Remotely Sensed Big Data and Iterative Approaches to Cultural Feature Detection and Past Landscape Process Analysis

• Published in: Journal of field archaeology 2020-02, Vol.45 (sup1), p.S27-S38
• Main Authors: Howey, Meghan C. L., Sullivan, Franklin B., Burg, Marieka Brouwer, Palace, Michael W.
• Format: Article
• Language: English
• Subjects: Food Storage; Great Lakes; Landscape Archaeology; Lidar; Maximum Entropy; Remote Sensing
• ISSN: 0093-4690; 2042-4582
• DOI: 10.1080/00934690.2020.1713435

The concept of "big" data is nothing new to archaeologists; we have long made a profession of collecting, organizing, and analyzing a surfeit of data describing everything from minute artifact attributes to landscape-wide environmental characteristics. Regardless of this abundance, we have and continue to confront the self-same problem inherent in "big" data, namely what analyses will actually help us use these data to advance understandings of past human behaviors. With burgeoning remote sensing technologies archaeology faces a new wave of "big" data, but how do these techniques improve our ability to make the inferential leaps to bridge the present to the past and bring new insights forward? We argue that, to date, remote sensing techniques (satellite, aerial, and unpersonned aerial imagery) have been applied somewhat narrowly to mostly high-resolution site-based research in archaeology. To truly unleash the capabilities of these techniques, and expand our capacity for wrangling "big" data to more fully investigate past patterns, we need to conduct iterative analyses incorporating remotely sensed data on bounded archaeological sites and regions and unbounded landscapes. A case study from the Late Precontact (ca. A.D. 1200-1600) period in the northern Great Lakes of North America detailing how such an iterative approach can be initiated is explored here.