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WILDLABS logoWildlabs.net : The conservation technology network

Group

AI for Conservation / Feed

Artificial intelligence is increasingly being used in the field to analyse information collected by wildlife conservationists, from camera traps 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. The AI For Conservation group is intended to unite and inspire all WILDLABS community members—whether already involved in AI for conservation, or not—to understand how to use and/or directly contribute to open-source research and development efforts.

Group curators

Anna Vallery
Seabird biologist experienced in research and applied conservation. Dedicated to conducting and using innovative research to inform conservation decisions.
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Viktor Domazetoski
PhD student at University of Exeter & University of Queensland. Interested in researching AI and its responsible application to Ecology, Environmental Monitoring and Nature Conservation.
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event

Tusk Conservation Lecture 2018: Ted Schmitt

Tusk

15 October 2018 12:00am

Tusk are delighted to announce that their Tusk Conservation Lecture 2018 speaker will be Ted Schmitt of Vulcan Inc, Paul G Allen Philanthropies. He will speak about advancing Innovation in conservation, a talk that will...

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event

Ocean Hack: San Francisco, 10-11th September, 2018

One Ocean Collab

20 August 2018 12:00am

A 48 hr pop up innovation lab for the ocean, bringing together a mix of designers, strategists, technologists, engineers, scientists, marine conservationists, educators, artists and buisness talent to co-create...

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article

Thermal Sensor Project Update: Testing with live animals at the San Diego Zoo

Arribada Initiative

27 July 2018 12:00am

The winners of our Human Wildlife Conflict Tech Challenge are offering regular updates throughout the year to chronicle their failures, successes and what they learn along the way as they develop their solutions. In...

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Lars Holst Hansen

31 March 2023 2:15pm

Hi Anne!Interesting with the vaccum seal issue!When looking at the sensor comparison images, it seems like there is not only a difference in noise (or SNR). The Heimann 80x64 also...
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article

FIT Cheetahs

Larissa Slaney

4 December 2017 12:00am

A new research project is looking to investigate whether technology combined with the ancient skills and knowledge of Namibian trackers can help save cheetahs from extinction. Called FIT Cheetahs, the research project...

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discussion

Automated video count of migratory birds

Steffen Oppel

22 April 2016 8:38am

We are wondering whether anybody is aware of a free/open-source system to extract and count moving signals from a videostream, with the ultimate goal to apply such a system to...

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Stephanie O'Donnell

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:

Steph 

 

 

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article

HWC Tech Challenge Update: Meet the Judges

WILDLABS Team

20 October 2017 12:00am

Our panel of international experts has been hard at work reviewing the 47 proposals we recieved for innovative technological tools to address human wildlife conflict. The panelists have systematically been assessing the...

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discussion

[ARCHIVED] Fish identification computer vision competition

Kate Wing

26 September 2017 8:43pm

Our contest to develop tools that can count, measure, and identify fish from commercial fishing video runs through October 30. Please pass this on to anyone you know who might be...

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Stephanie O'Donnell

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 $3,000

There 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 

 

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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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