Investigating the old city of Babylon: tracing buried structural history based on photogrammetry and integrated approaches

Abstract

Non-invasive techniques (e.g., photogrammetry and laser scanning) are becoming crucial methods for archaeological applications, such as monitoring, revealing, and preserving buried monuments. Visual analysis techniques based on photogrammetric-derived digital models have been applied in several scientific fields, particularly in digital archaeology. Examining the capabilities of these techniques (visual analysis techniques) for archaeological prospection and enhancing the effectiveness of existing approaches are therefore fundamental themes in digital archaeology. This research aimed to evaluate the capabilities of different visual analysis techniques derived from the Structure from Motion and multi-view stereoscopic (SfM-MVS) methods in identifying archaeological remains in the ancient city of Babylon, the capital of the ancient Babylonian civilization in Mesopotamia. These techniques can produce: 1) standalone raster images, such as hillshade, slope gradient, local relief, and sky-view factor (SVF); and 2) fusible new rasters that could produce by integrateing multi-layered topographic data calculated from gridded digital models towards detecting new potential findings. In this study, raw aerial images were collected using a DJI Phantom 4 Pro drone over the ancient city of Babylon in summer 2018. The raster layers obtained from the derived SfM digital models (i.e., DSM) are applied to trace buried monuments that could reveal possible new attributes of the ancient city structure. Using more than one raster image in prospecting is critical to confirm possible new archaeological remains. Although these raster images have the same fine grain (2.19 cm/pix), they have various merits in terms of highlighting topographic features in the investigated site. The combination of different visualization tools (e.g., DSM, differential openness, and SVF layers) into a new integrated single raster could simultaneously emphasis and advance the identification of archaeological features, such as edges, circular, and linear features in comparison with a standalone raster approach. Therefore, this study revealed several potential buried monuments, such as foundations, construction remains, and paths of the archaeological site. These non-destructive approaches for prospecting and detection archaeological remains should be applied more widely by the digital archaeological community to enhance the evaluation of archaeological change and promote comprehension of understudied archaeological areas worldwide.