Prehistoric petroglyphs as well as inscriptions carved by nomads, travelers, and pilgrims in later periods can be found across the deserts and rocky mountains along the Red Sea coast of Saudi Arabia. These materials are useful for reconstructing inland networks and have provided important research evidence alongside other written sources and the archaeological remains of cities and settlements. However, due to the vastness of the survey area, more efficient survey methods are needed. During an archaeological survey of the early Islamic port city of al-Hawra’，Tabuk Province, Saudi Arabia, a joint Saudi–Japanese archaeological mission comprising the Saudi Heritage Commission, Waseda University, and Tokai University discovered more than 300 clusters of graffiti and petroglyphs, using Landsat OLI imagery. This is the first time that Landsat OLI imagery has been used to uncover previously unknown graffiti and petroglyph sites in Saudi Arabia, highlighting the usefulness of satellite remote sensing data as a powerful tool for identifying graffiti and petroglyph sites. This paper presents the successful discovery of graffiti and petroglyphs on the Red Sea coast of Saudi Arabia, using Landsat OLI imagery.

For better estimation of hazardous areas affected by landslide occurrences, this paper has proposed a Causal- and Trigger-factor Influence map (CTI map) using geospatial information for landslide hazardous areas assessment. The CTI map can be produced by the proposed CTI algorithm based on the Structural Equation Modeling (SEM). The training data set (i.e., endogenous variable) of past landslide occurrences induced by the Niigata Chuetsu earthquake in Nagaoka area are prepared. Causal factors (i.e., topography, surface geology, soil, slope, relief, elevation, aspect, drainage, and vegetation) are used as “observed variables” on geospatial information, while trigger factor caused landslides are regarded as “unobserved latent variable.” A path diagram in which the single-headed paths are drawn from the two exogeneous variables (i.e., causal and trigger factors) to endogenous variable (i.e., training data) is considered. Through the goodness-of-fit test on the path diagrams, the recall rate, the precision rate and the F1 score curves with respect to the training data set, the proposed path diagram can be accepted. Based on the structural and measurement equations in SEM, the causal and trigger factor influence maps are newly produced, respectively. As a final outcome, the differences between those maps are delineated on the CTI map which is effective in comparative evaluation of the influence between causal and trigger factors for landslide hazard assessment, supporting the installation of various measurement devices, as well as identifying candidate categories for subdivision within causal factor maps.