traps, Data management and processing tools | 3 years ago | |
Hi all, We're excited to welcome Siyu Yang to Tech Tutors to chat about getting started with Megadetector! For all you AI... |
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AI for Conservation, Camera Traps, Data management and processing tools | 3 years ago | |
You should join the Key Conservation app, which connects people and NGO's for specific needs, whether funding, time, or skills (programming included). Organizations can list... |
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AI for Conservation | 3 years 2 months ago | |
Thank you VERY much for that, Carly. I really appreciate it. I have only heard of the bear research project and was not aware of the others. This is very helpful. FYI: Most... |
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AI for Conservation | 3 years 4 months ago | |
The Atlas Coral Atlas combines high-resolution, seamless satellite imagery mosiacs, image processing techniques, machine learning, and... |
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AI for Conservation | 3 years 4 months ago | |
Sure thing, I'll email you directly. |
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AI for Conservation | 3 years 5 months ago |
Ocean Hack: San Francisco, 10-11th September, 2018
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20 August 2018 12:00am
Thermal Sensor Project Update: Testing with live animals at the San Diego Zoo
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27 July 2018 12:00am
$90K in grants from the Con X Tech Prize
11 June 2018 12:00am
Webinar: Artificial Intelligence for Earth, Microsoft Research
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23 April 2018 12:00am
HWC Tech Challenge: Thermopile Sensor Project
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19 April 2018 12:00am
BES Guide to Reproducible Code
27 March 2018 2:50pm
#Tech4Wildlife Photo Challenge 2018: Our Top 10
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3 March 2018 12:00am
Congratulations to Zoohackathon winners, team ODINN!
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6 December 2017 12:00am
FIT Cheetahs
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4 December 2017 12:00am
How The San Diego Zoo Is Using AI And Drones To Save Polar Bears
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30 November 2017 12:00am
Automated video count of migratory birds
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
HWC Tech Challenge Update: Meet the Judges
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20 October 2017 12:00am
[ARCHIVED] Fish identification computer vision competition
26 September 2017 8:43pm
29 September 2017 1:00pm
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 see what solutions come out of the challenge - please do keep us updated as it progresses if you have time. The challenge is focused on the New England fishery - are you envisaging that this is an approach you can take to scale and eventually extend to other fisheries?
To add a bit more information for anyone interested, there's actually $50,000 of prizes attached to this challenge:
Place Prize Amount 1st $20,000 2nd $15,000 3rd $10,000 4th $3,000There is also a wildcard prize:
We're also looking for innovative approaches to solving this fishy problem, even if they don't score in our Top 4. If you want to be eligible for our $2,000 Judges' Choice Award, submit your code on the Submit Report page (available once you've signed up) by the competition end date for review. Our judges will be looking for inventive, novel solutions that can be incorporated into video review programs, so share your most fin-tastic ideas.
Finally, if you're curious to find out more about what led to the challenge, Kate actually wrote a piece called 'Machine learning, meet the ocean' that we published in the resources area a few months ago. Do have a read!
Cheers,
Steph
DAS: A Scaleable Solution For Protected Area Management
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26 September 2017 12:00am
The Greenhouse 2017: Planet Saving Technology Series (Syd, Australia)
19 September 2017 2:01pm
19 September 2017 2:37pm
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 ability to help leapfrog or change corrupt and inefficient power structures can revolutionize the way we approach issues ranging from the supply chain, financial inclusion, human rights abuses, and modern slavery to environmental, energy, and workforce problems.
One source of shared truth and trusted infrastructure can help NGOs, charities, social entrepreneurs, civil societies and companies achieve their mission.
Come and discover the innovators, leaders, and philosophers in the space showcasing their solutions and meet the technologists who can support your needs.
So what is Blockchain, and is it just hype or is it really a Planet Saving Technology?
Speakers and Panellists
• Dr Jane Thomason - CEO Abt Australia, Social Policy Adviser, Devex Impact Strategic Advisory Council, Commentator Blockchain
• Arthur Falls - Director of Media at Consensys / Podcaster, State Change & The Ether Review Podcasts
• Bubba Cook - Pacific Tuna Programme Manager, WWF NZ / Pacific
• Leah Callon-Butler - Member, Advisory Board, RedGrid
• Bridie Ohlsson - External Relations, AgriDigital
July: Virtual Reality and Augmented Reality
With it's origins in science fiction, the idea of Virtual Reality has been around since the 1950's, but in the last few years, with the promise of mobile computing, it's suddenly the talk of the town.
Many are excited by the deep immersive nature and empathetic story telling potential of VR/AR and see huge opportunity in awareness raising and shifting public opinion around important issues.
So what is VR, and it's related technology cousin Augmented Reality, an is it a potential Planet Saving Technology?
Speakers and Panellists
We have a bumper, star-studded panel to unpack, explain and explore this promising technology..
• Kim McKay - CEO, Australian Museum
• Brennan Hatton - Founder, Equal Reality (Augmented Reality Development)
• Parrys Raines - FBGen / Future Business Council / Climate Girl
• Jennifer Wilson - Creative/Digital Strategist, Founder, Lean Forward
• Mikaela Jade - CEO, Indigital (Indigenous storytelling with AR)
• Scott O'Brien - CEO, Humense (Volumetric Video + Virtual Reality) (Panel Moderator)
June: Smart Cities and the Internet of Things
What is a Smart City? How will Smart Cities change the way we organise our lives? Will they bring about the so-called ‘fourth industrial revolution’?
What is the Internet of Things, and does it have the potential to be a Positive Impact Techonology? What are the opportunities and what are the risks?
We explore all this and more in the first of our deep dives into Planet Saving Technology: Smart Cities and the Internet of Things.
Speakers and Panellists
• Frank Zeichner - CEO, IoT Alliance Australia
• Angela Bee Chan - Schneider Electric / Hackathons Australia
• Ben Moir - Snepo Fablab / WearableX
• Monica Richter - Low Carbon Futures, WWF Australia.
• Andrew Tovey - Total Environment Centre, TULIP/Smart Locale (Panel Host)
Deep Learning Project Repository
10 December 2015 7:53pm
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/
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
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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
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27 June 2017 12:00am
Pairing Scientists and Citizen Scientists with AI Assistants
18 May 2017 7:06pm
Machine learning, meet the ocean
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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
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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
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20 July 2016 12:00am
31 March 2023 2:15pm