Apply natural language processing, gazetteer lookup, and geocoding to extract locations from unstructured data sources.
This chapter surveys semi-structured and unstructured geospatial data, emphasizing their formats, challenges, and analytical approaches. Semi-structured data formats, such as JSON, do not follow rigid schemas but retain internal organization that supports spatial processing. These formats underpin many widely used datasets, including OpenStreetMap, and can represent both object-based and network-based spatial models. Unstructured data, including text, imagery, sensor streams, and point clouds, lack standardized formatting and must be transformed or enriched before spatial analysis is possible. For instance, crowdsourced or drone-collected imagery can be processed using Structure from Motion (SfM) to reconstruct 3D surfaces and terrain models. Textual data, such as social media posts or institutional reports, can be mined for geographic content using natural language processing techniques like named entity recognition and geoparsing. The chapter also considers recent developments in AI, including deep learning methods for image classification, segmentation of point clouds, and modeling spatiotemporal patterns from sensor data. Finally, it discusses the emerging role of multimodal models that integrate visual and textual information in geospatial workflows. Together, these tools and methods enable the use of increasingly diverse data sources in spatial analysis, broadening both the scope and depth of geographic inquiry.