Advances in artificial intelligence, hardware accelerators, and data processing architectures, continue to infiltrate the geospatial information sciences, with a transformative impact on many societal challenges. Recent breakthroughs in deep learning have brought forward an automated capability to learn representational features from massive and complex data, including text, images, and videos. In tandem, rapid innovations in sensing technologies enhance the collection of geospatial data in even higher resolution and throughput, supporting the observation, mapping, and analysis of different events and phenomena on the Earth's surface with unprecedented detail. Combined, these developments are offering the potential for breakthroughs in geographic knowledge discovery, impacting decision-making in areas such as humanitarian mapping, intelligent transport systems, urban expansion analysis, health data analysis and epidemiology, the study of climate change, handling natural disasters, the general monitoring of the Earth's surface, and achieving sustainability.
Gao et al. (2025) studied this question.