A survey of published research, from the Essentia Memoria newsroom. It lists real papers, each looked up at its DOI registry; the game has not repeated their results.

Research survey

What AI research has found: finding and excavating historic sites

Happened: October 28, 2026 at 11:11 PM UTC · Published: October 28, 2026 at 11:11 PM UTC

12 published works on finding and excavating historic sites, each looked up at its DOI registry before it was listed here, with what each one reports in plain words.

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News from the Essentia Memoria game universe. What AI research has found: finding and excavating historic sites. 12 published works on finding and excavating historic sites, each looked up at its DOI registry before it was listed here, with what each one reports in plain words. This survey lists what each paper reports. The game has not repeated these results. A search finds papers that match its words, so a listed paper may use AI only in part. The search is limited to the fifty most-cited matches, so the newest results may be missing until they are cited. This report was written from the world record by a fixed template. The full story and its source records are on essentiamemoria.com.

The question

How does AI find ruins, mounds and drawings in the ground before anyone digs?

Claims, and where each one comes from

Where this research lives in the game (a place the world grammar builds)archaeological-site → heritage-site
Game simulation · Set in code src/lib/world-grammar/place-grammar.ts · resolvePurpose('archaeological-site')
Published works in this survey, each resolved at its DOI registry12
Real-world knowledge · Source [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12]
A classifier on radar and multispectral satellite images mapped ancient settlement mounds across the Cholistan desert.Orengo 2020
Real-world knowledge · Source [1]
A model picked likely spots in aerial photos of the Nazca desert; walking to them found hundreds of new figures in six months.Sakai 2024
Real-world knowledge · Source [2]
A Review of Artificial Intelligence and Remote Sensing for Archaeological ResearchArgyrou 2022
Real-world knowledge · Source [3]
Convolutional neural networks for archaeological site detection – Finding “princely” tombsCaspari 2019
Real-world knowledge · Source [4]
Using deep neural networks on airborne laser scanning data: Results from a case study of semi‐automatic mapping of archaeological topography on Arran, ScotlandTrier 2018
Real-world knowledge · Source [5]
Integrating Remote Sensing, Machine Learning, and Citizen Science in Dutch Archaeological ProspectionLambers 2019
Real-world knowledge · Source [6]
Deep Learning in Archaeological Remote Sensing: Automated Qanat Detection in the Kurdistan Region of IraqSoroush 2020
Real-world knowledge · Source [7]
Point Cloud Semantic Segmentation Using a Deep Learning Framework for Cultural HeritagePierdicca 2020
Real-world knowledge · Source [8]
Machine Learning for Cultural Heritage: A SurveyFiorucci 2020
Real-world knowledge · Source [9]
Artificial intelligence-assisted visual inspection for cultural heritage: State-of-the-art reviewMishra 2024
Real-world knowledge · Source [10]
An Analysis of Research Trends for Using Artificial Intelligence in Cultural HeritageGîrbacia 2024
Real-world knowledge · Source [11]
Machine Learning Arrives in ArchaeologyBickler 2021
Real-world knowledge · Source [12]

