Szczegóły publikacji
Opis bibliograficzny
Machine learning-based detection of archeological sites using satellite and meteorological data: a case study of funnel beaker culture tombs in Poland / Krystian KOZIOŁ, Natalia BOROWIEC, Urszula MARMOL, Mateusz RZESZUTEK, Celso Augusto Guimarães Santos, Jerzy Czerniec // Remote Sensing [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2072-4292. — 2025 — vol. 17 iss. 13 art. no. 2225, s. 1–24. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 21–24, Abstr. — Publikacja dostępna online od: 2025-06-28
Autorzy (6)
- AGHKozioł Krystian
- AGHBorowiec Natalia
- AGHMarmol Urszula
- AGHRzeszutek Mateusz
- Santos Celso Augusto Guimarães
- Czerniec Jerzy
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 160986 |
|---|---|
| Data dodania do BaDAP | 2025-07-10 |
| Tekst źródłowy | URL |
| DOI | 10.3390/rs17132225 |
| Rok publikacji | 2025 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Remote Sensing |
Abstract
The detection of archeological sites in satellite imagery is often hindered by environmental constraints such as vegetation cover and variability in meteorological conditions, which affect the visibility of subsurface structures. This study aimed to develop predictive models for assessing archeological site visibility in satellite imagery by integrating vegetation indices and meteorological data using machine learning techniques. The research focused on megalithic tombs associated with the Funnel Beaker culture in Poland. The primary objective was to create models capable of detecting archeological features under varying environmental conditions, thereby enhancing the efficiency of field surveys and reducing associated costs. To this end, a combination of vegetation indices and meteorological parameters was employed. Key indices—including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), and Normalized Archeological Index (NAI)—were analyzed alongside meteorological variables such as wind speed, temperature, humidity, and total precipitation. By integrating these datasets, the study evaluated how environmental conditions influence the visibility of archeological sites in satellite imagery. The machine learning models, including logistic regression and decision tree-based algorithms, demonstrated strong potential for predicting site visibility. The highest predictive accuracy was achieved during periods of high soil moisture variability and fluctuating weather conditions. These findings enabled the development of visibility prediction maps, guiding the optimal timing of aerial surveys and minimizing the risk of unsuccessful data acquisition. The results underscore the effectiveness of integrating meteorological data with satellite imagery in archeological research. The proposed approach not only improves site detection but also reduces operational costs by concentrating resources on optimal survey conditions. Furthermore, the methodology is applicable to diverse archeological contexts, enhancing the capacity to locate and document heritage sites across varying environmental settings.
