Connecting the public: This mini program aims to promote the mainstream of biodiversity conservation by desensitizing current monitoring data in the industry and designing low threshold interactions for the traditional data labeling process. This allows the public to participate in the training process of biodiversity models in a more accessible and intuitive way through the mini program. On the one hand, the public can enjoy and learn about the most authentic protection monitoring images through the form of "playing games"; On the other hand, the power of the public can be utilized to continuously train a universal model of biodiversity, achieving the goal of citizen science in the process.
Through product design, 'Wild Friends' breaks down the process of annotating and verifying institutional data into tool based tasks, reducing the initial training costs of institutions. With simple guidance, volunteers or the general public can complete basic annotation content.
The first step is to check for the presence of animals (manually identified or judged by AI);
Step two, estimate the number of animals (manually determined);
Step three, select animals (manually or through AI evaluation of selection accuracy);
Step four, identify the name of the animal (manually selected or judged by AI);
Step five, randomly allocate cross validation in the background. Ensure the accuracy and consistency of data.
 

AI species recognition: This product uses AI recognition as the underlying technology, with endangered species as the core recognition object. It trains a large biodiversity recognition model that can support monitoring of mountains, rivers, forests, fields, lakes, grasses, and sands systems. The model is free and open to public welfare organizations dedicated to biodiversity conservation, such as research institutes, conservation organizations, and individuals. The reason why "wild friends" are so powerful is because they have a powerful "engine": YOLO World.
As the underlying universal model of 'wild friends', its primary characteristic is strong learning ability. It has powerful multimodal zero sample recognition and few sample recognition capabilities, which means it can quickly identify animal location regions and species information of multiple species through a small number of samples. For example, to recognize a new species, traditional models require thousands of photos and several days of training; YOLO World only requires a small number of photos and training iterations to achieve rapid adaptation.
Secondly, it has a high degree of tolerance. No longer limited to training and prediction of specific species, it has strong open vocabulary recognition ability and zero sample recognition ability, and can accurately identify and locate untrained species. For example, traditional models can only recognize trained species such as tigers and antelopes; The new model can also recognize snow leopards and foxes simultaneously - even if it has never trained these two animals before.
Another advantage of "wild friends" is that they spend less money. Common AI models heavily rely on high-performance acceleration cards, which result in high costs for both hardware environment and maintenance operations.
 

The database fosters collaboration by providing accessible tools for policymakers, law enforcement, researchers, and NGOs. This collaborative model could be replicated to facilitate cross-sector cooperation in combating global challenges such as climate change or poverty.

The platform is designed to assess the effectiveness of laws and enforcement efforts, such as the Endangered Species Act and Lacey Act. This framework can be applied to evaluate policy impacts in other regulatory areas, like labor laws or international trade compliance.

By collecting detailed data on federal prosecutions, this tool provides insights into enforcement patterns, sentencing trends, and legal applications. Similar systems could be developed to monitor prosecutions in areas like corporate fraud, tax evasion, or cybercrime.

The database integrates web design technologies and Esri ArcGIS to create a user-friendly interface for visualizing patterns and trends. This visual component can be adapted to various sectors to improve stakeholder engagement and decision-making, such as by local, state and federal wildlife and environmental agencies.

The database uses open-source technologies to aggregate and structure federal prosecution data. This approach is scalable and adaptable, making it an effective tool for organizing and analyzing enforcement data in other domains, such as human trafficking, organized crime, or other environmental offenses.

Tailored governance training provided WMA leaders practical conflict resolution skills and financial oversight capabilities. This empowered them to address governance challenges internally and create a sustainable operational framework.

Good governance is essential for the sustainable growth of Randilen Wildlife Management Area (WMA). Effective leadership, transparency, and accountability ensure that tourism revenues are managed efficiently, benefiting both conservation efforts and local communities. Strong governance structures help in decision-making, equitable distribution of resources, and the enforcement of policies that protect wildlife and natural habitats. By fostering community participation and fair representation, Randilen WMA can enhance trust among stakeholders, attract more investment, and promote long-term sustainability. Ultimately, good governance ensures that conservation and development go hand in hand, securing a prosperous future for both people and nature.