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Artificial Intelligence in Biodiversity and Citizen Science
- 1. AI Examples in Biodiversity/Citizen Science Katina Michael Faculty of Engineering and Information Sciences @katinamichael | katinamichael.com
- 2. Artificial Intelligence • “The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.” https://en.oxforddictionaries.com/definition/artificial_intelligence
- 3. Biodiversity • “The variety of plant and animal life in the world or in a particular habitat, a high level of which is usually considered to be important and desirable.” https://en.oxforddictionaries.com/definition/biodiversity
- 4. What is citizen science? • “The collection and analysis of data relating to the natural world by members of the general public, typically as part of a collaborative project with professional scientists.” https://en.oxforddictionaries.com/definition/citizen_science
- 5. Biodiversity – Nature & Natural, Organic & Organism • Cycles • Carbon, Phosphorus, Nitrogen, Water, Oxygen • Biogeochemical • Pyramids & Chains • Ecological • Producers, Consumers • 1st, 2nd, 3rd tiers • Food • Networks, Webs, Flows • Species
- 7. My Experience with Citizen Science in ‘96-’03 • “The world is made up of dots…” • In 2003, I walked my way through my local suburb with a GPS strapped to my pram (a Magellan Gold)… • I linked the dots to the Council’s Strategic Plan • The following year Australia’s Geocoded National Address File (G-NAF) was made available by PSMA Australia • Indexed from ten diverse data sets • Officially recognised by government (ABS, AEC, Australia Post) • It was the year Google launched Gmail, and then Google Earth • There was no StreetView and no GoogleCars • We used MapBlast and MapQuest – remember?)
- 9. But what’s happened to us today? • We want to document “everything” • Every sunrise • Every person • Every location • Every animal • Every plant • Every other human • Every sand particle • Every moment • Every word we spoke • Every breath we took • Everything • What if we all wore cameras ALL the time? • Photoborgs • And what if all this data was fed back to a global brain? • What if one day we knew everything about our body, our homes, our earth? • And why are we documenting so profusely? • And audio recorders for speech? • “Speech to text” deep mining TIME
- 10. “Google Trekker” Inspired • 2 million miles project in VisitFlorida.com… • Go where no human has gone before • Go where no car can go • But these initially began with Human-Made Things • “Wardriving” through every neighbourhood • Now we are doing something more brazen • “Footdriving” (e.g. PokemonGo) • Next might well be “home-driving” • And “heart-driving”…
- 13. Every animal, every plant, every human, every tree, every fish etc. Then what?
- 15. Examples Projects – Size Matters Academics / Research Industry Government NGOs Citizens Data Collection Data Analysis Publication Project Address Humans Animals Plants Soil Water Air Ocean Fish Forest
- 16. Example Technologies vs Eco System Dimensions Visual/ Images Speech/ Sound Statistical Neural Networks Social Media Decision Support System RFID Tag GPS Track Deep Learning Trace Drones/ Sensors Humans Animals Plants Soil Water Air Ocean Fish Trees Birds Insects
- 17. Environmental Decision Support Systems • Applied Intelligence: July 2000, Volume 13, Issue 1, pp 77–91 • “Artificial Intelligence and Environmental Decision Support Systems” • An effective protection of our environment is largely dependent on the quality of the available information used to make an appropriate decision. • Problems arise when the quantities of available information are huge and nonuniform (i.e., coming from many different disciplines or sources) and their quality could not be stated in advance. • Another associated issue is the dynamical nature of the problem. Computers are central in contemporary environmental protection in tasks such as monitoring, data analysis, communication, information storage and retrieval, so it has been natural to try to integrate and enhance all these tasks with Artificial Intelligence knowledge-based techniques. • This paper presents an overview of the impact of Artificial Intelligence techniques on the definition and development of Environmental Decision Support Systems (EDSS) during the last fifteen years. • The review highlights the desirable features that an EDSS must show. The paper concludes with a selection of successful applications to a wide range of environmental problems. Goodchild, 2000
- 19. Notion of “People as Sensors”
