Mappa. Pisa in the Middle Ages

This volume represents the third edition of a work cycle that started in 2006 for my PhD thesis. The thesis was presented in 2010 (first edition, GATTIGLIA 2010), partially published as a summary monograph in 2011 (second edition, GATTIGLIA 2011) or in articles (GATTIGLIA 2012, GATTIGLIA 2012a, GATTIGLIA G. 2011a), and now (third edition) takes the form of a more comprehensive publication in the light of new data. Over the past two years, the work study on Pisa, not only relating to the Middle Ages, continued within the MAPPA (Metodologie Applicate alla Predittività del Potenziale – Methodologies Applied to Archaeological Potential Predictivity) project, allowing a widespread collection of data thanks to which it was possible to explain more fully the hydrogeological, geomorphological and topographic context and to check (and in many cases change) part of the assumptions made. Archaeology, albeit slowly, is moving towards Big Data, i.e. enormous amounts of machine readable data, continuously produced , which can modify theories, conclusions and assumptions at any time and develop new applications for archaeology. We no longer live in an age in which printed texts have a long life cycle before becoming outdated; new data are enough today to contradict or validate the assumptions made. Archaeology is closer and closer to science, not only because it uses scientific analysis methods but because it is based on falsifiable hypotheses, to put it as Popper would say. For this reason, the data analysed here are published as open data on the MOD (ANICHINI et alii 2013) (the open data archive of Italian archaeology www.mappaproject.org/mod) or as searchable data on MAPPA Web GIS (MAPPAgis www.mappaproject.org/webgis). In this first introductory chapter, the history of urban archaeology in Pisa will be briefly presented. The second chapter will provide a broad outline of the territorial context and the landscape. The rivers and marshy areas will be analysed in order to understand how the environment influenced the development of the medieval city for better or for worse. Since man was not a passive responder to these events, the study of the port system and road networks will help understand which solutions were taken to draw the geographical benefits and generate economic and commercial profits. The third and last chapter is divided into two parts. The first part will illustrate the great urban transformations throughout the period ranging from the end of the Roman Age (VI century) to the Florentine conquest (start of the XV century). Although it is still difficult to have a clear picture of the Roman and early-medieval urban design of Pisa, it is nevertheless possible to understand some of its nodal points, to interpret the city’s development during the middle years of the Middle Ages and to analyse what happened during the transition that led to the modern city. The second part will deal with the material traces, i.e. the archaeological sources that allowed us to recover pieces of history and build the overall picture. Excavation data will provide information about the buildings, roads, workshops and craft laboratories, waste disposal and water supply systems, and on the wealth and social status of the city’s inhabitants

Think big about data: Archaeology and the Big Data challenge

Usually defined as high volume, high velocity, and/or high variety data, Big Data permit us to learn things that we could not comprehend using smaller amounts of data, thanks to the empowerment provided by software, hardware and algorithms. This requires a novel archaeological approach: to use a lot of data; to accept messiness; to move from causation to correlation. Do the imperfections of archaeological data preclude this approach? Or are archaeological data perfect because they are messy and difficult to structure? Normally archaeology deals with the complexity of large datasets, fragmentary data, data from a variety of sources and disciplines, rarely in the same format or scale. If so, is archaeology ready to work more with data-driven research, to accept predictive and probabilistic techniques? Big Data inform, rather than explain, they expose patterns for archaeological interpretation, they are a resource and a tool: data mining, text mining, data visualisations, quantitative methods, image processing etc. can help us to understand complex archaeological information. Nonetheless, however seductive Big Data appear, we cannot ignore the problems, such as the risk of considering that data = truth, and intellectual property and ethical issues. Rather, we must adopt this technology with an appreciation of its power but also of its limitations

