Digitally-Mediated Practices of Geospatial Archaeological Data: Transformation, Integration, & Interpretation

Digitally-mediated practices of archaeological data require reflexive thinking about where archaeology stands as a discipline in regard to the ‘digital,’ and where we want to go. To move toward this goal, we advocate a historical approach that emphasizes contextual source-side criticism and data intimacy—scrutinizing maps and 3D data as we do artifacts by analyzing position, form, material and context of analog and digital sources. Applying this approach, we reflect on what we have learned from processes of digitally-mediated data. We ask: What can we learn as we convert analog data to digital data? And, how does digital data transformation impact the chain of archaeological practice? Primary, or raw data, are produced using various technologies ranging from Global Navigation Satellite System (GNSS)/Global Positioning System (GPS), LiDAR, digital photography, and ground penetrating radar, to digitization, typically using a flat-bed scanner to transform analog data such as old field notes, photographs, or drawings into digital data. However, archaeologists not only collect primary data, we also make substantial time investments to create derived data such as maps, 3D models, or statistics via post-processing and analysis. While analog data is typically static, digital data is more dynamic, creating fundamental differences in digitally-mediated archaeological practice. To address some issues embedded in this process, we describe the lessons we have learned from translating analog to digital geospatial data—discussing what is lost and what is gained in translation, and then applying what we have learned to provide concrete insights to archaeological practice

Culture boundaries in semantic web

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.Culture, being created by any and every of us, is the expression form of the society. We easily manipulate this term in everyday life, but defining the culture brings a lot of discussions in between scientists. The most common approach of understanding culture is from anthropologists (Harris & Johnson, 2006; Tylor, 1871) who associate culture with the common developed complex pattern of the society life expressed through knowledge, believes, art, morality, laws, traditions and other features. Approaching extinct cultures all this can be found and interpreted just from archaeological artefacts. Despite many culture definitions, the spatio-temporal aspect of culture is brought mostly by archaeologists. All in all the culture and cultural area understandings remain very fuzzy, though culture area is always formalized as a crispy one. Due to such fuzziness, author would guess, there was no hurry for cultural area or boundary digitalization as it happened with other cultural data in Europe within last decades. The cultural boundary question stayed 'taboo' in semantic web also, that is recently developing for cultural data in order to help to represent the meaning in a restricted sense. It is therefore in this thesis the culture boundary representation in semantic web is analyzed

A Review of Documentation: A Cross-Disciplinary Perspective

Documents are tools of communication which are changing rapidly in nature and quantity. Prompted by the COVID-19 pandemic, digital formats have become ubiquitous. However, documents and documentation have a long pre-digital history. In seeking to survey document types and features, two major online journal databases from the Web of Science database were analysed over a 30-year period to 2020. Documents were classified into types and the (arbitrary) features of format, dimension, production, administration and distribution. Such tabulation of journal documents has not been undertaken previously. As the sampled journals covered a range of fields, the types and features of documentation in selected specialised areas were included. Digitalisation of documentation, especially of rare documents, has accelerated in recent times, contributing to the retention of knowledge and its rapid dissemination, despite the accompanying disadvantages of the digital age, with its largely unregulated social media. Classifying and describing the diversity of existing documents is a major task and we have initiated this process by analysing two scientific databases

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

Satellite-based Machine Learning modelling of Ecosystem Services indicators: A review and meta-analysis

Satellite-based Machine Learning (ML) modelling has emerged as a powerful tool to understand and quantify spatial relationships between landscape dynamics, biophysical variables and natural stocks. Ecosystem Services indicators (ESi) provide qualitative and quantitative information aiding the assessment of ecosystems’ status. Through a systematic meta-analysis following the PRISMA guidelines, studies from one decade (2012–2022) were analyzed and synthesized. The results indicated that Random Forest emerged as the most frequently utilized ML algorithm, while Landsat missions stood out as the primary source of Satellite Earth Observation (SEO) data. Nonetheless, authors favoured Sentinel-2 due to its superior spatial, spectral, and temporal resolution. While 30% of the examined studies focused on modelling proxies of climate regulation services, assessments of natural stocks such as biomass, water, food production, and raw materials were also frequently applied. Meta-analysis illustrated the utilization of classification and regression tasks in estimating measurements of ecosystems' extent and conditions and findings underscored the connections between established methods and their replication. This study offers current perspectives on existing satellite-based approaches, contributing to the ongoing efforts to employ ML and artificial intelligence for unveiling the potential of SEO data and technologies in modelling ESi.info:eu-repo/semantics/publishedVersio

Proceedings of the 3rd Open Source Geospatial Research & Education Symposium OGRS 2014

