Ground control to major policy makers: Unlocking user-centric AI’s potential for development — Part 1
By: World Bank
Tags: AI, Technology, Governance, Satellite Technology, Data

While a human can generally classify which parts of these images are green space, water, or even augmented structures, the sheer amount of imagery makes it impossible for human analysis to scale without algorithmic assistance. Artificial intelligence (AI) algorithms can help automate this process, making it more efficient. Decision-support platforms can connect stakeholders with the outputs of AI imagery analysis to help them understand the land cover composition of urban areas and where land use changes have occurred.

AI Cooking

While generating satellite image classifications with AI is both art and science, for the end user, it boils down to a dish made of three main ingredients: satellite imagery, labeled “ground truth” data (i.e., “labelling” the class of interest), and AI algorithms. The question is then whether the results are fit for purpose. Do they provide some useful insights at an acceptable level of accuracy and resolution for the user on the ground? These considerations change the required ingredients.

In tech speak, user-centric means getting something that ultimately addresses an issue or process that works for an actual set of people, potentially with limited technical specialization or just basic digital literacy. The use case refers to the insight or decision that users can actually apply the tool to in practice. For example, if the particular use case demands discerning whether solar panels are on roofs, higher resolution imagery will be needed as opposed to if the objective is to get an overall sense of green spaces. But putting this tool directly in the hands of users is likely to stimulate creativity around issues that matter to people on the ground!

Satellite imagery from different satellites can vary by spatial resolution (e.g., some satellites capture 30 cm resolution images, 1000 times the resolution of the Landsat images shown above), spectral resolution or which bands of light are captured by the satellite (e.g. red, green, blue, and other non-visible bands such as infra-red), and temporal resolution (e.g. how often a satellite revisits a certain location). Labeled “ground truth” data refers point/pixels that have been correctly classified into a given scheme by human analysts. Finally, different AI algorithms must be trained to perform the desired classification task.

The fact that many PhDs are now being earned based on which models are most fit for purpose for any given issue suggests that most of us will be looking to get what works off the shelf.

Find out in part 2 of our blog how satellite technology and data can be used by real people for real life applications.

The image below is a land cover classification available online from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS), with each pixel covering an area of 500m. The image below shows urban concentration not just within Ho Chi Minh’s boundaries in 2011, stretching into the Mekong delta. User-centric tools can now help most everyone derive granular and timely insights online.