Mappa. Pisa medievale

Questo volume rappresenta la terza edizione di un ciclo di lavoro iniziato nel 2006 per la tesi di dottorato. Questa, discussa nel 2010 (prima edizione, GATTIGLIA 2010), è stata parzialmente pubblicata, come monografia sintetica nel 2011 (seconda edizione, GATTIGLIA 2011) o in articoli (GATTIGLIA 2012, GATTIGLIA 2012a, GATTIGLIA G. 2011a) e trova ora (terza edizione) una pubblicazione più ampia alla luce di molti dati nuovi. Negli ultimi 2 anni, infatti, il lavoro di studio su Pisa, non solo medievale, è proseguito all’interno del progetto MAPPA (Metodologie Applicate alla Predittività del Potenziale) , che ha reso possibile una capillare raccolta dati attraverso i quali si è potuto ricomporre più compiutamente il quadro idrogeologico, geomorfologico e topografico e sottoporre a verifica, in molti casi modificando, parte delle ipotesi fatte in quelle sedi. I dati qui analizzati sono open data sul MOD (ANICHINI et alii 2013) (l’archivio open data dell’archeologia italiana www.mappaproject.org/mod) o interrogabili su webGIS (MAPPAgis www.mappaproject.org/webgis). Nel primo capitolo viene sinteticamente raccontata la storia dell’archeologia urbana a Pisa. Nel secondo capitolo lo sguardo si allarga sul contesto territoriale e sul paesaggio. I percorsi fluviali e le aree palustri vengono analizzati per capire come l’ambiente abbia condizionato nel bene e nel male lo sviluppo della città medievale. Ma siccome l’uomo non è stato passivo di fronte ad esso, lo studio del sistema portuale e della viabilità terrestre permette di comprendere quali soluzioni siano state adottate per sfruttare i vantaggi geografici e generare profitti economici e commerciali.Il terzo ed ultimo capitolo è diviso in due parti. La prima vuole illustrare le grandi trasformazioni urbanistiche nel lungo periodo che va dalla fine della romanità (VI secolo) alla conquista fiorentina (inizio XV secolo). La seconda parte fa parlare le tracce materiali, le fonti archeologiche, quei tasselli della storia che hanno permesso di ricostruire il quadro generale. Saranno soprattutto i dati provenienti dagli scavi a raccontare com’erano gli edifici, le strade, gli opifici e le botteghe artigianali, il sistema di smaltimento dei rifiuti e di approvvigionamento dell’acqua, infine la ricchezza e lo status sociale dei suoi abitanti

NAVIGATING A NEW DIGITAL INTERFACE: USING AUTOMATED IMAGE RECOGNITION TO IDENTIFY POTTERY IN THE ARCHAIDE PROJECT

Archaeological Automatic Interpretation and Documentation of cEramic (ArchAIDE) is a H2020 funded project (2016-2019) developing digital tools to support archaeologists in recognising and classifying pottery. ArchAIDE is not designed to replace the knowledge of pottery specialists, but seeks to complement by speeding time consuming tasks, provide support for non-specialists, help students learn more about pottery recognition, and aid in the collection of metadata needed to describe the pottery. ArchAIDE is developing a tablet-based mobile app which relies upon image recognition and uses deep learning to narrow and suggest possible matches. While ArchAIDE has been careful to design a tool that allows classification decisions to be made by users at key points in the recording workflow, the app uses digital tools and methods for a significant tasks that were previously carried out using analogue methods. This paper will explore how users are engaging differently with the archaeology when using a digital workflow for identifying, classifying and recording pottery, as observed by the ArchAIDE project partners in early testing. This will include issues around using digitised comparative collections rather than paper catalogues, using the app to identify pottery while still in the field-rather than during post-excavation, how users might ‘see’ pottery differently through a digital rather than analogue analysis, and whether pottery identification using a digital interface changes knowledge transmission and learning processes. While the purpose of the ArchAIDE project is to make pottery identification faster and easier, this paper will pause to reflect and critically engage with moving to a digital workflow, and how this may influence how archaeological knowledge is produced and understood

An Open System for Collection and Automatic Recognition of Pottery through Neural Network Algorithms

In the last ten years, artificial intelligence (AI) techniques have been applied in archaeology. The ArchAIDE project realised an AI-based application to recognise archaeological pottery. Pottery is of paramount importance for understanding archaeological contexts. However, recognition of ceramics is still a manual, time-consuming activity, reliant on analogue catalogues. The project developed two complementary machine-learning tools to propose identifications based on images captured on-site, for optimising and economising this process, while retaining key decision points necessary to create trusted results. One method relies on the shape of a potsherd; the other is based on decorative features. For the shape-based recognition, a novel deep-learning architecture was employed, integrating shape information from points along the inner and outer profile of a sherd. The decoration classifier is based on relatively standard architectures used in image recognition. In both cases, training the algorithms meant facing challenges related to real-world archaeological data: the scarcity of labelled data; extreme imbalance between instances of different categories; and the need to take note of minute differentiating features. Finally, the creation of a desktop and mobile application that integrates the AI classifiers provides an easy-to-use interface for pottery classification and storing pottery data

Verso la rivoluzione. Dall'Open Access all'Open Data: la pubblicazione aperta in archeologia

La pubblicazione congiunta di articoli e datasets, che è ormai prassi in molte disciplinescientifiche, fa ancora fatica ad affermarsi in campo umanistico e in archeologia in particolare. L’articolo analizza i principali ostacoli e propone soluzioni basandosi su tre principifondamentali. 1) Il principio di scientificità: i dati sono essenziali per verificare l’attendibilitàdelle conclusioni. 2) Il principio di condivisione: in una disciplina basata su metodi distrut-tivi, la mancanza di condivisione delle informazioni non solo inibisce la ricerca, ma rappre-senta anche una tragica perdita di insostituibile conoscenza culturale e storica. 3) Il principio di riuso: poiché i dati archeologici sono di pubblico interesse, devono essere apertamente riutilizzabil