- 20. Holderness & Turpin, 2014
- 21. Confirmed Flood Reports ≥ December 2015. https://petajakarta.org Holderness & Turpin, 2014
- 23. https://github.com/smart-facility/cognicity-server Holderness & Turpin, 2014
- 24. Holderness & Turpin, 2014
- 25. Holderness & Turpin, 2014
- 26. _ User Report Card Holderness & Turpin, 2015
- 27. PetaJakarta – Robert Ogie • Problem ID: Coping with underlying issue of flooding • Human-made canals, megacity population pressures, monsoons • Data Collection • Topological and hydrological map data • Incoming sources of social media (Tweets re: flooding) • Sensor devices based in devices in canals (idea of social machines) • Other sources: video footage from flood events (drone view) • Data Analysis • Social network analysis • Uses graph theory • Outcome • Towards a decision support system for hydrological systems optimisation
- 31. Problem ID: Koala’s dying – why? • “Koalas are struck by a different strain of the disease from that which affects humans – although it seems humans can catch the koala version through exposure to an infected animal’s urine. In koalas, the effects of chlamydia are devastating, including blindness, infertility and an infection known as ‘dirty tail’. • “Dirty tail is actually really awful," says Wilson. “The urinary tract gets inflamed and expands substantially; it’s incredibly painful. They get discharge and many koalas die.” http://www.bbc.com/earth/story/20160211-half-of-australias-koalas-now-have-chlamydia
- 32. Effects of Chlamydia in Koalas Source: http://ichef.bbci.co.uk/wwfeatures/wm/live/624_351/image s/live/p0/3j/52/p03j52ws.jpg Source: https://time2transcend.files.wordpress.com/2014/ 10/chlamydia-img_4448.jpg More about Chlamydia: https://www.youtube.com/watch?v=PQDucVT9QCk (citizen/class reporting)
- 33. What are academics proposing? • Drones (UAV) • AI • Statistical analysis • GPS locations and high-resolution imaging • The robot eyes have it: cutting-edge tool for koala conservation • Local councils are testing a new tool for protecting their vulnerable koala populations – drones equipped with artificial intelligence and backed by powerful statistical analysis. Source: https://www.qut.edu.au/news/news?news-id=110822
- 34. What is role of citizen? • 2K-10K koalas left • Population dropped by 90% in less than a decade • Anigram Trakka • School competition finalist • Raise awareness, collect data, contribute time • Focused on large-scale logging https://www.youtube.com/watch?v=jeLmWr9W5Qc&feature=youtu.be
- 37. • iNaturalist has grown exponentially in the past few years. There are nearly 250,000 users and about 3 million observations. • At gatherings, put on by the Texas Parks and Wildlife Department, people who've connected online meet in person to swap stories about giant walking sticks and learn about moths together. • “Stalin Murugesapandi, an engineer by day, is one of the citizen scientists here with his smartphone. He points out a moth with feathery antennae that's landed below some mayflies. • Murugesapandi's passion is photography. Some of the moths we're looking at — including one that's meringue yellow and another with bands of olive green.”
- 43. Tagging our trees and plants…
- 44. Tagging our food… Source: http://www.smartag.my/wp-content/uploads/2014/03/Meat-Traceability1.jpg
- 46. Submarine built for ocean exploration
- 47. Open-source insulin in conjunction with University of Sydney
- 48. Plastics made from prawn shells
- 49. High school student engaged in plat cloning Lipid extractions on Metschnikowia pulcherrima (yeast that makes palm oil)
- 54. Reflections and Questions • Still difficult to know what is true AI and what is not? • In many cases… it is simple data collection, and data manipulation in the Cloud • Crowdsourcing • Your only as good as the quality of your data- is bad data better than nothing at all? • Tracking animals • Should wild animals be let alone? • Facial recognition? Marks? Tags [not?] • Sophisticated techniques being applied • Deep Learning, Animal identification, Convolutional Neural Networks • Still driven by industry • Should be open source & accessible • Are there privacy issues to consider? • Who stores the data? • Whose history is it? • Need collaborative workflows • Citizen science good for raising alarm bells but not perhaps for making evaluations based on ongoing evidence unless guided by a research ethics process • Citizen training by professionals • What happens to citizens when data collection is being done by auto-machines? • Do humans focus on exception reporting?