Remote-sensing data-based Archaeological Predictive Model (APM) for archaeological site mapping in desert area, South Morocco

Nsanziyera, Ange Felix; Lechgar, Hicham; Fal, S.; Maanan, Mehdi; Saddiqi, Omar; Oujaa, Aïcha; Rhinane, Hassan

doi:10.1016/j.crte.2018.06.010

Cited by 11 publications

(7 citation statements)

References 31 publications

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“…However, the overall multi-temporal and multi-scale results were only visually cross-compared with each other. Moreover, data integration is often restricted to similar nature of data (e.g., data obtained from satellite sensors such as pan-sharpening techniques [12][13][14][15], data enhancement of geophysical prospection data [16][17][18][19], etc. ).…”

Section: Introductionmentioning

confidence: 99%

Beyond GIS Layering: Challenging the (Re)use and Fusion of Archaeological Prospection Data Based on Bayesian Neural Networks (BNN)

Αγαπίου

Sarris

2018

Remote Sensing

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Multisource remote sensing data acquisition has been increased in the last years due to technological improvements and decreased acquisition cost of remotely sensed data and products. This study attempts to fuse different types of prospection data acquired from dissimilar remote sensors and explores new ways of interpreting remote sensing data obtained from archaeological sites. Combination and fusion of complementary sensory data does not only increase the detection accuracy but it also increases the overall performance in respect to recall and precision. Moving beyond the discussion and concerns related to fusion and integration of multisource prospection data, this study argues their potential (re)use based on Bayesian Neural Network (BNN) fusion models. The archaeological site of Vésztő-Mágor Tell in the eastern part of Hungary was selected as a case study, since ground penetrating radar (GPR) and ground spectral signatures have been collected in the past. GPR 20 cm depth slices results were correlated with spectroradiometric datasets based on neural network models. The results showed that the BNN models provide a global correlation coefficient of up to 73%—between the GPR and the spectroradiometric data—for all depth slices. This could eventually lead to the potential re-use of archived geo-prospection datasets with optical earth observation datasets. A discussion regarding the potential limitations and challenges of this approach is also included in the paper.

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Section: Introductionmentioning

confidence: 99%

Beyond GIS Layering: Challenging the (Re)use and Fusion of Archaeological Prospection Data Based on Bayesian Neural Networks (BNN)

Αγαπίου

Sarris

2018

Remote Sensing

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“…In recent years, archaeologists and satellite-image analysts have conducted intensive scientific research in this interdisciplinary area and have achieved many results, in addition to Google Earth, they have used various satellite images for archaeological research [1], and have also used spatial information technology to identify, predict, and map archaeological sites in large-scale areas. The basic idea in identification and prediction methods is to use remote-sensing data [2] to develop a specific algorithm or analysis method to build a prediction model, thereby realizing the identification or prediction of existing and potential sites [3][4][5][6][7][8][9]. The specific methods are divided into manual implementation type and machine implementation type [10].…”

Section: Introductionmentioning

confidence: 99%

“…The specific methods are divided into manual implementation type and machine implementation type [10]. The analysis methods used include advanced classification algorithms (support vector machine and random forest), an analytic hierarchy process (AHP) [11], an improved Otsu segmentation algorithm with a linear Hough transform (LHT) [12], and various other techniques (spatial analysis, statistical techniques, and fuzzy logic functions [3]). Commonly used data sources include satellite image data (including historical satellite image data represented by CORONA satellite data [13,14]), elevation data, LiDAR data [15], UAV photogrammetric data [16], etc.…”

Section: Introductionmentioning

confidence: 99%

New Archaeological Discoveries Based on Spatial Information Technology and Cultural Analysis: Taking the Study of the Spatial Relationship between Ancient Chinese Capitals and the Natural Environment as an Example

