Re-wilding Natural Habitats with Flying Robots, AI, and Metaverse Ecosystems

Abstract

Climate change and the destruction of natural habitats represent a truly big and existential issue for humanity, with the effects of it felt everywhere by everyone and by posing substantial challenges to tackle its negative impact at a local level. To date, localized ecological habitat analysis with UAVs (unmanned aerial vehicles, or “drones”) has been a complex process of many individual steps, manual tasks, further greatly limited by i) a lack of drone operators with advanced skill levels, ii) heavy drone equipment, iii) inadequate operational environments and restricted flying envelopes, iv) data processing time and storage limitations, and v) costly software. As part of our UAVs and habitat restoration research, we have developed innovative workflows that combine affordable turn-key consumer drones with free and/or low-cost off-the-shelf third-party AI applications and cloud-computing tools that can transform UAVs into autonomously “flying robots” and local drone enthusiasts into citizen scientists. Now they can collect aerial data, analyze it “on-the-fly,” and work with local wildlife experts and park rangers on improving the habitat they actually live in. That data as well as the geospatial models derived from those workflows has proven invaluable for localized ecosystem risk management and habitat restoration efforts as well as alleviating damage caused by climate change. A similar methodology can be used in the agricultural sector to monitor soil conditions, spread of infestations, measure crop yields, or determine the best time for harvest. This approach can easily be adopted by micro-businesses and/or individuals supporting high-tech, high-impact, low-cost employment within rural communities. We demonstrate current examples/use cases and will also look towards future innovation combining such autonomous flying robots (UAVs + third-party AI) together with AI analytics tools, cloud computing, and geospatial/metaverse applications. Finally, the digital assets and geospatial models derived in this way easily import into GSM platforms such as SketchFab to share them widely and, if need be, can be fully integrated into metaverse ecosystems such as spatial or similar.