Using AI to Track Environmental Damage
Artificial intelligence (AI) and satellite imagery are coming together to monitor environmental changes in forests, oceans, and other ecosystems in unprecedented detail. This AI environment monitoring is enabling more rapid and granular tracking of deforestation, wildlife declines, coral bleaching, and other concerning shifts.
The combination of AI and remote sensing data offers immense promise for conservation efforts worldwide.
Tracking Deforestation with Computer Vision
Tropical rainforests around the globe are facing alarming rates of clearing. Traditionally, conservation groups have relied on medium-resolution satellite data to estimate forest loss over time. However, this approach lacks precision and can miss small-scale logging and land conversion happening under the forest canopy.
Enter AI. New computer vision algorithms can now analyze high-resolution satellite and aerial imagery to identify subtle signs of human incursion into forests. This could include small clearings, logging roads, or even individual fallen trees. Researchers can train neural networks on many examples of these patterns so the AI learns to spot them automatically in new images.
The results are much more detailed maps of deforestation and forest degradation previously hard to detect. Conservationists can then use this intelligence to respond more quickly to new incursions and target their limited resources more effectively. Over time, the goal is to slow or even reverse the loss of vitally important tropical forests.
Tracking Wildlife Population Changes
Another emerging use case for AI environment monitoring is assessing changes in the distribution and abundance of wildlife species. Again, satellite imagery combined with computer vision algorithms offers an efficient way to cover very large areas systematically.
For example, one research group trained a convolutional neural network to identify and count seals on aerial photos of Antarctic ice flows during breeding season. Compared to traditional visual counting, the AI achieved a 96% accuracy rate while saving significant time and effort. Other projects have focused on spotting and tracking whales in drone and aircraft photos.
Meanwhile, on land, AI projects are underway to automatically detect and monitor elephants, rhinoceroses, giraffes, and other large mammals facing population declines and habitat loss. Trail camera data is being used to train algorithms to recognize different species. The trained AI can then scan satellite images for signs of animal presence, density, and movements over time.
This technology enables much wider geographical coverage than ground surveys. It also removes human bias and fatigue that can creep in with visual counting. As training data quality and quantity improve, AI will become an indispensable tool for getting population estimates right and guiding effective intervention.
Monitoring Coral Reef Health
Below the ocean surface, AI and satellites are beginning to shed light on changes happening in largely invisible coral reef ecosystems. Coral bleaching events triggered by marine heatwaves are occurring more often due to climate change and wreaking havoc underwater.
One research team from Australia developed an AI system to automatically assess bleaching levels across the Great Barrier Reef. They trained a deep neural network on thousands of underwater survey images depicting healthy corals and different extents of bleaching. When they applied this AI to satellite data, it generated regional bleaching risk maps with over 90% accuracy.
Similar projects are working to map seagrass and kelp forests, which also provide vital nursery and shelter habitats for marine life. The next frontier will be using AI to track populations of specific fish species that rely on these threatened habitats.
Empowering Conservation Around the Globe
These examples only scratch the surface when it comes to AI’s potential to revolutionize environmental monitoring and conservation. Every week new advances are happening in computer vision, object detection, simulation, and predictive modeling. Meanwhile, satellite and drone coverage of the planet is growing exponentially. The synergies are ripe for machine learning innovations to preserve biodiversity and ecosystem health.
Of course, technology is only one piece of the puzzle – policy change, sustainable economic development, public engagement, and law enforcement also play crucial roles in addressing environmental threats. However better data and evidence can help mobilize interventions exactly when and where they are needed most. AI environment monitoring is set to become an indispensable tool for maximizing limited conservation resources.
