Analytics

β€œBe Data Driven”.

One of the main products that from drones is data; data is key for impact assessment or decision-making. The β€œAnalytics” section of this toolkit centres on curating drone-related artificial intelligence and data products that inform decision-making. Areas where these drone analytics tools are used include surveillance, research, operations, project management and impact evaluation.

A man wearing a UNICEF shirt and vest observes a vertical take-off of a drone. Behind him, a large crowd of people and children observe.

Bioverse Labs (Brazil): Using drones and AI to support the sustainability of the Amazonian ecosystem πŸ”—

At Bioverse Labs, the team uses drone imagery and machine learning to identify and map non-timber species of trees (e.g., chestnut, copaiba, and Brazil nuts) in the Trombetas River basin in Para State in Northern Brazil. Bioverse aims to identify tree species that support food security and economic growth for the indegenious communities in the region. This methodology, and accompanying sustainable agro-forestry management plan will provide communities with a more direct connection to renewable income sources.

Image of a bee with Bioverse logo imposed ontop of it.

Use Bioverse labs GitHub Repository (Click here) πŸ”—

qAira (Peru): Using drones to monitor air quality from illegal mining areas in Peru πŸ”—

The team at qAIRa aim to assess air quality in Madre de Dios, through the measurement of pollutants; increase air quality monitoring in rural areas; create awareness about the quality of the air people breathe and its effects on health.

According to the World Health Organization, new data shows that 9 out of 10 people breathe air ontaining high levels of pollutants with an estimated 7 million deaths every year from exposure to polluted air causing diseases such as respiratory infection, heart disease and pneumonia.

Our technological solutions are connected to the internet, withstand harsh environmental conditions and are cost-effective compared to traditional solutions because they perform monitoring in larger areas and in less time.

Four girls walking through brown and rocky South American mountainside.

Use qAira GitHub Repository (Click here) πŸ”—

Rentadrone (Chile): Using aerial thermal imagery and machine learning for clean energy and agriculture uses πŸ”—

Rentadrone detects, classifies, and organizes errors and damaged modules in solar power panels using thermal aerial imagery to increase energy efficiency of solar power plants. Our team will also explore an alternate use-case of thermal imagery for agriculture and will collect and label aerial photos of crops which will be used to automatically detect diseases on crops.

Using thermal aerial imagery, our team detects little defaults that are represented by hot spots in solar panels. These hotspots can make the solar plants less efficient resulting in less energy output. Throughout our development process we realized the same technologies can be applied to detecting diseases in crops.

Aerial image of large rectangular array of solar panels in a desert.

Use Rentadrone GitHub Repository (Click here) πŸ”—

Submit open analytics tools or datasets to the UNICEF drones for SDGs toolkit πŸ”—

Do you have an open source collection of drone images or other data?

Join our efforts to make innovation for good more accessible and to scale impactful tools across the world. Visit The Drone for SDG Toolkit Project Submission Form. and fill out the information about your project. We will respond to your submission within ninety (90) days.