Publication
PNAS
Stage
Journal article
What we read
Summary of the abstract
Authors
Erel Uziel, Gideon Avni, Gary M. Feinman, Guy Bar-Oz, Yitzchak Y. Jaffe
Universities and research institutions
University of Haifa, Northwestern University, Field Museum of Natural History, University of Illinois Chicago, University of Chicago

Institution metadata: OpenAlex record ↗

What they did and found

Researchers analyzed Shivta by counting individual stones across 604 walls, using drone imagery and close-up photos to measure stone size, shape, and how stones were arranged, revealing clusters near churches and evidence of outward growth from a southern core.

Why it matters

The masonry patterns offer clues about town form and expansion, helping planners think about ancient settlements from stone choices and wall layouts rather than dates alone.

The study looks at each stone, not whole walls, to map how Shivta grew. It combines drone imagery with close photos to count stones and map sizes and layouts.

For practitioners, the lesson is that broad settlement patterns can come from repeated stone choices and wall spacing, not only from famous buildings or dates.

What remains uncertain

Abstract-only scope; no field validation reported.

Original sources · 1
  1. Quantifying ancient architecture: Stone-level machine learning reveals architectural variation and settlement growth at Shivta ↗PNAS · 2026-10-28

Check the original paper for its authors, methods, version and access terms.