Damian Borth
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Prof. Dr.
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Borth
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Damian
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damian.borth@unisg.ch
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+41 71 224 26 27
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https://twitter.com/damianborth
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A SUM GREATER THAN ITS PARTS: COLLECTIVE ARTIFICIAL INTELLIGENCE IN AUDITING - Advancing Audit Models through Federated Learning Without Sharing Proprietary Data

2024-04-10 , Marco Schreyer , Damian Borth , Tore Flemming Ruud , Miklos A. Vasarhelyi

Artificial intelligence exhibits the potential to transform auditing by extracting insights from large volumes of audit-relevant data. This article introduces federated learning, an emerging artificial intelligence learning setting. It outlines the integration of federated learning into practical audit procedures to gather collective intelligence from various audit-relevant data sources while ensuring data privacy.

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Künstliche Intelligenz im Internal Audit als Beitrag zur Effektiven Governance - Deep-Learning basierte Detektion von Buchungsanomalien in der Revisionspraxis

2022-01-07 , Schreyer, Marco , Baumgartner, Marcel , Ruud, Flemming , Borth, Damian

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Deep Learning für die Wirtschaftsprüfung - Eine Darstellung von Theorie, Funktionsweise und Anwendungsmöglichkeiten

2021-07-28 , Gierbl, Anita Stefanie , Schreyer, Marco , Borth, Damian , Leibfried, Peter

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EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification

2019-07 , Patrick, Helber , Benjamin, Bischke , Andreas, Dengel , Damian, Borth

In this paper, we present a patch-based land use and land cover classification approach using Sentinel-2 satellite images. The Sentinel-2 satellite images are openly and freely accessible, and are provided in the earth observation program Copernicus. We present a novel dataset, based on these images that covers 13 spectral bands and is comprised of ten classes with a total of 27000 labeled and geo-referenced images. Benchmarks are provided for this novel dataset with its spectral bands using state-of-the-art deep convolutional neural networks. An overall classification accuracy of 98.57% was achieved with the proposed novel dataset. The resulting classification system opens a gate toward a number of earth observation applications. We demon- strate how this classification system can be used for detecting land use and land cover changes, and how it can assist in improving geographical maps. The geo-referenced dataset EuroSAT is made publicly available at https://github.com/phelber/eurosat.

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Toward Global Estimation of Ground-Level NO2 Pollution With Deep Learning and Remote Sensing

2022-03-21 , Scheibenreif, Linus Mathias , Mommert, Michael , Borth, Damian

Air pollution is a central environmental problem in countries around the world. It contributes to climate change through the emission of greenhouse gases, and adversely impacts the health of billions of people. Despite its importance, detailed information about the spatial and temporal distribution of pollutants is complex to obtain. Ground-level monitoring stations are sparse, and approaches for modeling air pollution rely on extensive datasets which are unavailable for many locations. We introduce three techniques for the estimation of air pollution to overcome these limitations: 1) a baseline localized approach that mimics conventional land-use regression through gradient boosting; 2) an OpenStreetMap (OSM) approach with gradient boosting that is applicable beyond regions covered by detailed geographic datasets; and 3) a remote sensing-based deep learning method utilizing multiband imagery and trace-gas column density measurements from satellites. We focus on the estimation of nitrogen dioxide (NO2), a common anthropogenic air pollutant with adverse effects on the environment and human health. Our local baseline model achieves strong results with a mean absolute error (MAE) of 5.18 ± 0.16 μg/m3 NO2. Substituting localized inputs with OSM leads to a degraded performance (MAE 7.22 ± 0.14) but enables NO2 estimation at a global scale. The proposed deep learning model on remote sensing data combines high accuracy (MAE 5.5 ± 0.14) with global coverage and heteroscedastic uncertainty quantification. Our results enable the estimation of surface-level NO2 pollution with high spatial resolution for any location on Earth. We illustrate this capability with an out-of-distribution test set on the US westcoast. Code and data are publicly available.

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Artificial Intelligence Enabled Audit Sampling - Learning to draw representative and interpretable audit samples from large-scale journal entry data

2022-03-07 , Schreyer, Marco , Gierbl, Anita Stefanie , Ruud, Flemming , Borth, Damian

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Künstliche Intelligenz in der Prüfungspraxis - Eine Bestandsaufnahme aktueller Einsatzmöglichkeiten und Herausforderungen

2020-09-01 , Gierbl, Anita Stefanie , Schreyer, Marco , Leibfried, Peter , Borth, Damian

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Artificial Intelligence in Internal Audit as a Contribution to Effective Governance - Deep-learning enabled Detection of Anomalies in Financial Accounting Data

2022-01-07 , Schreyer, Marco , Baumgartner, Marcel , Ruud, Flemming , Borth, Damian

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Stichprobenauswahl durch die Anwendung von Künstlicher Intelligenz - Lernen repräsentativer Stichproben aus Journalbuchungen in der Prüfungspraxis

2022-02-07 , Schreyer, Marco , Gierbl, Anita Stefanie , Ruud, Flemming , Borth, Damian

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Management von Künstlicher Intelligenz in Unternehmen

2020 , van Giffen, Benjamin , Borth, Damian , Brenner, Walter

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