Latent representation learning for geospatial entities

• Main Authors: LEE, Ween Jiann, LAUW, Hady Wirawan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Geographic information systems; Data encoding and canonicalization; Data mining; Neural networks; Representation learning; Geospatial; Location sensitivity; Artificial Intelligence and Robotics; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/9843; https://ink.library.smu.edu.sg/context/sis_research/article/10843/viewcontent/tsas24.pdf
• Institution: Singapore Management University

Representation learning has been instrumental in the success of machine learning, offering compact and performant data representations for diverse downstream tasks. In the spatial domain, it has been pivotal in extracting latent patterns from various data types, including points, polylines, polygons...