Michael Mommert
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Mommert
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Michael
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michael.mommert@unisg.ch
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+41 71 224 2615
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  • Publication
    Masked Vision Transformers for Hyperspectral Image Classification
    ( 2023-06-18)
    Scheibenreif, Linus
    ;
    Mommert, Michael
    ;
    Borth, Damian
    Transformer architectures have become state-of-the-art models in computer vision and natural language processing. To a significant degree, their success can be attributed to self-supervised pre-training on large scale unlabeled datasets. This work investigates the use of self-supervised masked image reconstruction to advance transformer models for hyperspectral remote sensing imagery. To facilitate self-supervised pre-training, we build a large dataset of unlabeled hyperspectral observations from the EnMAP satellite and systematically investigate modifications of the vision transformer architecture to optimally leverage the characteristics of hyperspectral data. We find significant improvements in accuracy on different land cover classification tasks over both standard vision and sequence transformers using (i) blockwise patch embeddings, (ii) spatialspectral self-attention, (iii) spectral positional embeddings and (iv) masked self-supervised pre-training 1. The resulting model outperforms standard transformer architectures by +5% accuracy on a labeled subset of our EnMAP data and by +15% on Houston2018 hyperspectral dataset, making it competitive with a strong 3D convolutional neural network baseline. In an ablation study on label-efficiency based on the Houston2018 dataset, self-supervised pretraining significantly improves transformer accuracy when little labeled training data is available. The self-supervised model outperforms randomly initialized transformers and the 3D convolutional neural network by +7-8% when only 0.1-10% of the training labels are available.
    Type: conference paper
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/118772
  • Publication
    Traffic Noise Estimation from Satellite Imagery with Deep Learning
    (IEEE Geoscience and Remote Sensing Society, 2022-07-20)
    Eicher, Leonardo
    ;
    Mommert, Michael
    ;
    Borth, Damian
    Road traffic noise represents a global health issue. Despite its importance, noise data are unavailable in many regions of the world. We therefore propose to approximate noise data from satellite imagery in an end-to-end Deep Learning approach. We train a U-Net segmentation model to estimate road noise based on freely available Sentinel-2 satellite imagery and existing road traffic noise estimates for Switzerland. We are able to achieve an RMSE of 8.8 dB(A) for day-time traffic noise and 7.6 dB(A) for nighttime traffic noise with a spatial resolution of 10 m. In addition to identifying major road networks, our model succeeds to predict the spatial propagation of noise. Our results suggest that this approach provides a pathway to estimating road traffic noise for areas for which no such measures are available
    Type: conference paper
    URL: https://igarss2022.org/view_paper.php?PaperNum=3187
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/108479
  • Publication
    Estimation of Power Generation and CO2 Emissions Using Satellite Imagery
    ( 2022-10-13)
    Hanna, Joëlle
    ;
    Mommert, Michael
    ;
    Borth, Damian
    Burning fossil fuels produces large amounts of carbon dioxide (CO2), a major Greenhouse Gas (GHG) and a main driver of Climate Change. Quantification of GHG emissions related to power plants is crucial for accurate predictions of climate effects and for achieving a successful energy transition (from fossil-fuel to carbon-free energy). The reporting of such emissions is only required in some countries, resulting in insufficient global coverage. In this work, we propose an end-to-end method to predict power generation rates for fossil fuel power plants from satellite images based on which we estimate GHG emission rates. We present a multitask deep learning approach able to simultaneously predict: (i) the pixel-area covered by plumes from a single satellite image of a power plant, (ii) the type of fired fuel, and (iii) the power generation rate. To ensure physically realistic predictions from our model we account for environmental conditions. We then convert the predicted power generation rate into estimates for the rate at which CO2 is being emitted, using fuel-dependent conversion factors.
    Type: conference paper
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/108158
  • Publication
    A Multimodal Approach for Event Detection: Study of UK Lockdowns in the Year 2020.
    (IEEE Geoscience and Remote Sensing Society, 2022-07-19)
    Hanna, Joëlle
    ;
    Scheibenreif, Linus Mathias
    ;
    Mommert, Michael
    ;
    Borth, Damian
    Satellites allow spatially precise monitoring of the Earth, but provide only limited information on events of societal impact. Subjective societal impact, however, may be quantified at a high frequency by monitoring social media data. In this work, we propose a multi-modal data fusion framework to accurately identify periods of COVID-19-related lockdown in the United Kingdom using satellite observations (NO2 measurements from Sentinel-5P) and social media (textual content of tweets from Twitter) data. We show that the data fusion of the two modalities improves the event detection accuracy on a national level and for large cities such as London.
