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20212023
most citedSynthetic Map Generation to Provide Unlimited Training Data for Historical Map Text Detection

13 citations · 13 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2023

The mapKurator System: A Complete Pipeline for Extracting and Linking Text from Historical Maps

Jina Kim, Zekun Li, Yijun Lin +3

Scanned historical maps in libraries and archives are valuable repositories of geographic data that often do not exist elsewhere. Despite the potential of machine learning tools li…

cs.CL2022

SpaBERT: A Pretrained Language Model from Geographic Data for Geo-Entity Representation

Zekun Li, Jina Kim, Yao-Yi Chiang +1

Named geographic entities (geo-entities for short) are the building blocks of many geographic datasets. Characterizing geo-entities is integral to various application domains, such…

cs.CV2022

HPS-Det: Dynamic Sample Assignment with Hyper-Parameter Search for Object Detection

Ji Liu, Dong Li, Zekun Li +4

Sample assignment plays a prominent part in modern object detection approaches. However, most existing methods rely on manual design to assign positive / negative samples, which do…

cs.CV2021★ 13 cited

Synthetic Map Generation to Provide Unlimited Training Data for Historical Map Text Detection

Zekun Li, Runyu Guan, Qianmu Yu +2

Many historical map sheets are publicly available for studies that require long-term historical geographic data. The cartographic design of these maps includes a combination of map…

cs.AI2021

An Automatic Approach for Generating Rich, Linked Geo-Metadata from Historical Map Images

Zekun Li, Yao-Yi Chiang, Sasan Tavakkol +4

Historical maps contain detailed geographic information difficult to find elsewhere covering long-periods of time (e.g., 125 years for the historical topographic maps in the US). H…