Exploring how Large Language Models can Improve Search in Spatial Data Infrastructures
Every day, a huge amount of spatial data is produced by public agencies, research institutes, companies, and other data providers. Spatial Data Infrastructures (SDIs) have been set up at local, national and international levels to bring these data and its metadata together and make it easier to find. Metadata standards have matured and been adopted along the way. And yet, users still struggle to find the right datasets.
Can you remember the last time you were looking for a dataset? Perhaps you had to visit several geoportals or open data portals, each with its own interface and layout. You typed a few keywords and hoped they matched something in the metadata. Woe unto you if you used a synonym! Searching for “global warming datasets” will not necessarily find a record described as “greenhouse effect data”. Your chances are even worse if you searched in English while the metadata was written in German, French or any other different language. And if you did get a keyword right, you probably faced a long, poorly ranked list of results and had to open each one and check whether it’s relevant.
If you can relate to these frustrations, you are not alone. Many users spend a lot of time searching for data before they can use it. This brings us to this week’s challenge:
How can Large Language Models (LLMs) improve data discovery in SDIs?