Page 19 - AI Ready – Analysis Towards a Standardized Readiness Framework

AI Ready – Analysis Towards a Standardized Readiness Framework

3.4 Case Study-4: Empowering Local Communities

This case study involves use cases which bring AI research along with multiple data stakeholders,
to infer impacts to local communities and utilities. Deployment of these solutions should take into
consideration effective ways of disseminating inferences among local stakeholders. Integration
of opensource and other innovative solutions to create value in the overall solution is required.

For example, Embrace the Forest [51] addresses the growing threats of wildland fires to
communities, biodiversity, and the environment by empowering forest fire resilience in wildland
territories and by activating a holistic, multi-stakeholder approach, adding high-end tools and
technologies, respecting local knowledge, and cultural and biological diversity.

In this use case, the designers combined the use of cameras to detect changes in smoke and
light to alert the potential fire in areas that have dense human activity, and satellite images for
remote areas with fewer human existence. The combination of the cameras paved the way for
responses of low latency and high accuracy, which guarantee the efficiency and effectiveness
of conservation efforts. Deployment of the cameras, by applying the knowledge of local
communities, e.g. determining specific areas that tend to have frequent fires, enhances the
accuracy of the model and also brings in the local community, facilitating the conversation
among stakeholders and the implementation of the project.

It is also noteworthy that the project's fire prevention efforts help safeguard jobs and economic
activities related to forestry, agriculture, and tourism. In the background of Brazil, forest fire
greatly impacts the performance of the powerlines. Once a forest fire is detected, for the sake
of safety, the electricity companies should cut off the power in case of detrimental emergencies.
Yet the cut-off is closely related to the key performance indicators (KPI) of the company, which
implies that the more times the fire happens, the lower KPIs companies gain, and the higher
loss of the amount of money.

With the help of this prevention and prediction model, not only the environment could be
protected, but also the local community could be empowered.

12