Location Encoding

Location encoding focuses on representing geographic coordinates as numerical vectors suitable for machine learning models, enabling the integration of spatial data into various applications. Current research emphasizes developing robust and efficient encoding methods, often employing neural network architectures like variational autoencoders and convolutional neural networks, and evaluating their performance across diverse datasets and tasks using benchmarks like LocBench. This field is crucial for advancing geospatial artificial intelligence (GeoAI), improving accuracy and generalization in applications ranging from remote sensing and environmental modeling to trajectory analysis and resource management. The development of unified frameworks and bias-aware evaluation metrics is driving progress towards more reliable and equitable GeoAI systems.