Empowering Wildlife Guardians: An Equitable Digital Stewardship and Reward System for Biodiversity Conservation using Deep Learning and 3/4G Camera Traps

要約

タイトル：
深層学習と3/4Gカメラトラップを利用した、野生生物保護のための公正なデジタル管理と報酬システムを用いた野生生物保護員の支援。

要約：
– 人間の行動により、約1,000,000種が危機に瀕し、多くの種が絶滅の危機にある。
– 環境や野生生物保護のために慈善団体や政府が多額の投資をしているが、野生生物の個体数は依然として減少している。
– 地域の野生生物保護員は、世界的な保全活動で重要な役割を果たしており、持続可能性を実現してきた実績がある。
– この論文では、動物たちが自分たちのお金を所有する「種間マネー」の概念に基づいた新しい解決策を提案している。
– 各種にデジタルツインを作成し、動物が保護員に必要なサービスを提供した場合、支払いが行われる仕組みを実現する。
– 南アフリカのLimpopo州のWelgevondenゲーム保護区に27のカメラトラップを設置し、12種類の動物を捕捉することに成功した。
– 各種に100ポンドの貯蓄口座を作成し、種が認識されるたびに1ペニーの支払いが保護員に対して振り込まれた。

要約(オリジナル)

The biodiversity of our planet is under threat, with approximately one million species expected to become extinct within decades. The reason; negative human actions, which include hunting, overfishing, pollution, and the conversion of land for urbanisation and agricultural purposes. Despite significant investment from charities and governments for activities that benefit nature, global wildlife populations continue to decline. Local wildlife guardians have historically played a critical role in global conservation efforts and have shown their ability to achieve sustainability at various levels. In 2021, COP26 recognised their contributions and pledged US$1.7 billion per year; however, this is a fraction of the global biodiversity budget available (between US$124 billion and US$143 billion annually) given they protect 80% of the planets biodiversity. This paper proposes a radical new solution based on ‘Interspecies Money,’ where animals own their own money. Creating a digital twin for each species allows animals to dispense funds to their guardians for the services they provide. For example, a rhinoceros may release a payment to its guardian each time it is detected in a camera trap as long as it remains alive and well. To test the efficacy of this approach 27 camera traps were deployed over a 400km2 area in Welgevonden Game Reserve in Limpopo Province in South Africa. The motion-triggered camera traps were operational for ten months and, using deep learning, we managed to capture images of 12 distinct animal species. For each species, a makeshift bank account was set up and credited with {\pounds}100. Each time an animal was captured in a camera and successfully classified, 1 penny (an arbitrary amount – mechanisms still need to be developed to determine the real value of species) was transferred from the animal account to its associated guardian.

arxiv情報

提供元, 利用サービス

arxiv.jp, OpenAI