CCF provides a holistic digital ecosystem integrating landscape-scale technology, training and community engagement to drive meaningful change, contributing to GBF Target 20 – Strengthen Capacity-Building, Technology Transfer, and Scientific and Technical Cooperation for Biodiversity.

A visualisation platform for real-time protected area management, helping managers make informed, conservation-related operational decisions. A community of users is now sharing best practices and innovative concepts, engaging EarthRanger as it extends beyond just elephant protection to a diversity of wildlife, ecological and community applications.

From soil moisture and water levels to animal migrations and habitat health, data from sensors travels via Cisco LoRaWAN gateways and Actility’s ThingPark™ Platform to central operations rooms, where platforms like EarthRanger provide a comprehensive 360-degree view of ecosystem health and threats. Previously, these have been donated by Cisco Inc. 
These LoRaWan gateways now support over 800 IoT sensors across protected areas. 

Actility  LoRaWAN Things Park Network ServerThe network server connects sensors, gateways and end-user applications and ensures reliable and secure data routing all along the LoRaWAN network. Along with the Operation Support System (or OSS), they are the brain that controls the complete LoRaWAN network

Hack The Planet acknowledge that our partnerships allow us to combine strengths, resources, and expertise, amplifying the impact and fostering innovative solutions. Collaborating creates shared value and builds networks, enabling mutual growth and sustainability.

Local involment:
The scanners send real-time alerts to the anti-poaching control room. These alerts can also be shared with local communities or neighboring farms, enabling them to act as third-party partners in anti-poaching efforts. By involving locals directly in the response process, the system fosters collaboration, increases situational awareness, and empowers communities to take an active role in protecting wildlife.

Scanneredge is a collaboration with Tech for Conservation organisation Smartparks, Management of national parks like Gonarezhou - Zimbabwe, park technicians, rangers(QRU) and the local community. Through this cross-sector partnership, we have demonstrated that ScannerEdge is ready for broader deployment, increasing the number of active national parks and total scanners in use.

Leveraging real-time alerts from ScannerEdge, a response unit can quickly assess and mitigate potential threats, such as poaching or other illegal activities.

ScannerEdge specializes in monitoring RF signals from mobile and satellite phones, as well as other communication devices, to detect human activity in remote areas.

Ensuring that ScannerEdge devices are properly installed and configured in the field, with thorough training for operators to maximize their effectiveness in detecting illegal human activities.

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.