Thoughts on Emerging trends in geospatial artificial intelligence (geoAI): potential applications for environmental epidemiology (VoPham et al., 2018)

VoPham et al. basically summarized the major trend of practical application of geoAI (mostly machine learning, deep learning, and data mining), and its specific practice regards to environmental epidemiology. By using the example of Lin et al.’s air pollution concentration study at Los Angeles, the authors illustrate how geoAI is used to processing the combination of big data from different sources, as well as efficiency computational process on pattern detect and modelling.

However, a question struggles me from their introduction to geoAI in practical use to the end of their envision of geoAI’s future: what is the exact difference between machine learning/data mining algorithms and geoAI. Is geoAI merely a combination of different machine learning or data mining algorithms? Or is it something more complicated than they illustrated in their article? Since from their example of modelling air pollution, Lin et al. (as the authors of original study) says that specialized geospatial data experts are still needed to decide what kind and quality of data can go in the modelling, to avoid the “garbage in garbage out” situation. To me, however, if a geoAI cannot reach a standard to identify and evaluate what should be included in the computational process, it is just a combination of different computational algorithms. Self-evolution and decision making process might be key to distinguish geoAI and combination of algorithms.

Some may argue that geoAI is only on its early stages, and so much more need to be done in order for geoAI to self-evolve and make decisions. However, if geoAI cannot be adaptive to different spatial or temporal instance, what is the need for an AI instead of a team of machine learning programmers and data miners? I believe reaching proper self-evolutionary ability to adapt different spatial and temporal instances, as well as making decisions of what comes in the modelling process, and what parameter or logic need to be change to adapt the different input variables is essential to call it geoAI, rather than a systematic geospatial data modelling algorithm.

Comments are closed.