    Type: conference paper
    URL: https://igarss2022.org/view_paper.php?PaperNum=3388
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/108481
  • Publication
    Type: conference paper
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/108599
  • Publication
    Contrastive Self-Supervised Data Fusion for Satellite Imagery
    (ISPRS, 2022)
    Scheibenreif, Linus Mathias
    ;
    Mommert, Michael
    ;
    Borth, Damian
    Self-supervised learning has great potential for the remote sensing domain, where unlabelled observations are abundant, but labels are hard to obtain. This work leverages unlabelled multi-modal remote sensing data for augmentation-free contrastive self-supervised learning. Deep neural network models are trained to maximize the similarity of latent representations obtained with different sensing techniques from the same location, while distinguishing them from other locations. We showcase this idea with two self-supervised data fusion methods and compare against standard supervised and self-supervised learning approaches on a land-cover classification task. Our results show that contrastive data fusion is a powerful self-supervised technique to train image encoders that are capable of producing meaningful representations: Simple linear probing performs on par with fully supervised approaches and fine-tuning with as little as 10% of the labelled data results in higher accuracy than supervised training on the entire dataset.
    Type: conference paper
    Journal: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
    Volume: V-3-2022
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/109496
  • Publication
    Commercial Vehicle Traffic Detection from Satellite Imagery with Deep Learning
    (ICML 2021 Workshop on Tackling Climate Change with Machine Learning Workshop, 2021)
    Blattner, Moritz
    ;
    Mommert, Michael
    ;
    Borth, Damian
    Type: conference paper
    URL: https://www.climatechange.ai/papers/icml2021/19
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/110846
  • Publication
    Multitask Learning for Estimating Power Plant Greenhouse Gas Emissions from Satellite Imagery
    (Tackling Climate Change with Machine Learning workshop at NeurIPS., 2021-12-14)
    Hanna, Joëlle
    ;
    Mommert, Michael
    ;
    Scheibenreif, Linus Mathias
    ;
    Borth, Damian
    The burning of fossil fuels produces large amounts of carbon dioxide (CO2), a major Greenhouse Gas (GHG) and a main driver of Climate Change. Quantifying GHG emissions is crucial for accurate predictions of climate effects and to enforce emission trading schemes. The reporting of such emissions is only required in some countries, resulting in insufficient global coverage. In this work, we propose an end-to-end method to predict power generation rates for fossil fuel power plants from satellite images based on which we estimate GHG emission rates. We present a multitask deep learning approach able to simultaneously predict: (i) the pixel-area covered by plumes from a single satellite image of a power plant, (ii) the type of fired fuel, and (iii) the power generation rate. We then convert the predicted power generation rate into estimates for the rate at which CO2 is being emitted. Experimental results show that our model approach allows us to estimate the power generation rate of a power plant to within 139 MW (MAE, for a mean sample power plant capacity of 1177 MW) from a single satellite image and CO2 emission rates to within 311 t/h. This multitask learning approach improves the power generation estimation MAE by 39% compared to a baseline single-task network trained on the same dataset.
    Type: conference paper
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/109629
  • Publication
    Power Plant Classification from Remote Imaging with Deep Learning
    (IEEE, 2021-07)
    Mommert, Michael
    ;
    Scheibenreif, Linus Mathias
    ;
    Hanna, Joëlle
    ;
    Borth, Damian
    Satellite remote imaging enables the detailed study of land use patterns on a global scale. We investigate the possibility to improve the information content of traditional land use classification by identifying the nature of industrial sites from medium-resolution remote sensing images. In this work, we focus on classifying different types of power plants from Sentinel-2 imaging data. Using a ResNet-50 deep learning model, we are able to achieve a mean accuracy of 90.0% in distinguishing 10 different power plant types and a background class. Furthermore, we are able to identify the cooling mechanisms utilized in thermal power plants with a mean accuracy of 87.5%. Our results enable us to qualitatively investigate the energy mix from Sentinel-2 imaging data, and prove the feasibility to classify industrial sites on a global scale from freely available satellite imagery.
    Type: conference paper
    URL: https://ieeexplore.ieee.org/document/9553219
    DOI: 10.1109/IGARSS47720.2021.9553219
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/110263
    Scopus© Citations 2
  • Publication
    A Novel Dataset and Benchmark for Surface NO2 Prediction from Remote Sensing Data Including COVID Lockdown Measures
    (IEEE, 2021-07-16)
    Scheibenreif, Linus Mathias
    ;
    Mommert, Michael
    ;
    Borth, Damian
    NO2 is an atmospheric trace gas that contributes to global warming as a precursor of greenhouse gases and has adverse effects on human health. Surface NO2 concentrations are commonly measured through strictly localized networks of air quality stations on the ground. This work presents a novel dataset of surface NO2 measurements aligned with atmospheric column densities from Sentinel-5P, as well as geographic and meteorological variables and lockdown information. The dataset provides access to data from a variety of sources through a common format and will foster data-driven research into the causes and effects of NO2 pollution. We showcase the value of the new dataset on the task of surface NO2 estimation with gradient boosting. The resulting models enable daily estimates and confident identification of EU NO2 exposure limit breaches. Additionally, we investigate the influence of COVID-19 lockdowns on air quality in Europe and find a significant decrease in NO2 levels.
    Type: conference paper
    URL: https://igarss2021.com/view_paper.php?PaperNum=3638
    DOI: 10.1109/IGARSS47720.2021.9554037
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/110214
    Scopus© Citations 4