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Exploiting Polygon Metadata to Understand Raster Maps - Accurate Polygonal Feature Extraction

Authors:

Craig A. Knoblock,

Yao-Yi ChiangAuthors Info & Claims

SIGSPATIAL '23: Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems

Article No.: 109, Pages 1 - 12

https://doi.org/10.1145/3589132.3625659

Published: 22 December 2023 Publication History

Abstract

Locating undiscovered deposits of critical minerals requires accurate geological data. However, most of the 100,000 historical geological maps of the United States Geological Survey (USGS) are in raster format. This hinders critical mineral assessment. We target the problem of extracting geological features represented as polygons from raster maps. We exploit the polygon metadata that provides information on the geological features, such as the map keys indicating how the polygon features are represented, to extract the features. We present a metadata-driven machine-learning approach that encodes the raster map and map key into a series of bitmaps and uses a convolutional model to learn to recognize the polygon features. We evaluated our approach on USGS geological maps; our approach achieves a median F1 score of 0.809 and outperforms state-of-the-art methods by 4.52%.

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Cited By

Li WChen WZhang JLi CMa X(2025)Geological Object Recognition in Legacy Maps through Data Augmentation and Transfer Learning TechniquesApplied Computing and Geosciences10.1016/j.acags.2025.100233(100233)Online publication date: Mar-2025
https://doi.org/10.1016/j.acags.2025.100233
Saxton ADong JBode AJaroenchai NKooper RZhu XKwark DKramer WKindratenko VLuo S(2024)Accurate Feature Extraction from Historical Geologic Maps Using Open-Set Segmentation and DetectionGeosciences10.3390/geosciences1411030514:11(305)Online publication date: 13-Nov-2024
https://doi.org/10.3390/geosciences14110305

Index Terms

Exploiting Polygon Metadata to Understand Raster Maps - Accurate Polygonal Feature Extraction
1. Computing methodologies
  1. Computer graphics
    1. Image manipulation
      1. Image processing
2. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
      1. Geographic information systems

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Information

Published In

cover image ACM Conferences

SIGSPATIAL '23: Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems

November 2023

686 pages

ISBN:9798400701689

DOI:10.1145/3589132

Copyright © 2023 Owner/Author(s).

This work is licensed under a Creative Commons Attribution International 4.0 License.

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 December 2023

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Defense Advanced Research Projects Agency (DARPA)

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SIGSPATIAL '23

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SIGSPATIAL

SIGSPATIAL '23: 31st ACM International Conference on Advances in Geographic Information Systems

November 13 - 16, 2023

Hamburg, Germany

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Overall Acceptance Rate 257 of 1,238 submissions, 21%

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Cited By

Li WChen WZhang JLi CMa X(2025)Geological Object Recognition in Legacy Maps through Data Augmentation and Transfer Learning TechniquesApplied Computing and Geosciences10.1016/j.acags.2025.100233(100233)Online publication date: Mar-2025
https://doi.org/10.1016/j.acags.2025.100233
Saxton ADong JBode AJaroenchai NKooper RZhu XKwark DKramer WKindratenko VLuo S(2024)Accurate Feature Extraction from Historical Geologic Maps Using Open-Set Segmentation and DetectionGeosciences10.3390/geosciences1411030514:11(305)Online publication date: 13-Nov-2024
https://doi.org/10.3390/geosciences14110305

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