Landscape Pattern Detection in Archaeological Remote Sensing

Abstract

Automated detection of landscape patterns on Remote Sensing imagery has seen virtually little or no development in the archaeological domain, notwithstanding the fact that large portion of cultural landscapes worldwide are characterized by land engineering applications. The current extraordinary availability of remotely sensed images makes it now urgent to envision and develop automatic methods that can simplify their inspection and the extraction of relevant information from them, as the quantity of information is no longer manageable by traditional "human" visual interpretation. This paper expands on the development of automatic methods for the detection of target landscape features-represented by field system patterns-in very high spatial resolution images, within the framework of an archaeological project focused on the landscape engineering embedded in Roman cadasters. The targets of interest consist of a variety of similarly oriented objects of diverse nature (such as roads, drainage channels, etc.) concurring to demark the current landscape organization, which reflects the one imposed by Romans over two millennia ago. The proposed workflow exploits the textural and shape properties of real-world elements forming the field patterns using multiscale analysis of dominant oriented response filters. Trials showed that this approach provides accurate localization of target linear objects and alignments signaled by a wide range of physical entities with very different characteristics.

Remote Sensing and Geosciences for Archaeology

“…The contributions by Sonnemann et al [14] and Traviglia and Torsello [15] provide an interesting glimpse of the research currently being carried out on this topic.…”

“…Traviglia and Torsello [15] instead applied a workflow of feature extraction and detection implemented in ©Matlab to estimate the location and periodicity of dominant linear features on georeferenced images depicting the cultural landscape in northern Italy that had been engineered with land cadastration since Roman times. The trials suggest that this approach provides the accurate location of target linear objects and alignments signaled by a wide range of physical entities with very different characteristics, which also can be later interpreted against historical documentations, old maps, and in situ archaeological evidence.…”

“…Some of the papers also explore the benefits of combining different approaches, e.g., optical remote sensing, aerial imagery and geophysical prospection [27,28], 3D surveys, and reconstruction in GIS environment [29]. In other cases, the focus is the demonstration of automatic detection tools and workflows [14,15].…”

“…The published Special Issue contains a collection of 21 articles; one is a review paper on piezoelectric/seismoelectric methods to identify near-surface targets [9], and six are feature papers that were solicited for submission to provide either an overview of specific domains of archaeological remote sensing (e.g., passive airborne optical imaging, [10]) or demonstrations of state-of-the-art remote sensing data [11], research methodologies [12,13], and processing tools and workflows [14,15] for landscape archaeology and archaeological prospection. Figure 1 compares the geographic distribution of the authors and research teams publishing in the Special Issue ( Figure 1a) and of the case studies and demonstration sites (Figure 1b).…”

An object‐based approach to support the automatic delineation of magnetic anomalies

“…In the last decades, geographical object-based image analysis (GEOBIA) has emerged as a discipline devoted to automation of extracting geospatial information from raster data (Blaschke et al, 2014;Hay & Castilla, 2008). While (semi)automated alternatives to manual delineation are relatively well-established in other fields, archaeology has only recently begun adopting them (Davis, 2019;Traviglia & Torsello, 2017). Such techniques have proved useful in several archaeological branches as airborne laser scanning (Cerrillo-Cuenca, 2017;Davis, Lipo, & Sanger, 2019;Freeland, Heung, Burley, Clark, & Knudby, 2016;Inomata et al, 2017;Sevara & Pregesbauer, 2014;Wang, Hu, Wang, Ai, & Zhong, 2017;Witharana, Ouimet, & Johnson, 2018), predictive modelling (Verhagen & Dr aguţ, 2012), detecting looting activities (Agapiou, Lysandrou, & Hadjimitsis, 2017;Lasaponara & Masini, 2018), and feature classification (Magnini & Bettineschi, 2019;Sevara & Pregesbauer, 2014).…”