References, checked at their registry

[1] Orengo (2020). Automated detection of archaeological mounds using machine-learning classification of multisensor and multitemporal satellite data. Proceedings of the National Academy of Sciences. doi:10.1073/pnas.2005583117
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 11:11 PM UTC · the registry holds “Automated detection of archaeological mounds using machine-learning classification of multisensor and multitemporal satellite data” (2020)
[2] Sakai (2024). AI-accelerated Nazca survey nearly doubles the number of known figurative geoglyphs and sheds light on their purpose. Proceedings of the National Academy of Sciences. doi:10.1073/pnas.2407652121
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 11:11 PM UTC · the registry holds “AI-accelerated Nazca survey nearly doubles the number of known figurative geoglyphs and sheds light on their purpose” (2024)
[3] Argyrou (2022). A Review of Artificial Intelligence and Remote Sensing for Archaeological Research. Remote Sensing. doi:10.3390/rs14236000
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 11:11 PM UTC · the registry holds “A Review of Artificial Intelligence and Remote Sensing for Archaeological Research” (2022)
[4] Caspari (2019). Convolutional neural networks for archaeological site detection – Finding “princely” tombs. Journal of Archaeological Science. doi:10.1016/j.jas.2019.104998
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 11:11 PM UTC · the registry holds “Convolutional neural networks for archaeological site detection – Finding “princely” tombs” (2019)
[5] Trier (2018). Using deep neural networks on airborne laser scanning data: Results from a case study of semi‐automatic mapping of archaeological topography on Arran, Scotland. Archaeological Prospection. doi:10.1002/arp.1731
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 11:11 PM UTC · the registry holds “Using deep neural networks on airborne laser scanning data: Results from a case study of semi‐automatic mapping of archaeological topography on Arran, Scotland” (2019)
[6] Lambers (2019). Integrating Remote Sensing, Machine Learning, and Citizen Science in Dutch Archaeological Prospection. Remote Sensing. doi:10.3390/rs11070794
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 11:11 PM UTC · the registry holds “Integrating Remote Sensing, Machine Learning, and Citizen Science in Dutch Archaeological Prospection” (2019)
[7] Soroush (2020). Deep Learning in Archaeological Remote Sensing: Automated Qanat Detection in the Kurdistan Region of Iraq. Remote Sensing. doi:10.3390/rs12030500
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 11:11 PM UTC · the registry holds “Deep Learning in Archaeological Remote Sensing: Automated Qanat Detection in the Kurdistan Region of Iraq” (2020)
[8] Pierdicca (2020). Point Cloud Semantic Segmentation Using a Deep Learning Framework for Cultural Heritage. Remote Sensing. doi:10.3390/rs12061005
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 11:11 PM UTC · the registry holds “Point Cloud Semantic Segmentation Using a Deep Learning Framework for Cultural Heritage” (2020)
[9] Fiorucci (2020). Machine Learning for Cultural Heritage: A Survey. Pattern Recognition Letters. doi:10.1016/j.patrec.2020.02.017
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 11:11 PM UTC · the registry holds “Machine Learning for Cultural Heritage: A Survey” (2020)
[10] Mishra (2024). Artificial intelligence-assisted visual inspection for cultural heritage: State-of-the-art review. Journal of Cultural Heritage. doi:10.1016/j.culher.2024.01.005
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 11:11 PM UTC · the registry holds “Artificial intelligence-assisted visual inspection for cultural heritage: State-of-the-art review” (2024)
[11] Gîrbacia (2024). An Analysis of Research Trends for Using Artificial Intelligence in Cultural Heritage. Electronics. doi:10.3390/electronics13183738
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 11:11 PM UTC · the registry holds “An Analysis of Research Trends for Using Artificial Intelligence in Cultural Heritage” (2024)
[12] Bickler (2021). Machine Learning Arrives in Archaeology. Advances in Archaeological Practice. doi:10.1017/aap.2021.6
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 11:11 PM UTC · the registry holds “Machine Learning Arrives in Archaeology” (2021)

Review

Not peer reviewed. No human scientist has reviewed this survey. Its template and landmark summaries were written with an AI assistant (Claude); the papers it lists are real and each was checked at its registry, so anyone can follow the links and read them.

Each listed DOI was resolved, and its title, year and first author matched the listing.
Each work the search found is held by Crossref, a registry independent of the search.
Each number in the headline and summary is accounted for by a listed claim.
No candidate work was dropped: every one the desk found or named was confirmed by its registry.

Code version: deb216f03447

What the record does not say

This survey lists what each paper reports; the game has not repeated these results. A search finds papers that match its words, so a listed paper may use AI only in part. The search is limited to the fifty most-cited matches, so the newest results may be missing until they are cited.

How this was written

Landmark works named by the desk, then the most-cited works with a DOI that OpenAlex returns when titles and abstracts are searched for (archaeology OR archaeological OR geoglyph OR "cultural heritage" OR "ancient site" OR "ancient settlement") AND ("machine learning" OR "deep learning" OR "neural network" OR "neural networks" OR "artificial intelligence" OR convolutional OR "reinforcement learning" OR "language model" OR transformer); a hit is kept only if its own title names the field and its title or abstract names an AI method, as whole words, and the hits that name the most of these words in their title come first. Each candidate was looked up at Crossref (OpenAlex as second resolver) and its title, year and first author compared; a work the search found is shown only when Crossref holds it. Landmark summaries were written by the desk; the other works are listed by their own titles.

Source records: code:src/lib/world-grammar/place-grammar.ts#resolvePurpose · doi:10.1073/pnas.2005583117 · doi:10.1073/pnas.2407652121 · doi:10.3390/rs14236000 · doi:10.1016/j.jas.2019.104998 · doi:10.1002/arp.1731 · doi:10.3390/rs11070794 · doi:10.3390/rs12030500 · doi:10.3390/rs12061005 · doi:10.1016/j.patrec.2020.02.017 · doi:10.1016/j.culher.2024.01.005 · doi:10.3390/electronics13183738 · doi:10.1017/aap.2021.6