Showing papers by "Simon Scheider published in 2023"
Proceedings Article•
1 citations
TL;DR: In this paper , the authors highlight some of these changes and identify current topics and challenges in geo-AI, which is the use of Artificial Intelligence methods and techniques in solving geo-spatial problems.
Abstract: Abstract Taken literally, geoAI is the use of Artificial Intelligence methods and techniques in solving geo-spatial problems. Similar to AI more generally, geoAI has seen an influx of new (big) data sources and advanced machine learning techniques, but also a shift in the kind of problems under investigation. In this article, we highlight some of these changes and identify current topics and challenges in geoAI.
1 citations
TL;DR: The authors showed that using ChatGPT can be fraudulent because it threatens the validity of assessments, and effective control in assessments and supervision is required to ensure that lower-level learning goals are substitutable by AI, and supervision and assessments can be refocused on higher-level goals.
Abstract: The recent success of large language models and AI chatbots such as ChatGPT in various knowledge domains has a severe impact on teaching and learning Geography and GIScience. The underlying revolution is often compared to the introduction of pocket calculators, suggesting analogous adaptations that prioritize higher-level skills over other learning content. However, using ChatGPT can be fraudulent because it threatens the validity of assessments. The success of such a strategy therefore rests on the assumption that lower-level learning goals are substitutable by AI, and supervision and assessments can be refocused on higher-level goals. Based on a preliminary survey on ChatGPT's quality in answering questions in Geography and GIScience, we demonstrate that this assumption might be fairly naive, and effective control in assessments and supervision is required.
TL;DR: In this article , the authors compare five manually constructed geographical question corpora, GeoAnQu, Giki, GeoCLEF, GeoQuestions201, and Geoquery, by applying a conceptual transformation parser.
Abstract: Abstract. There is an increasing trend of applying AIbased automated methods to geoscience problems. An important example is a geographic question answering (geoQA) focused on answer generation via GIS workflows rather than retrieval of a factual answer. However, a representative question corpus is necessary for developing, testing, and validating such generative geoQA systems. We compare five manually constructed geographical question corpora, GeoAnQu, Giki, GeoCLEF, GeoQuestions201, and Geoquery, by applying a conceptual transformation parser. The parser infers geo-analytical concepts and their transformations from a geographical question, akin to an abstract GIS workflow. Transformations thus represent the complexity of geo-analytical operations necessary to answer a question. By estimating the variety of concepts and the number of transformations for each corpus, the five corpora can be compared on the level of geo-analytical complexity, which cannot be done with purely NLP-based methods. Results indicate that the questions in GeoAnQu, which were compiled from GIS literature, require a higher number as well as more diverse geo-analytical operations than questions from the four other corpora. Furthermore, constructing a corpus with a sufficient representation (including GIS) may require an approach targeting a uniquely qualified group of users as a source. In contrast, sampling questions from large-scale online repositories like Google, Microsoft, and Yahoo may not provide the quality necessary for testing generative geoQA systems.
TL;DR: In this paper , a taxonomy of personal data marketplaces is presented, along with taxonomy-based design elements distinguishing them both from conventional data markets and among each other, and archetypes are derived.
Abstract: Abstract Since the emerging information economy relies heavily on data for advancement and growth, data markets have gained increasing attention. However, while global data economies are evolving and data are increasingly shared among organizations in various data ecosystems, marketplaces for personal data (PDMs) exhibited considerable start-up difficulties, which doomed their majority to either fail quickly or to operate in legal grey zones. Apparently, in recent times, novel PDMs have arisen which seem economically and technically viable. The study investigates this “new generation” from both an economic and a technological perspective. Adhering to a rigorous methodology for taxonomy building and evaluation, 18 dimensions and 59 characteristics are presented alongside which these new PDMs can be designed. Additionally, archetypes are derived. The findings reveal that PDMs tend to follow certain design commonalities holding for data markets generally but comprise specific design elements distinguishing them both from conventional data markets and among each other.
TL;DR: In this article , a design science research (DSR) approach is proposed to address the European data protection law that merely ascribes human data subjects a need for data privacy while widely neglecting their economic participatory claims to data.
Abstract: Abstract Since the European information economy faces insufficient access to and joint utilization of data, data ecosystems increasingly emerge as economical solutions in B2B environments. Contrarily, in B2C ambits, concepts for sharing and monetizing personal data have not yet prevailed, impeding growth and innovation. Their major pitfall is European data protection law that merely ascribes human data subjects a need for data privacy while widely neglecting their economic participatory claims to data. The study reports on a design science research (DSR) approach addressing this gap and proposes an abstract reference system architecture for an ecosystem centered on humans with personal data. In this DSR approach, multiple methods are embedded to iteratively build and evaluate the artifact, i.e., structured literature reviews, design recovery, prototyping, and expert interviews. Managerial contributions embody novel design knowledge about the conceptual development of human-centric B2C data ecosystems, considering their legal, ethical, economic, and technical constraints.
TL;DR: In this article , the authors argue that structuralism is inherently blind for purposes of any spatial representation, and therefore fails to account for the intelligence required to deal with geographic information, and that a pragmatic turn in GeoAI is required to overcome this problem.
Abstract: Abstract Current artificial intelligence (AI) approaches to handle geographic information (GI) reveal a fatal blindness for the information practices of exactly those sciences whose methodological agendas are taken over with earth-shattering speed. At the same time, there is an apparent inability to remove the human from the loop, despite repeated efforts. Even though there is no question that deep learning has a large potential, for example, for automating classification methods in remote sensing or geocoding of text, current approaches to GeoAI frequently fail to deal with the pragmatic basis of spatial information, including the various practices of data generation, conceptualization and use according to some purpose. We argue that this failure is a direct consequence of a predominance of structuralist ideas about information. Structuralism is inherently blind for purposes of any spatial representation, and therefore fails to account for the intelligence required to deal with geographic information. A pragmatic turn in GeoAI is required to overcome this problem.
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TL;DR: In this article , the authors present a survey of the state-of-the-art techniques in the area of online learning. But they do not discuss how to apply them in the real world.
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Proceedings Article•