A conceptual introduction to geospatial, text, and image data

Just like we use different senses to holistically understand objects around us, a machine learning (ML) model also benefits from data coming from different types of sensors and sources. Having only one type of data (for instance, numeric or categorical) limits the level of understanding, predictability, and robustness of a model. In this section, we will present a more in-depth discussion of the business importance of different data types in building models, the associated challenges, and the preprocessing steps necessary to mitigate these challenges.

Geospatial AI

Geospatial understanding has had long-standing implications for decision-making in certain industries, including mineral exploitation, insurance, retail, and real estate. While the commercial importance of data science is well established, location-based AI is just beginning to gain recognition. The use of ML in improving business performance has brought...