With Large Language Models (LLMs) gaining traction, our ability to see the world, to understand it, and to enact changes, gets even better.

This places a huge responsibility on our shoulders to use all these insights for the greater public good. We use data visualization and mapping to show people what and where things are happening. This enables them to get better informed, participate, and take timely actions, which is super important for all the bigger goals, like climate change mitigation and adaptation.

The combination of AI and ML with remote sensing info and ground truth data, allows us to identify what the algorithms predict. To improve those models over time, we need more field data through IoT sensors and field surveys, which is the way of the future.

Merging Data Pipelines

We use data for three things: planning, tracking, and evaluation. We also use it to communicate the problems to local communities through data visualization and mapping.

We ensure our data is presented in a useful and accessible manner. Based on these insights, decision- makers are able to take important decisions and allocate resources.

Traditionally, remote sensing scientists have been solving only a set of problems, which is quite difficult. With bigger datasets, gradually they encounter other problems as well. This is why, there’s a need of greater collaboration across disciplines for merging data pipelines, which can get quite daunting.

The work we do with remote sensing is challenging enough, though it presents new opportunities and horizons. We use data in a whole range of ways, starting from planning. Acting in isolation can lead to disruption, so we try to get a clear picture of what’s actually happening in the whole system. We have a System Change Lab that tries to look at how all of systems interact in society and track their progress.

“We must deeply understand the data needs of people making policy and infrastructure decisions, develop solutions and tools to meet those needs, and then work together to use analytics to drive more sustainable infrastructure and policy choices.”

Cooler Urban Infra

WRI, with support from Google.org, is working with cities and urban decision-makers to improve the uptake of cool infrastructure solutions.

Urban greening can have great impacts, in creating cooler space, and improving shade. The complexity for policymakers is that they have space limitations – they can’t overnight build a bunch of trees. With the combination of better measurement, we can actually measure the experience of temperature or the reflectivity of services at a very local level.

This is the era of geospatial. There’s a need to understand the data needs of key stakeholders, develop solutions and tools to meet their needs, and use analytics to drive more sustainable infrastructure choices. This, in the nutshell, is the need of the hour.

Disclaimer: Views Expressed are Author's Own. Geospatial World May or May Not Endorse it