Public awareness on environmental and biodiversity conservation
BirdLife South Africa has an extensive network of members, supporters, and birders locally and abroad.
BirdLife South Africa has a membership of over 6000 people, and an extensive reach of over 60 000 people through social media. BirdLife South Africa is well-regarded in South Africa and abroad as an authority on birds, birding, and bird conservation.
Effective branding, messaging, donor servicing, and communications on a consistent basis
Maintaining networks of supporters is imperative for the success of funding appeals.
The BirdLife South Africa Community Bird Guides are well regarded and respected members of the South African birding community.
The foundation for the success of the BirdLife South Africa Community Bird Guide Relief Fund was the regard that the birding community has for the guides. They are treasured and respected among birders, and there was a high level of support for this project and the guides before the pandemic struck.
A long-running, well-run, and well-publicized community benefit project with a few notable graduates who have become flagbearers for the project and for BirdLife South Africa.
Maintaining public support for projects on a consistent basis is important for future support.
The BirdLife South Africa Community Bird Guides are well regarded and respected members of the South African birding community.
BirdLife South Africa has an extensive network of members, supporters, and birders locally and abroad.
AI-based crack gauge for rockfall
AI-based crack guage
KNPS
AI-based crack gauge
TV chosun
AI-based crack gauge for rockfall is a device that monitors the occurrence of rockfall and the crack displacements in real-time by installing an observation sensor in a rockfall risky area located along the trail. Since 2013, automatic and manual crack gauges have been installed on steep slopes with a high risk of collapse, and 525 units are currently in operation at 174 locations. The rockfall measuring device is divided into risk levels of 'interest, caution, alert, and serious'. In the interest stage, regular and frequent inspections are carried out. In the caution stage where cracks are less than 5 mm and less than 2°, monitoring is strengthened. In the alert stages, precise investigation and action plans for the disaster are prepared. In the serious stage, the adjacent trails are controlled and emergency measures such as rockfall removal are implemented.
Prior to the installation of the AI-based crack gauge, a dedicated investigation team composed of geologists and disaster prevention experts was established in advance to systematically manage rockfalls and steep slopes to investigate areas with risk of rockfall accidents along the trails of national parks. In addition, the safety hazardous areas were graded from A to E according to the degree of risk, steepness, and other geologic characteristics and converted into databases.
81 rockfall events have occurred in the national park during the last 10 years, resulting in 3 deaths and 6 injuries, damaging properties of about KRW 2.1 billion. However, since 2018, when the AI-based crack gauge was used, there has been no fatality or injury to visitors due to rockfalls. In addition, it took a lot of time and labor to inspect all the crack gauges installed throughout the national park one by one. With the saved time park rangers can concentrate more on park other management activities, which greatly improved the internal satisfaction.
AI-based Intelligent CCTV is a scientific safety management system that uses deep learning technology to control emergencies in real-time image analysis. By recognizing and analyzing abnormal behavior patterns, such as intrusion, screaming, wandering, etc., a warning broadcast is immediately sent to the site and delivered to the control system, following the emergency responses.
In addition, in the case of marine/coastal national parks with a high risk of safety accidents due to tides and tides, the broadcast of tide times is automatically issued to the site. Intelligent CCTV was installed in 2020 and is currently being operated in 89 places in 15 national parks.
The most important enabling factor is to select the optimal location where the equipment can be operated effectively. Intelligent CCTV was installed by selecting areas where drowning accidents occurred frequently in the past. Another success factor is having a set of systems to deal with emergencies. When the AI alarm system is activated, the general control center in KNPS HQ checks real-time streaming to quickly grasp the situation and then rescue teams in national parks on the spot to start rescue operations.
AI-based intelligent CCTV is a scientific safety management system using deep learning technology. In order to continuously improve the accuracy of deep learning, experts continue to maintain the software and provide technical support in the field so that it can be managed stably. As data for deep learning is accumulating, it is expected that the operating level of the system will be increased. Based on these achievements and limitations, it is necessary to improve the numerous CCTVs that have been monitored by manpower using this innovative technology gradually in connection with the KNPS safety management system.