MAPPA. Metodologie Applicate alla Predittività del Potenziale Archeologico. 2

La carta di potenziale archeologico della città di Pisa. L'algoritmo elaborato ad hoc per il calcolo del potenziale archeologico. Il MOD: il primo archivio italiano open data di documenti archeologici

Developing the ArchAIDE Application: A digital workflow for identifying, organising and sharing archaeological pottery using automated image recognition

Pottery is of fundamental importance for understanding archaeological contexts, facilitating the understanding of production, trade flows, and social interactions. Pottery characterisation and the classification of ceramics is still a manual process, reliant on analogue catalogues created by specialists, held in archives and libraries. The ArchAIDE project worked to streamline, optimise and economise the mundane aspects of these processes, using the latest automatic image recognition technology, while retaining key decision points necessary to create trusted results. Specifically, ArchAIDE worked to support classification and interpretation work (during both fieldwork and post-excavation analysis) with an innovative app for tablets and smartphones. This article summarises the work of this three-year project, funded by the European Union's Horizon 2020 Research and Innovation Programme under grant agreement N.693548, with a consortium of partners representing both the academic and industry-led ICT (Information and Communications Technology) domains, and the academic and development-led archaeology domains. The collaborative work of the archaeological and technical partners created a pipeline where potsherds are photographed, their characteristics compared against a trained neural network, and the results returned with suggested matches from a comparative collection with typical pottery types and characteristics. Once the correct type is identified, all relevant information for that type is linked to the new sherd and stored within a database that can be shared online. ArchAIDE integrated a variety of novel and best-practice approaches, both in the creation of the app, and the communication of the project to a range of stakeholders

From digitization to datafication. A new challenge is approaching archaeology.

Digitisation has changed archaeology deeply. In the last years, the continuous development of IT technologies brought out a new phenomenon: datafication. Datafication promises to go significantly beyond digitisation, and to have an even more profound impact on archaeology, challenging the foundations of our established methods of measurement and providing new opportunities. Digitisation usually refers to the migration of pieces of information into digital formats, for transmission, re-use and manipulation. Surely, this process has increased exponentially the amount of data that could be processed, but from a more general point of view the act of digitisation, i.e. turning analogue information into computer readable format, by itself does not involve datafication. To datafy means to transform the objects, processes, etc. in a quantified format so they can be tabulated and analysed. We can argue that datafication puts more emphasis on the I (information) of IT, unembedding the knowledge associated with physical objects by decoupling them from the data associated with them. Datafication is manifesting in a variety of forms and can also, but not always, be associated with sensors/actuators and with the Internet of Things. Moreover, a key differentiating aspect between digitisation and datafication is the one related to data analytics: digitisation uses data analytics based on traditional sampling mechanisms, while datafication fits a Big Data approach and relies on the new forms of quantification and associated data mining techniques, that permit more sophisticated mathematical analyses to identify non-linear relationships among data, allowing us to use the information, for instance, for massive predictive analyses. To datafy archaeology would mean to produce a flow of data starting from the data produced by the archaeological practice, for instance, locations, interactions and relations between finds and sites. A flow of data that the archaeological community should have available. Data are a “non-rivalrous” good, in other words, they can be processed again and again and their value does not diminish, on the contrary, it arises from what the data reveal in the aggregate. The ArchAIDE project goes exactly in this direction. ArchAIDE is a H2020 funded project (2016-2019) that will realise an as-automatic-as-possible procedure to transform the paper catalogues in a digital description, to be used as a data pool for search and retrieval process; a tool (mainly designed for mobile devices) that will support archaeologists in recognising and classifying potsherds, through an easy-to-use interface and efficient algorithms for characterization, search and retrieval of the visual/geometrical correspondences; an automatic procedure to derive the potsherd’s description by transforming the data collected into a formatted electronic document; a web-based real-time data visualization to improve access to archaeological heritage and generate new understanding; an open archive to allow the archival and re-use of archaeological data, transforming them into common heritage. This process requires a strong cultural and theoretical framework: from a cultural point of view any researcher must be aware of the opportunity of sharing data for improving their researches, from a theoretical point of view, the archaeological theory should shift towards data-driven research and a Big Data approach

Databases in Archaeology

The storage of archaeological data in, and their retrieval from, relational databases are by now essential components of archaeological research. Given the growing accessibility of statistical techniques and the ease with which they can be applied, databases have greatly increased the possibility for the archaeologists to implement complex quantitative analysis when they examine their datasets. Relational databases mirror how archaeological data are structured and permit better analysis, better publication, and better archiving. Thesauri and the management of the chronology, for instance, are aspects that allow better handling of the archaeological documentation. New and exciting tools such as mobile devices have great potential for entering coded data in large datasets and for datafying the archaeological workflow. In fact, datafication (i.e., the act of transforming objects and processes by giving them a quantified format) promises to go beyond digitization and to have an even more profound impact on archaeology