Unveiling Patterns of the Earth through Machine Learning and Geospatial Analysis

XRDS: For those unfamiliar with your work, can you describe the field you are working in, and what types of projects you have been involved in? Konstantin Klemmer: I work on machine learning specifically for geospatial data, which has a few properties that make it different from other data modalities. For instance, many models build on the statistical assumption that data are i.i.d (independent and identically distributed). However, this does not hold for geospatial data. Another challenge is that the training and test data could be close together. For instance, two weather stations located close to each other might have very similar readings. In machine learning, this might lead to the trained model being over-confident that it performs well on. © 2024 Association for Computing Machinery. All rights reserved.