How can you use machine vision to automate climate change monitoring?

1 Why monitor climate change?

Climate change is one of the most pressing challenges of our time. It affects the natural systems that support life on Earth, such as the water cycle, the carbon cycle, the biosphere, and the atmosphere. It also has direct and indirect consequences for human health, food security, economic development, and social stability. To adapt to the changing conditions and mitigate the risks, we need reliable and timely information on how climate change is affecting different regions, sectors, and communities. That's where climate change monitoring comes in.

2 What is climate change monitoring?

Climate change monitoring is the systematic observation and measurement of climate variables and indicators over time and space, which can be conducted at different scales and using various methods. The primary goals of climate change monitoring are to detect and quantify changes in the climate system, assess the impacts and vulnerabilities of the environment and society, evaluate the effectiveness of adaptation and mitigation actions, and provide evidence for policy making and planning.

4 What are some examples of machine vision applications?

Machine vision has been applied to various aspects of climate change monitoring, such as glacier monitoring, forest monitoring, and urban monitoring. For glacier monitoring, machine vision can help measure changes in size, shape, volume, and movement over time using satellite imagery or drone footage. Image segmentation, feature extraction, and change detection can be used to identify and quantify glacier features and dynamics. For forest monitoring, machine vision can help monitor health and diversity using aerial or ground-based imagery. Image classification, object detection, and semantic segmentation can be used to identify and count trees, species, and canopy cover. Additionally, machine vision can detect and measure the effects of deforestation, degradation, fire, pests, and diseases on forests. For urban monitoring, machine vision can help assess the impacts of climate change on urban areas using satellite or street-level imagery. Image enhancement, edge detection, and pattern recognition can be used to analyze changes in urban morphology, infrastructure, and land use. Machine vision can also evaluate the performance of urban adaptation and mitigation measures such as green roofs, solar panels, and bike lanes.

5 How can you get started with machine vision?

Machine vision is a rapidly evolving and expanding field that offers many opportunities and challenges for climate change monitoring. To get started, you need to define your objectives and questions, select the appropriate data sources and formats, choose the suitable machine vision tools and techniques, apply the machine vision algorithms and models to your data, validate and verify the results and outputs, and interpret and communicate the findings and implications. Although machine vision is not a magic bullet that can solve all the problems of climate change monitoring, it can be a powerful tool to automate and improve your climate change monitoring efforts and outcomes. However, there are limitations such as data quality, availability, algorithm accuracy, reliability, transparency, ethical and social implications.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?