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AI for Conservation / Feed

Artificial intelligence is increasingly being used in the field to analyse information collected by wildlife conservationists, from camera trap and satellite images to audio recordings. AI can learn how to identify which photos out of thousands contain rare species; or pinpoint an animal call out of hours of field recordings - hugely reducing the manual labour required to collect vital conservation data.

discussion

The Greenhouse 2017: Planet Saving Technology Series (Syd, Australia)

Stephanie O'Donnell

19 September 2017 2:01pm

If you're Sydney based, you should already be aware of Greenups, Sydney's longstanding sustainability drinks that happen on the first Tuesday of every month. But what you...

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Stephanie O'Donnell

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)

 

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discussion

Deep Learning Project Repository

David J Klein

10 December 2015 7:53pm

Feel free to post links to projects you're aware of using Deep Learning to assist in conservation.  Here's a few to get started. Classification of 121...

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Stephanie O'Donnell

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/

 

 

 

Jon Van Oast

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! ]

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discussion

MIT's SLOOP: machine learning (ML) animal image recognition

Dave Cortright

27 July 2017 2:04am

Joining others in the space like IBEIS and Dr. Frederic Maire from Queensland University of Technology, MIT has a program to do something similar. From the MIT SLOOP...

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Jonno Bourne

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?

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article

Machine learning, meet the ocean

Kate Wing

10 May 2017 12:00am

There is a revolution coming in conservation. Advances in conservation technology are generating more data than ever before on what lives where, who eats who, and what’s disappearing and how fast, but it still requires...

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discussion

Welch Labs - Learning to see

Tom Swinfield

31 March 2017 11:10am

For those of you working on image detection systems of whatever kind, I highly recommend the YouTube series Learning to See by WelchLabs. It presents truely beautiful...

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Stephanie O'Donnell

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

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event

Video: Discover the SMART Approach

The SMART Partnership

20 July 2016 12:00am

The Spatial Monitoring and Reporting Tool (SMART) Partnership combines a ranger-based data collection tool with capacity building and a suite of best practices aimed at helping protected area and wildlife managers...

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event

Wildlife Crime Tech Challenge Accelerator Bootcamp

Sophie Maxwell

24 June 2016 12:00am

Earlier this month, the 16 prize winners of the Wildlife Crime Tech Challenge were called to Washington D.C. for an Accelerator Boot Camp. Sophie Maxwell, a member of the prize winning team from the Zoological Society...

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discussion

Computer Vision to Identify Individual Animals

Jason Holmberg

29 May 2016 4:52am

Here's a demo of the work we're doing to identify individual humpback whales (and other species) using computer vision and A.I. on the IBEIS.org project. https://www....

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Stephanie O'Donnell

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

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article

TEAM Network and Wildlife Insights

Eric Fegraus

28 April 2016 12:00am

Operating the largest tropical forest camera trap network globally, TEAM Network has accumulated over 2.6 million images. How can large datasets coupled with new techniques for data management and analysis provide...

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article

ContentMine: Mining Helpful Facts for Conservation

Jenny Molloy

5 April 2016 12:00am

Thousands of papers and reports about flora and fauna are published each year. While peer-reviewed published information is vitally important to conservation organisations, the ever-increasing mountain of information...

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discussion

Introductions

David J Klein

10 December 2015 8:13pm

Welcome to the Machine and Deep Learning group! Here, we'll be discussing current and future uses of Deep Learning (DL) and more broadly Machine Learning (ML) for improving...

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David J Klein

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

Jason Holmberg

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

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