Inclusive and participatory mapping of all stakeholders in the mangrove space in the five counties of Kwale, Mombasa, Kilifi, Tana River and Lamu. A series of meetings for sensitization on the National Mangrove Management Plan, and later facilitated formation of the national and five county committees. The committees were then facilitated in developing their workplans and executing some of the activities. This has since been picked up. 

An Act of Parliament that provide for the development and sustainable management, including conservation and rational utilization of all forest resources for the socio-economic development of the country and for connected purposes

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.

This building block aimed to establish a robust governance framework through the development and implementation of essential management tools. Financial management systems, such as QuickBooks, were introduced to streamline budgeting, accounting, and financial reporting. Additionally, standardized reporting templates and monitoring frameworks, along with key management tools such as the Code of Conduct, Human Resources Manual, Gender Policy, Environmental Policy, Court Policy, Procurement Policy, Dispute Resolution Manual, Stakeholder Engagement Plan, and Business Entrepreneurship Sustainability Tool were developed to ensure effective resource management in the WMA. These tools promote adherence to human rights, enhance data collection and evaluation, ensure transparent communication with stakeholders, foster meaningful engagement, incorporate gender considerations, and support sustainable business practices. Their integration has significantly improved financial accountability, operational transparency, and decision-making, thereby fostering trust among community members and external stakeholders.

MBOMIPA WMA in partnership with STEP have integrated the use of novel conservation technology the EarthRanger (ER) in management of HWC around MBOMIPA WMA. The established HWC Response Unit uses the ER app in their smartphone to record information related to the reported HWC incidents and the unit is being tracked live (through an InReach device) for accountability purposes and to help mobilize scarce resources efficiently. The use of ER has also simplified data collection, reduced errors associated with data entry from paper forms, and reduced the time required to process information. This technology allowed scouts to be more strategic and efficient in their patrols, leading to improved protection for both wildlife and crops. 

To start the game, a map representing the local area is first created. The facilitator begins by asking participants to describe their land and sketches features as they respond. Once all key elements are outlined, color-coded hexagonal tiles, called ‘parcels,’ are placed over the drawing to form the board. Each tile’s color reflects soil fertility, ranging from high to low fertility. These parcels generate trees and resources based on their fertility levels. The board is designed to represent various landscapes, including mature forests, young forests, savannahs, and rivers or lakes. Wildlife such as forest animals and fish can also be added. Additionally, extra tiles may be introduced to capture local specifics.

Next, players are assigned a certain number of family members to manage. For each family member, they choose activities such as farming, breeding, or fishing to gather resources. To encourage new perspectives, the facilitator invites players to select activities different from those they do in real life. The game proceeds in rounds alternating between rainy and dry seasons, with each season affecting activities and resource availability. Throughout the game, the facilitator introduces events and, at the end of each season, leads a brief debrief to discuss players’ feelings about the current situation.

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.
 

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.