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Damian Borth
Title
Prof. Dr.
Last Name
Borth
First name
Damian
Email
damian.borth@unisg.ch
Phone
+41 71 224 26 27
Twitter
https://twitter.com/damianborth
Google Scholar
Now showing
1 - 10 of 49
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PublicationToward 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: 4705914
Scopus© Citations 10 -
PublicationType: journal articleJournal: Expert FocusVolume: Special: Internal AuditIssue: 01
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PublicationType: journal articleJournal: Expert FocusVolume: Special: Interne RevisionIssue: 01
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PublicationType: journal articleJournal: Expert FocusIssue: 02
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PublicationType: journal articleJournal: Expert FocusVolume: 2020Issue: 09
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PublicationType: journal articleJournal: HMD Praxis der WirtschaftsinformatikVolume: 57Issue: 1
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PublicationEuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover ClassificationIn 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.Type: journal articleJournal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingVolume: 12Issue: 7
Scopus© Citations 605 -
PublicationAudioPairBank: towards a Large-Scale Tag-Pair-based Audio Content Analysis(Springer, 2018-09-15)
;Sebastian, Säger ;Benjamin, Elizalde ;Christan, Schluze ;Bhiksha, RajIan, LaneRecently, sound recognition has been used to identify sounds, such as the sound of a car, or a river. However, sounds have nuances that may be better described by adjective-noun pairs such as “slow car” and verb-noun pairs such as “flying insects,” which are underexplored. Therefore, this work investigates the relationship between audio content and both adjective-noun pairs and verb-noun pairs. Due to the lack of datasets with these kinds of annotations, we collected and processed the AudioPairBank corpus consisting of a combined total of 1123 pairs and over 33,000 audio files. In this paper, we include previously unavailable documentation of the challenges and implications of collecting audio recordings with these types of labels. We have also shown the degree of correlation between the audio content and the labels through classification experiments, which yielded 70% accuracy. The results and study in this paper encourage further exploration of the nuances in sounds and are meant to complement similar research performed on images and text in multimedia analysis.Type: journal articleJournal: EURASIP Journal on Audio, Speech, and Music ProcessingVolume: 2018Issue: 12Scopus© Citations 1 -
PublicationClass-Incremental Learning with Repetition( 2023)
;Andrea Cossu ;Antonio Carta ;Julio Hurtado ;Lorenzo Pellegrini ;Davide Bacciu ;Vincenzo Lomonaco ;Sarath Chandar ;Razvan Pascanu ;Hanie SedghiDoina PrecupReal-world data streams naturally include the repetition of previous concepts. From a Continual Learning (CL) perspective, repetition is a property of the environment and, unlike replay, cannot be controlled by the agent. Nowadays, the Class-Incremental (CI) scenario represents the leading test-bed for assessing and comparing CL strategies. This scenario type is very easy to use, but it never allows revisiting previously seen classes, thus completely neglecting the role of repetition. We focus on the family of Class-Incremental with Repetition (CIR) scenario, where repetition is embedded in the definition of the stream. We propose two stochastic stream generators that produce a wide range of CIR streams starting from a single dataset and a few interpretable control parameters. We conduct the first comprehensive evaluation of repetition in CL by studying the behavior of existing CL strategies under different CIR streams. We then present a novel replay strategy that exploits repetition and counteracts the natural imbalance present in the stream. On both CIFAR100 and TinyImageNet, our strategy outperforms other replay approaches, which are not designed for environments with repetition.Type: conference paper