A Deep Learning Method for the Automated Mapping of Archaeological Structures from Geospatial Data: A Case Study of Delos Island
The integration of artificial intelligence (AI), specifically through convolutional neural networks (CNNs), is paving the way for significant advancements in archaeological research. This study explores the innovative application of the so-called Mask Region-based convolutional neural network (Mask...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/14/6/220 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850167437884915712 |
|---|---|
| author | Pavlos Fylaktos George P. Petropoulos Ioannis Lemesios |
| author_facet | Pavlos Fylaktos George P. Petropoulos Ioannis Lemesios |
| author_sort | Pavlos Fylaktos |
| collection | DOAJ |
| description | The integration of artificial intelligence (AI), specifically through convolutional neural networks (CNNs), is paving the way for significant advancements in archaeological research. This study explores the innovative application of the so-called Mask Region-based convolutional neural network (Mask R-CNN) algorithm in a GIS environment, utilizing high-resolution satellite imagery from the WorldView-3 system. By combining these state-of-the-art technologies, this study demonstrates the algorithm’s effectiveness at recognizing and segmenting the ancient structures within the archaeological site of Delos, Greece. Despite the computational constraints, the outcomes are promising, with around 25.91% of the initial vector data (434 out of 1675 polygons) successfully identified. The algorithm achieved an impressive F1 Score of 0.93% at a threshold of 0.9, indicating its high precision in differentiating specific features from their environments. This research highlights AI’s crucial role in archaeology, enabling the remote analysis of vast areas through automated or semi-automated techniques. Although these technologies cannot supplant essential on-site investigations, they can significantly enhance traditional methodologies by minimizing costs and fieldwork duration. This study also points out obstacles, such as the complexity of and variability in archaeological remains, which complicate the creation of standardized data libraries. Nevertheless, as AI technologies progress, their applications in archaeology are anticipated to broaden, fostering further innovation within the discipline. |
| format | Article |
| id | doaj-art-18d58424bc394a0188ef3de03ab095dd |
| institution | OA Journals |
| issn | 2220-9964 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj-art-18d58424bc394a0188ef3de03ab095dd2025-08-20T02:21:12ZengMDPI AGISPRS International Journal of Geo-Information2220-99642025-06-0114622010.3390/ijgi14060220A Deep Learning Method for the Automated Mapping of Archaeological Structures from Geospatial Data: A Case Study of Delos IslandPavlos Fylaktos0George P. Petropoulos1Ioannis Lemesios2Department of Geography, Harokopio University of Athens, El. Venizelou 70, Kallithea, 17671 Athens, GreeceDepartment of Geography, Harokopio University of Athens, El. Venizelou 70, Kallithea, 17671 Athens, GreeceDepartment of Geography, Harokopio University of Athens, El. Venizelou 70, Kallithea, 17671 Athens, GreeceThe integration of artificial intelligence (AI), specifically through convolutional neural networks (CNNs), is paving the way for significant advancements in archaeological research. This study explores the innovative application of the so-called Mask Region-based convolutional neural network (Mask R-CNN) algorithm in a GIS environment, utilizing high-resolution satellite imagery from the WorldView-3 system. By combining these state-of-the-art technologies, this study demonstrates the algorithm’s effectiveness at recognizing and segmenting the ancient structures within the archaeological site of Delos, Greece. Despite the computational constraints, the outcomes are promising, with around 25.91% of the initial vector data (434 out of 1675 polygons) successfully identified. The algorithm achieved an impressive F1 Score of 0.93% at a threshold of 0.9, indicating its high precision in differentiating specific features from their environments. This research highlights AI’s crucial role in archaeology, enabling the remote analysis of vast areas through automated or semi-automated techniques. Although these technologies cannot supplant essential on-site investigations, they can significantly enhance traditional methodologies by minimizing costs and fieldwork duration. This study also points out obstacles, such as the complexity of and variability in archaeological remains, which complicate the creation of standardized data libraries. Nevertheless, as AI technologies progress, their applications in archaeology are anticipated to broaden, fostering further innovation within the discipline.https://www.mdpi.com/2220-9964/14/6/220machine learningconvolutional neural networks (CNNs)WorldView-3archaeologyDelos Island |
| spellingShingle | Pavlos Fylaktos George P. Petropoulos Ioannis Lemesios A Deep Learning Method for the Automated Mapping of Archaeological Structures from Geospatial Data: A Case Study of Delos Island ISPRS International Journal of Geo-Information machine learning convolutional neural networks (CNNs) WorldView-3 archaeology Delos Island |
| title | A Deep Learning Method for the Automated Mapping of Archaeological Structures from Geospatial Data: A Case Study of Delos Island |
| title_full | A Deep Learning Method for the Automated Mapping of Archaeological Structures from Geospatial Data: A Case Study of Delos Island |
| title_fullStr | A Deep Learning Method for the Automated Mapping of Archaeological Structures from Geospatial Data: A Case Study of Delos Island |
| title_full_unstemmed | A Deep Learning Method for the Automated Mapping of Archaeological Structures from Geospatial Data: A Case Study of Delos Island |
| title_short | A Deep Learning Method for the Automated Mapping of Archaeological Structures from Geospatial Data: A Case Study of Delos Island |
| title_sort | deep learning method for the automated mapping of archaeological structures from geospatial data a case study of delos island |
| topic | machine learning convolutional neural networks (CNNs) WorldView-3 archaeology Delos Island |
| url | https://www.mdpi.com/2220-9964/14/6/220 |
| work_keys_str_mv | AT pavlosfylaktos adeeplearningmethodfortheautomatedmappingofarchaeologicalstructuresfromgeospatialdataacasestudyofdelosisland AT georgeppetropoulos adeeplearningmethodfortheautomatedmappingofarchaeologicalstructuresfromgeospatialdataacasestudyofdelosisland AT ioannislemesios adeeplearningmethodfortheautomatedmappingofarchaeologicalstructuresfromgeospatialdataacasestudyofdelosisland AT pavlosfylaktos deeplearningmethodfortheautomatedmappingofarchaeologicalstructuresfromgeospatialdataacasestudyofdelosisland AT georgeppetropoulos deeplearningmethodfortheautomatedmappingofarchaeologicalstructuresfromgeospatialdataacasestudyofdelosisland AT ioannislemesios deeplearningmethodfortheautomatedmappingofarchaeologicalstructuresfromgeospatialdataacasestudyofdelosisland |