Chen

Sun

et al. 2022

Remote Sensing

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How to combine science and technology with the humanities in the research on ancient cities to reveal ancient peoples’ urban planning thoughts is worthy of in-depth study. The capitals of the Western Han dynasty as well as the Sui and Tang dynasties were some of the greatest cities in the world at the time. This paper takes them as its subjects and puts forward a method to study the spatial relationship between ancient cities and the natural environment by combining spatial information technology and cultural analysis. Firstly, satellite images, elevation maps, urban ichnographies, and literature materials were collected and sorted to deeply understand the cultural thoughts involved in ancient urban planning; based on this, key element points were marked and rechecked on the spot, and the above drawings were accurately superimposed by GIS technology to form a geographic information base that integrated multisource information. Then, Python was used to construct a “decision model of spatial relationship between urban elements and natural elements”, and rules as well as parameters were set through man–machine collaboration. The decision model was used to test the geographic information base, and the information of strong correlations between urban objects and natural objects was outputted. The drawings were exported after screening, and a visual expression was realized with Illustrator software. The research results indicated that this analysis method was feasible, effective, and easy to promote. The new archaeological discoveries included eight important line segments with a 9:6 proportional relationship (which represents the balance of Yin and Yang) and two important line segments with a 9:5 proportional relationship (which represents the supreme imperial power) in the capitals of the Western Han dynasty as well as the Sui and Tang dynasties, and 16 contraposition lines in a positive direction or oblique 45° direction (which reflects the close relationship between urban elements and natural elements). We consider that the two capitals were intentionally closely related to natural environments such as mountain peaks and valley entrances in the planning stage, and that proportions and scales with profound humanistic meaning were selected. The capital of the Sui and Tang dynasties was specially aligned with the capital of the Western Han dynasty in space. These characteristics embody ancient Chinese Confucian cultural thoughts such as the “integration of yang and yin”, “harmony between nature and humans”, the “supremacy of emperors”, and the “use of numbers and shapes to convey meaning”.

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“…The assumption is that the selection of a site's location is influenced by specific environmental parameters. (Hatzinikolaou et al, 2003;Mink et al, 2009;Nsanziyera et al, 2018;Oyarzun, 2016). Willey (1953) pioneered the concept of archaeological site prediction in the Viru Valley.…”

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confidence: 99%

Utilizing the MaxEnt machine learning model to forecast urban heritage sites in the desert regions of southwestern Algeria: A case study in the Saoura region

Imen,

Halima,

Ayoub

et al. 2023

Archaeological Prospection

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The Saoura region, a renowned oasis in North Africa with heritage and archaeological significance of both national and universal importance, has witnessed a gradual deterioration over time. This research involves archaeological predictive modelling, aiming to create models capable of predicting the likelihood of discovering archaeological sites, cultural resources or evidence of past landscape use within a specific region. The study specifically focuses on predicting the locations of historical sites in the Sahara Desert, employing the maximum entropy (MaxEnt) model and six geo‐environmental criteria, including slope, elevation (digital elevation model [DEM]), distance from water, normalized difference vegetation index (NDVI), fertility and proximity to palm groves. The research is based on data from 58 historical sites and includes an assessment of the model's accuracy. The study highlights the remarkable significance of the fertility variable, which accounts for 94.1% of the predictive influence, making it the most crucial geo‐environmental factor in forecasting the location of historical sites in the Sahara. This underscores its pivotal role in shaping settlement patterns and subsistence strategies within the region, followed by the distance variable from the palm cove (3.2%) and the distance variable from the river (2.3%). The MaxEnt model proves to be suitable for predicting historical site positions, with an impressive average area under the ROC curve (AUC) score of 0.859, reflecting its effectiveness. Notably, areas with a high prediction probability are predominantly situated near the Saoura Valley. The study's findings hold the potential to assist planners in safeguarding archaeological sites by avoiding areas where historical sites are likely to be present.

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Remote-sensing data-based Archaeological Predictive Model (APM) for archaeological site mapping in desert area, South Morocco

Cited by 11 publications

References 31 publications

Beyond GIS Layering: Challenging the (Re)use and Fusion of Archaeological Prospection Data Based on Bayesian Neural Networks (BNN)

Beyond GIS Layering: Challenging the (Re)use and Fusion of Archaeological Prospection Data Based on Bayesian Neural Networks (BNN)

New Archaeological Discoveries Based on Spatial Information Technology and Cultural Analysis: Taking the Study of the Spatial Relationship between Ancient Chinese Capitals and the Natural Environment as an Example

Utilizing the MaxEnt machine learning model to forecast urban heritage sites in the desert regions of southwestern Algeria: A case study in the Saoura region

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