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Evaluating the Performance of Geographic Object-Based Image Analysis in Mapping Archaeological Landscapes Previously Occupied by Farming Communities: A Case of Shashi–Limpopo Confluence Area

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Thabeng, Olaotse Lokwalo  ORCID logo
Merlo, Stefania 


jats:pThe use of pixel-based remote sensing techniques in archaeology is usually limited by spectral confusion between archaeological material and the surrounding environment because they rely on the spectral contrast between features. To deal with this problem, we investigated the possibility of using geographic object-based image analysis (GEOBIA) to predict archaeological and non-archaeological features. The chosen study area was previously occupied by farming communities and is characterised by natural soils (non-sites), vitrified dung, non-vitrified dung, and savannah woody vegetation. The study uses a three-stage GEOBIA that comprises (1) image object segmentation, (2) feature selection, and (3) object classification. The spectral mean of each band and the area extent of an object were selected as input variables for object classifications in support vector machines (SVM) and random forest (RF) classifiers. The results of this study have shown that GEOBIA approaches have the potential to map archaeological landscapes. The SVM and RF classifiers achieved high classification accuracies of 96.58% and 94.87%, respectively. Visual inspection of the classified images has demonstrated the importance of the aforementioned models in mapping archaeological and non-archaeological features because of their ability to manage the spectral confusion between non-sites and vitrified dung sites. In summary, the results have demonstrated that the GEOBIAs ability to incorporate spatial elements in the classification model ameliorates the chances of distinguishing materials with limited spectral differences.</jats:p>


Peer reviewed: True

Acknowledgements: We appreciate the invaluable support we obtained from SANParks who let us into Mapungubwe National Park, and the assistance offered by its members of staff. We thank the DeBeers Group for accommodating us at Venetia Research Centre and permitting us to look for archaeological sites in the Venetia Nature Reserve. We also thank the Venetia staff for protecting us during the survey in the reserve and Thomas Huffman for sharing his data and taking us around the study area.

Publication status: Published

Funder: University of Botswana training department and Digital Globe Foundation


3709 Physical Geography and Environmental Geoscience, 37 Earth Sciences, 40 Engineering, 4013 Geomatic Engineering, 3701 Atmospheric Sciences, 15 Life on Land

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Remote Sensing

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