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Machine learning-ready remote sensing data for Maya archaeology.

Žiga Kokalj1, Sašo Džeroski2,3, Ivan Šprajc4

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This study introduces a new multimodal annotated dataset for remote sensing of Maya archaeology, ideal for deep learning. It aids researchers in developing computer vision models for uncovering ancient Maya structures.

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Area of Science:

  • Archaeology
  • Remote Sensing
  • Computer Vision

Background:

  • The Maya civilization left extensive archaeological records.
  • Remote sensing technologies offer new ways to study these sites.
  • Developing AI models requires comprehensive, annotated datasets.

Purpose of the Study:

  • To create a multimodal annotated dataset for Maya archaeology.
  • To facilitate deep learning applications in the field.
  • To support the development of computer vision models for archaeological research.

Main Methods:

  • Collected airborne laser scanning (ALS) data (visualizations, canopy height model).
  • Acquired Sentinel-1 and Sentinel-2 satellite imagery.
  • Performed manual annotations of Maya structures (buildings, platforms, aguadas) as binary masks.

Main Results:

  • A comprehensive dataset covering the Chactún region in the Yucatán Peninsula.
  • Dataset includes five data types: ALS, Sentinel-1, Sentinel-2, and manual annotations.
  • Annotations precisely delineate locations and boundaries of ancient Maya structures.

Conclusions:

  • The dataset is ready for machine learning tasks like object recognition and semantic segmentation.
  • This resource will empower research teams to build or enhance computer vision models for Maya archaeology.
  • Facilitates advanced analysis of remote sensing data for archaeological discovery.