1- Predictive Analytics

WWF has used AI to analyze large amounts of data to predict where and when poaching incidents are likely to occur. This allows them to take preventative measures to protect endangered species such as elephants and rhinos.

Wildlife Insights

• Using AI to speed up camera trap data analysis through blank image filtering and species identification. 

• "Launched in December 2019 by Google and a host of conservation partners—including WWF and Conservation International—Wildlife Insights offers a simple upload system, cloud-based storage, and AI tagging and analysis. By harnessing the power of big data, the platform unites millions of photos from camera trap projects (conducted by conservation organizations, governments, and citizen scientists) to reveal how wildlife is faring—in near-real time."

SMART PAWS: Predicting poaching through AI

• "Protection Assistant for Wildlife Security (PAWS), predictive AI software that crunches massive amounts of data and leverages machine learning to suggest the most effective patrol routes. More accurate than human intuition, the software employs mathematical modeling and game theory.For rangers, deciding when and where to patrol to thwart poaching has been largely an educated guessing game — until now."

2- Image Recognition

WWF has developed AI algorithms to analyze camera trap photos and detect animal and plant species and their behavior. This helps them monitor wildlife populations and track changes in animal behavior and plant life over time.

WoodAI App: Tackling deforestation with AI

• “The H&M Group has revealed a new application made together with the World Wildlife Fund (WWF) that aims to support garment and textile factories in reducing their contribution to deforestation.The Wood Artificial Intelligence Application, also called the WoodAI App, utilises artificial intelligence (AI) to help identify wood species and provide information around biomass sourcing, WWF said in a release.” 

Smart Camera Traps

• The Smart Cameria Traps use Machine Learning (ML) to analyse photos in realtime on-device to detect animals and humans.The system sends alerts to rangers if something of interest was detected. They are equipped with a satellite uplink system that can operate anywhere on the globe without any dependency on a GSM / Wifi. The project is being piloted in Gabon.

Snow Leopard Early Warning System

• EWS (of snow leopards and other wildlife species) utilises automated image and data processing technologies. The system will monitor biodiversity hotspots located in close proximity to rural communities that are prone to wildlife attacks.

3- Natural Language Processing (NLP)

WWF has used NLP to analyze social media posts and other text data to understand public perceptions of conservation issues and how to better engage with the public.

Google AI- Media Text Monitoring for Timely Conservation Action 

•  “Media text monitoring for timely conservation action, funded by Google’s AI for Social Good program. WWF India is exploring the use of Artificial Intelligence-based approaches to automatically detect and geolocate emerging issues of conservation concern, including infrastructure development, from public newsfeeds and other websites.”

’HI vs. AI Behavior change study in Xe Sap NPA

•  “This programme compares Human Intelligence (HI) and Artificial Intelligence (AI) approaches in effectiveness for delivering behavioural change messaging around illegal wildlife trade in communities in rural Lao. HI Approach Focus group discussions Identify communities or actors to identify key individuals within a network who are influential, authoritative and trusted in a community Messaging on a topic is given to these individuals to distribute The focus group is accurately identifying the “best” individuals for transmitting the given message based on their subjective and qualitative knowledge of the network.”erian lynx conservation is used."

4- Precision Conservation

WWF has used AI to analyze satellite imagery and other data to identify areas that are most important for conservation efforts. This allows them to prioritize their efforts and use resources more efficiently.

Tracking water loss 

• “Using ML, the MapBiomas water project released its results after processing more than 150,000 images generated by Nasa’s Landsat 5, 7 and 8 satellites from 1985 to 2020 across the 8.5m sq km of Brazilian territory. Without AI, researchers could not have analyzed water changes across the country at the scale and level of detail needed. AI can also distinguish between natural and human-created water bodies.”

Earth observation-based analysis of the turbidity dynamics in selected North Sea estuaries

• EO and local Geodata are used for tracking the long-term turbidity dynamics and changes in estuaries.

5- Habitat Restoration

WWF has used AI to help predict the best locations to plant trees and restore habitats that have been degraded or destroyed. This helps ensure that restoration efforts are successful and that wildlife populations can thrive in their natural habitats.

Forest Management 

• “...artificial intelligence (AI) monitoring technology in Russia, supported by the WWF and IKEA forest partnership, detects logging operations in high-conservation value (HCV) forests, natural habitats of outstanding significance or critical importance, in a matter of minutes. A first for Russia, the technology will help tackle illegal logging and monitor forest cover loss, which reached 3.69 million hectares in 2019 alone.”

Forest Foresight 

• "Forest Foresight (FF) is an early warning system that uses radar satellite imagery and machine learning models to predict illegal deforestation in the next six months. FF enables local actors, such as governments, communities, and private and civil society actors, to intervene in the field before (more) unauthorised deforestation has taken place."

6- AI and Bioacoustics

WWF has used AI to record and analyze audio of wildlife. This helps them monitor wildlife populations and better understand animal behavior and communication methods.

Working to protect whales with AI and bioacoustics 

 “In northern B.C.’s Squally Channel, four underwater hydrophones capture the calls, clicks and moans of orcas, humpbacks, and fin whales.”

Nature Guardians 

Applied acoustic monitoring devices are integrated with artificial intelligence in order to monitor biodiversity and contrast poaching in 3 Nature reserves managed by WWF in Italy.

For more technology-related innovation stories, check out the WWF Conservation Technology Use Case Repository. The aim of this repository is to provide a live and updated database of planned/ongoing technology use cases within the global WWF network, improving transparency and ensuring greater efficiency and effectiveness of technology initiatives.