Damian Borth
Title
Prof. Dr.
Last Name
Borth
First name
Damian
Email
damian.borth@unisg.ch
ORCID
Phone
+41 71 224 26 27
Twitter
https://twitter.com/damianborth
Google Scholar
71 results
Now showing 1 - 10 of 71
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models(2026-01-15); ; Remote sensing data is commonly used in a wide range of tasks, such as natural disaster monitoring and land-use studies. For each task, scientists carefully choose appropriate modalities or leverage data from purpose-built instruments. Recent work on remote sensing foundation models pre-trains computer vision models on large amounts of remote sensing data to learn general-purpose representations. However, this progress comes at the cost of very large models, which are particularly challenging to deploy on edge devices due to their high inference costs. Moreover, downstream applications often rely on only a subset of modalities and operate under strict resource constraints, creating a mismatch between the large, multi-purpose models produced by pre-training and the lightweight, task-specific models needed in practice. We address this mismatch with MAPEX, a remote sensing foundation model based on mixture-of-modality experts. MAPEX is pre-trained on multi-modal remote sensing data using a novel modality-conditioned token routing mechanism that naturally encourages the emergence of modality-specialized experts. To apply the model on a specific task, we propose a modality-aware pruning technique that retains only the experts relevant to the task’s modalities, resulting in lightweight, task-specific models that can be directly extracted from the pre-trained foundation model, at no additional cost. Our approach yields efficient modality-specific models while simplifying fine-tuning and deployment for the modalities of interest. We experimentally validate MAPEX on diverse remote sensing datasets and show strong performance compared to fully supervised training and state-of-the-art remote sensing foundation models. Code will be available at https://github.com/HSG-AIML/MAPEX}{github.com/HSG-AIML/MAPEX.Type:journal articleVolume:64 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Model Zoo on Phase Transitions in Neural Networks(2025-09-29); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A SUM GREATER THAN ITS PARTS: COLLECTIVE ARTIFICIAL INTELLIGENCE IN AUDITING - Advancing Audit Models through Federated Learning Without Sharing Proprietary Data(ExpertSuisse, 2024-04-10); ; ; Miklos A. VasarhelyiArtificial 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.Type:journal articleJournal:EXPERT FOCUS - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Physics-Guided Multitask Learning for Estimating Power Generation and CO 2 Emissions From Satellite Imagery(2023-05); ; ; IEEE Transactions on Geoscience and Remote Sensing (TGRS)Fossil fuel combustion produces large quantities of carbon dioxide (CO2), a major greenhouse gas (GHG), which is one of the main drivers of climate change. A quantitative assessment of GHG emissions is fundamental to predicting climate change effects, enforcing emission regulations, and monitoring pollution trading schemes. Unfortunately, the reporting of GHG emissions is only required in some countries, resulting in insufficient global coverage. At the same time, the transition from fossil fuels to zero carbon to limit climate change is at the heart of several ecological movements, hence the need for quantifying energy production, as well. In this work, we propose an end-to-end method to estimate power generation rates for fossil fuel power plants from satellite images, based on which we approximate GHG (CO2) emission rates. We present a physics-guided multitask deep-learning approach able to simultaneously predict from a single-satellite image of a power plant: 1) the pixel-area covered by plumes; 2) the type of fired fuel; and 3) the power generation rate. To ensure physically realistic predictions from our model we account for environmental conditions and empirical physical constraints. We then convert the predicted power generation rate into estimates for the rate at which CO2 is being emitted, using a fuel-dependent conversion factor. Experimental results show that our multitask learning approach improves the power generation estimation mean absolute error (MAE) by 23% compared to a single-task network trained on the same dataset.Type:journal article - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Toward Global Estimation of Ground-Level NO2 Pollution With Deep Learning and Remote SensingAir 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.Type:journal articleJournal:IEEE Transactions on Geoscience and Remote SensingVolume:60Issue:4705914Scopus© Citations 34 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Artificial Intelligence in Internal Audit as a Contribution to Effective Governance - Deep-learning enabled Detection of Anomalies in Financial Accounting DataType:journal articleJournal:Expert FocusVolume:Special: Internal AuditIssue:01 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Künstliche Intelligenz im Internal Audit als Beitrag zur Effektiven Governance - Deep-Learning basierte Detektion von Buchungsanomalien in der RevisionspraxisType:journal articleJournal:Expert FocusVolume:Special: Interne RevisionIssue:01 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Artificial Intelligence Enabled Audit Sampling - Learning to draw representative and interpretable audit samples from large-scale journal entry data(EXPERTsuisse, 2022-03-07); ; ; Type:journal articleJournal:Expert FocusIssue:04 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Stichprobenauswahl durch die Anwendung von Künstlicher Intelligenz - Lernen repräsentativer Stichproben aus Journalbuchungen in der Prüfungspraxis(EXPERTsuisse, 2022-02-07); ; ; Type:journal articleJournal:Expert FocusIssue:02 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Deep Learning für die Wirtschaftsprüfung - Eine Darstellung von Theorie, Funktionsweise und Anwendungsmöglichkeiten(C.H. Beck Vahlen Verlag, 2021-07-28); ; ; Type:journal articleJournal:Zeitschrift für Internationale Rechnungslegung (IRZ)Issue:7/8