activity
20202026
most citedCross-attention Spatio-temporal Context Transformer for Semantic Segmentation of Historical Maps

7 citations · 12 across the 12 of their papers we have counts for

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Showing 2024Show all

6 papers · 1 filter

cs.CV2024

Self-supervised Video Instance Segmentation Can Boost Geographic Entity Alignment in Historical Maps

Xue Xia, Randall Balestriero, Tao Zhang +1

Tracking geographic entities from historical maps, such as buildings, offers valuable insights into cultural heritage, urbanization patterns, environmental changes, and various his…

cs.CV2024

MapSAM: Adapting Segment Anything Model for Automated Feature Detection in Historical Maps

Xue Xia, Daiwei Zhang, Wenxuan Song +2

Automated feature detection in historical maps can significantly accelerate the reconstruction of the geospatial past. However, this process is often constrained by the time-consum…

cs.CV2024

An Efficient System for Automatic Map Storytelling -- A Case Study on Historical Maps

Ziyi Liu, Claudio Affolter, Sidi Wu +2

Historical maps provide valuable information and knowledge about the past. However, as they often feature non-standard projections, hand-drawn styles, and artistic elements, it is…

cs.AI2024

A roadmap for generative mapping: unlocking the power of generative AI for map-making

Sidi Wu, Katharina Henggeler, Yizi Chen +1

Maps are broadly relevant across various fields, serving as valuable tools for presenting spatial phenomena and communicating spatial knowledge. However, map-making is still largel…

cs.CV2024

Probabilistic road classification in historical maps using synthetic data and deep learning

Dominik J. Mühlematter, Sebastian Schweizer, Chenjing Jiao +3

Historical maps are invaluable for analyzing long-term changes in transportation and spatial development, offering a rich source of data for evolutionary studies. However, digitizi…

cs.CV2024

StegoGAN: Leveraging Steganography for Non-Bijective Image-to-Image Translation

Sidi Wu, Yizi Chen, Samuel Mermet +4

Most image-to-image translation models postulate that a unique correspondence exists between the semantic classes of the source and target domains. However, this assumption does no…