The third Open Source Geospatial Research & Education Symposium (OGRS) was held in Helsinki, Finland, on 10 to 13 June 2014. The symposium was hosted and organized by the Department of Civil and Environmental Engineering, Aalto University School of Engineering, in partnership with the OGRS Community, on the Espoo campus of Aalto University. These proceedings contain the 20 papers presented at the symposium. OGRS is a meeting dedicated to exchanging ideas in and results from the development and use of open source geospatial software in both research and education.  The symposium offers several opportunities for discussing, learning, and presenting results, principles, methods and practices while supporting a primary theme: how to carry out research and educate academic students using, contributing to, and launching open source geospatial initiatives. Participating in open source initiatives can potentially boost innovation as a value creating process requiring joint collaborations between academia, foundations, associations, developer communities and industry. Additionally, open source software can improve the efficiency and impact of university education by introducing open and freely usable tools and research results to students, and encouraging them to get involved in projects. This may eventually lead to new community projects and businesses. The symposium contributes to the validation of the open source model in research and education in geoinformatics

Integrated groundwater management: An overview of concepts and challenges

Managing water is a grand challenge problem and has become one of humanity’s foremost priorities. Surface water resources are typically societally managed and relatively well understood; groundwater resources, however, are often hidden and more difficult to conceptualize. Replenishment rates of groundwater cannot match past and current rates of depletion in many parts of the world. In addition, declining quality of the remaining groundwater commonly cannot support all agricultural, industrial and urban demands and ecosystem functioning, especially in the developed world. In the developing world, it can fail to even meet essential human needs. The issue is: how do we manage this crucial resource in an acceptable way, one that considers the sustainability of the resource for future generations and the socioeconomic and environmental impacts? In many cases this means restoring aquifers of concern to some sustainable equilibrium over a negotiated period of time, and seeking opportunities for better managing groundwater conjunctively with surface water and other resource uses. However, there are many, often-interrelated, dimensions to managing groundwater effectively. Effective groundwater management is underpinned by sound science (biophysical and social) that actively engages the wider community and relevant stakeholders in the decision making process. Generally, an integrated approach will mean “thinking beyond the aquifer”, a view which considers the wider context of surface water links, catchment management and cross-sectoral issues with economics, energy, climate, agriculture and the environment. The aim of the book is to document for the first time the dimensions and requirements of sound integrated groundwater management (IGM). The primary focus is on groundwater management within its system, but integrates linkages beyond the aquifer. The book provides an encompassing synthesis for researchers, practitioners and water resource managers on the concepts and tools required for defensible IGM, including how IGM can be applied to achieve more sustainable socioeconomic and environmental outcomes, and key challenges of IGM. The book is divided into five parts: integration overview and problem settings; governance; socioeconomics; biophysical aspects; and modelling and decision support. However, IGM is integrated by definition, thus these divisions should be considered a convenience for presenting the topics rather than hard and fast demarcations of the topic area

Development of a conceptual framework for integrated analysis and assessment of agricultural systems in SEAMLESS-IF

Production Economics,

Hybrid human-machine information systems for data classification

Over the last decade, we have seen an intense development of machine learning approaches for solving various tasks in diverse domains. Despite the remarkable advancements in this field, there are still task categories that machine learning models fall short of the required accuracy. This is the case with tasks that require human cognitive skills, such as sentiment analysis, emotional or contextual understanding. On the other hand, human-based computation approaches, such as crowdsourcing, are popular for solving such tasks. Crowdsourcing enables access to a vast number of groups with different expertise, and if managed properly, generates high-quality results. However, crowdsourcing as a standalone approach is not scalable due to the latency and cost it brings in. Addressing the challenges and limitations that the human and machine-based approaches have distinctly requires bridging the two fields into a hybrid intelligence, seen as a promising approach to solve critical and complex real-world tasks. This thesis focuses on hybrid human-machine information systems, combining machine and human intelligence and leveraging their complementary strengths: the data processing efficiency of machine learning and the data quality generated by crowdsourcing. In this thesis, we present hybrid human-machine models to address the challenges falling into three dimensions: accuracy, latency, and cost. Solving data classification tasks in different domains has different requirements concerning accuracy, latency, and cost criteria. Motivated by this fact, we introduce a master component that evaluates these criteria to find the suitable model as a trade-off solution. In hybrid human-machine information systems, incorporating human judgments is expected to improve the accuracy of the system. Therefore, to ensure this, we focus on the human intelligence component, integrating profile-aware crowdsourcing for task assignment and data quality control mechanisms in the hybrid pipelines. The proposed conceptual hybrid human-machine models materialize in conducted experiments. Motivated by challenging scenarios and using real-world datasets, we implement the hybrid models in three experiments. Evaluations show that the implemented hybrid human-machine architectures for data classification tasks lead to better results as compared to each of the two approaches individually, improving the overall accuracy at an acceptable cost and latency