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Künstliche Intelligenz 37(1) - März 2023

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  • Zeitschriftenartikel
    GeoAI as Collaborative Effort
    (KI - Künstliche Intelligenz: Vol. 37, No. 1, 2023) Richter, Kai-Florian; Scheider, Simon; Tuia, Devis
  • Zeitschriftenartikel
    Automated Interpretation of Place Descriptions: Determining Entity Types for Querying OSM
    (KI - Künstliche Intelligenz: Vol. 37, No. 1, 2023) Yousaf, Madiha; Schwartz, Tobias; Wolter, Diedrich
    This paper is concerned with interpretation of natural language place descriptions, as they are a rich source of geographic information. A place description is interpreted by matching geographic entities occurring in the text against the OpenStreetMap (OSM) database. This paper is mainly concerned with interpretation of paraphrased places, i.e., entities for which no name is given and which may only by described. Our objective is to determine suitable entity types that allow querying the OpenStreetMap database for the respective place. For example, if we wish to identify a place to eat , we have to check for entities of an a-priori unknown type (cafe, restaurant, etc.). Challenges arise from the open-endedness of language, its ambiguity, and context-sensitivity as well as from mismatches between human conceptualization of place and database ontologies. The contributions of this paper are, first, to present a hard problem that is key to geo-information retrieval beyond named entities. Second, we propose context-sensitive methods for identifying place types based on semantic word similarity. We evaluate the methods on text extracted from Wikipedia and travel blogs, revealing their contribution to advancing automated interpretation of place descriptions to paraphrased places.
  • Zeitschriftenartikel
    Identifying Landscape Relevant Natural Language using Actively Crowdsourced Landscape Descriptions and Sentence-Transformers
    (KI - Künstliche Intelligenz: Vol. 37, No. 1, 2023) Baer, Manuel F.; Purves, Ross S.
    Natural language has proven to be a valuable source of data for various scientific inquiries including landscape perception and preference research. However, large high quality landscape relevant corpora are scare. We here propose and discuss a natural language processing workflow to identify landscape relevant documents in large collections of unstructured text. Using a small curated high quality collection of actively crowdsourced landscape descriptions we identify and extract similar documents from two different corpora ( Geograph and WikiHow ) using sentence-transformers and cosine similarity scores. We show that 1) sentence-transformers combined with cosine similarity calculations successfully identify similar documents in both Geograph and WikiHow effectively opening the door to the creation of new landscape specific corpora, 2) the proposed sentence-transformer approach outperforms traditional Term Frequency - Inverse Document Frequency based approaches and 3) the identified documents capture similar topics when compared to the original high quality collection. The presented workflow is transferable to various scientific disciplines in need of domain specific natural language corpora as underlying data.
  • Zeitschriftenartikel
    Remember to Correct the Bias When Using Deep Learning for Regression!
    (KI - Künstliche Intelligenz: Vol. 37, No. 1, 2023) Igel, Christian; Oehmcke, Stefan
    When training deep learning models for least-squares regression, we cannot expect that the training error residuals of the final model, selected after a fixed training time or based on performance on a hold-out data set, sum to zero. This can introduce a systematic error that accumulates if we are interested in the total aggregated performance over many data points (e.g., the sum of the residuals on previously unseen data). We suggest adjusting the bias of the machine learning model after training as a default post-processing step, which efficiently solves the problem. The severeness of the error accumulation and the effectiveness of the bias correction are demonstrated in exemplary experiments.
  • Zeitschriftenartikel
    (KI - Künstliche Intelligenz: Vol. 37, No. 1, 2023) Scheider, Simon; Richter, Kai-Florian
  • Zeitschriftenartikel
    In Search of Basement Indicators from Street View Imagery Data: An Investigation of Data Sources and Analysis Strategies
    (KI - Künstliche Intelligenz: Vol. 37, No. 1, 2023) Vo, Anh Vu; Bertolotto, Michela; Ofterdinger, Ulrich; Laefer, Debra F.
    Street view imagery databases such as Google Street View, Mapillary, and Karta View provide great spatial and temporal coverage for many cities globally. Those data, when coupled with appropriate computer vision algorithms, can provide an effective means to analyse aspects of the urban environment at scale. As an effort to enhance current practices in urban flood risk assessment, this project investigates a potential use of street view imagery data to identify building features that indicate buildings’ vulnerability to flooding (e.g., basements and semi-basements). In particular, this paper discusses (1) building features indicating the presence of basement structures, (2) available imagery data sources capturing those features, and (3) computer vision algorithms capable of automatically detecting the features of interest. The paper also reviews existing methods for reconstructing geometry representations of the extracted features from images and potential approaches to account for data quality issues. Preliminary experiments were conducted, which confirmed the usability of the freely available Mapillary images for detecting basement railings as an example type of basement features, as well as geolocating the features.
  • Zeitschriftenartikel
    URWalking: Indoor Navigation for Research and Daily Use
    (KI - Künstliche Intelligenz: Vol. 37, No. 1, 2023) Ludwig, Bernd; Donabauer, Gregor; Ramsauer, Dominik; Subari, Karema al
    In this report, we present the project URWalking conducted at the University of Regensburg. We describe its major outcomes: Firstly, an indoor navigation system for pedestrians as a web application and as an Android app with position tracking of users in indoor and outdoor environments. Our implementation showcases that a variant of the $$A^*$$ A ∗ -algorithm by Ullmann (tengetriebene optimierung präferenzadaptiver fußwegrouten durch gebäudekomplexe https://epub.uni-regensburg.de/43697/ , 2020) can handle the routing problem in large, levelled indoor environments efficiently. Secondly, the apps have been used in several studies for a deeper understanding of human wayfinding. We collected eye tracking and synchronized video data, think aloud protocols, and log data of users interacting with the apps. We applied state-of-the-art deep learning models for gaze tracking and automatic classification of landmarks. Our results indicate that even the most recent version of the YOLO image classifier by Redmon and Farhadi (olov3: An incremental improvement. arXiv, 2018) needs finetuning to recognize everyday objects in indoor environments. Furthermore, we provide empirical evidence that appropriate machine learning models are helpful to bridge behavioural data from users during wayfinding and conceptual models for the salience of objects and landmarks. However, simplistic models are insufficient to reasonably explain wayfinding behaviour in real time—an open issue in GeoAI. We conclude that the GeoAI community should collect more naturalistic log data of wayfinding activities in order to build efficient machine learning models capable of predicting user reactions to routing instructions and of explaining how humans integrate stimuli from the environment as essential information into routing instructions while solving wayfinding tasks. Such models form the basis for real-time wayfinding assistance.
  • Zeitschriftenartikel
    GeoAI and Beyond
    (KI - Künstliche Intelligenz: Vol. 37, No. 1, 2023) Scheider, Simon; Richter, Kai-Florian; Janowicz, Krzysztof
  • Zeitschriftenartikel
    Current topics and challenges in geoAI
    (KI - Künstliche Intelligenz: Vol. 37, No. 1, 2023) Richter, Kai-Florian; Scheider, Simon
    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.
  • Zeitschriftenartikel
    (KI - Künstliche Intelligenz: Vol. 37, No. 1, 2023) null