MapSAM2: Adapting SAM2 for Automatic Segmentation of Historical Map Images and Time Series
Xue Xia, Randall Balestriero, Tao Zhang, Yixin Zhou, Andrew Ding, Dev Saini, Lorenz Hurni

TL;DR
MapSAM2 is a novel framework that leverages visual foundation models to automatically segment and analyze historical map images and time series, effectively handling variability and limited annotations by treating maps as videos.
Contribution
It introduces a unified video-based approach for segmentation of historical maps and time series, including a new dataset and pseudo video generation method for limited supervision scenarios.
Findings
Effective segmentation of historical maps with few-shot fine-tuning
Successful linking of buildings across time series images
Demonstrated ability to generate pseudo videos from single maps
Abstract
Historical maps are unique and valuable archives that document geographic features across different time periods. However, automated analysis of historical map images remains a significant challenge due to their wide stylistic variability and the scarcity of annotated training data. Constructing linked spatio-temporal datasets from historical map time series is even more time-consuming and labor-intensive, as it requires synthesizing information from multiple maps. Such datasets are essential for applications such as dating buildings, analyzing the development of road networks and settlements, studying environmental changes etc. We present MapSAM2, a unified framework for automatically segmenting both historical map images and time series. Built on a visual foundation model, MapSAM2 adapts to diverse segmentation tasks with few-shot fine-tuning. Our key innovation is to treat both…
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Taxonomy
TopicsAutomated Road and Building Extraction · Advanced Image and Video Retrieval Techniques · 3D Surveying and Cultural Heritage
