Finally, we propose a general workflow of an Archaeological Object-Based Image Analysis (ArchaeOBIA) project, designed for stimulating the development of an operational routine for object-based applications in archaeology. We also present an assessment of the limits and potential of this method, built from a set of case studies from published and unpublished research.
KITEMATIC ALPHA VS SERIES
This article discusses a series of crucial theoretical issues linked to the incompleteness and the equi-/multifinality of the archaeological record and introduces the core concept of Diachronic Semantic Models (DhSM) as a means to integrate the long-term evolution of the archaeological landscape in the conceptual, digital and real world frameworks of the object-based approach. However, the lack of a theoretical background adapted to the specificities of the archaeological discipline is still preventing researchers from finding a shared language and a common protocol of investigation necessary to allow the comparability of the results. OBIA is intended to replicate human perception by using a protocol of (semi)automated image segmentation and classification. Object-based image analysis (OBIA) is rapidly emerging as a valuable method for integrating the data processing techniques and GIS approaches classically employed in archaeology. Finally, containerisation technologies proved to enhance the reproducibility and we used UML diagrams to describe representative work-flows deployed in our GIScience project. Among these resources, we focused on containerisation technologies and performed a shallow review to reflect on the level of adoption of these technologies in combination with OSGeo software. According to our project needs, we evaluated a list of practices, standards and tools that may facilitate open and reproducible research in the geospatial domain, contextualising them on Peng’s reproducibility spectrum. In this context, we explain our experience in an attempt to improve the reproducibility of a GIScience project. However, there are numerous difficulties for some studies to be reproduced easily (i.e., biased results, the pressure to publish, and proprietary data).
KITEMATIC ALPHA VS CODE
Nowadays, more and more authors consider that the ultimate product of academic research is the scientific manuscript, together with all the necessary elements (i.e., code and data) so that others can reproduce the results. It allows the results of previous studies to be reproduced, validates their conclusions and develops new contributions based on previous research. Scientific reproducibility is essential for the advancement of science. The provided examples demonstrate how new tools, including a user-friendly tool for containerization of computational analyses called Sciunit, can lower the barrier to reproducibility and replicability in the environmental modelling community.
Using this taxonomy as a guide, we argue that containerization is a key missing component in environmental modelling and is necessary to achieve the goal of computational reproducibility. We introduce these terms with illustrative examples using the Structure for Unifying Multiple Modeling Alternatives (SUMMA) hydrologic modelling framework along with cyberinfrastructure aimed at fostering reproducibility.
To this end, we put forth a taxonomy that defines an environmental modelling study as being either 1) repeatable, 2) runnable, 3) reproducible, or 4) replicable.
Despite the growing acknowledgment of reproducibility crisis in computational science, there is still a lack of clarity around what exactly constitutes a reproducible or replicable study in many computational fields, including environmental modelling.
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