Unlocking the Archives: Turning Unstructured Documents into a Searchable Database for Groundwater Discovery
MapAid partnered with Databricks for Good to transform 700 scanned hydrogeological documents into a searchable database using multimodal AI. This serverless pipeline classifies documents and extracts water-related information, enabling researchers to quickly find historical studies and well records for improved groundwater prediction.
* MapAid partnered with Databricks for Good to classify and catalog nearly 700 scanned hydrogeological documents, transforming an unstructured collection into a searchable database. * Using multimodal AI, the team built a serverless pipeline that classifies documents and extracts water-related information directly from scanned page images. * Researchers can now locate relevant historical studies in seconds and access well records that feed directly into MapAid's groundwater prediction models, supporting improved drilling outcomes.