Project Number: P1347

Status: Open for Sponsorship

Amira Program Manager: Dr Silvia Black


This proposal aims to deliver low-cost and scalable Artificial Intelligence (AI) processes, procedures and software tools for quantifying land cover and human activities and their temporal changes using multi-sensor remote sensing data. The information extracted will be used as input into a carbon accounting and prediction system.

The idea is to use satellite images and AI to quantify carbon footprint and rehabilitation on a more regular basis for mining companies and other industries without the need to send people to measure things on site. It is drastically low-cost, more consistent and provides carbon change analytics.

Benefits from sponsoring this project

the combination of remote sensing and AI offers some unique advantages as follows:

1. Speed and Efficiency: This approach can cover large areas quickly and provide current data, allowing for prompt decision-making and intervention.

2. Novelty: Conventional machine learning solutions fall short in revealing intricate layers of insights and dynamic patterns within satellite images. The groundbreaking deep learning technology introduced in this project has the capability to extract dependable geospatial intelligence regarding both built and natural environments, as well as human activities, in ways that were previously unattainable.

3. Consistency: Processing big remote sensing data is almost impossible for human eyes and subject to Erros due to fatigue. Using Al ensures consistent and reliable results.

4. Cost-Effectiveness: While traditional methods may require extensive fieldwork and resources, remote sensing and AI can be more cost-effective in the long run.

5. Scalability: Currently, the entire globe can be captured by satellites on a weekly basis so this approach can be expanded to cover larger regions or even multiple locations simultaneously.

6. Objective Insights: The process is based on data and algorithms, minimizing subjective interpretations and potential biases.

7. Future Potential: With ongoing developments in both remote sensing and AI, the accuracy and capabilities of this approach can continue to improve over time.

Overall, this innovative approach offers a much more advanced and efficient way to measure and track carbon emissions. By harnessing the power of the latest space imaging and AI technologies, we’re taking a crucial step towards a more sustainable and resilient future for ourselves and generations to come.

For more information please contact Program Manager Dr Silvia Black at