AI

This research proposes a spatially-aware generative adversarial network for building generalization in image maps, SpaGAN. It enhances the capacity to generalize buildings of varying sizes and shapes.

This research investigates what cartographic knowledge have been learned by deep learning models in map generalization.

This research proposes a multi-task learning method with graph convolutional neural networks. The proposed method formulates the building simplification problem as a joint task of node removal classification and node movement regression.

This review article investigates the capacity of the state-of-the-art NLP methods for location recognition.

This research proposes a general approach for extracting place names from tweets by integrating global gazetteers, deep learning, and pretrained transformer models.

Utilizing Deep Learning in Map Generalization

Geospatial information extraction from texts