Now showing 1 - 10 of 19
  • Publication
    Toward Global Estimation of Ground-Level NO2 Pollution With Deep Learning and Remote Sensing
    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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    Scopus© Citations 10
  • Publication
    Distinguishing multicellular life on exoplanets by testing Earth as an exoplanet
    (Cambridge University Press, 2020-10)
    Doughty, Christopher E.
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    Abraham, Andrew J.
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    Windsor, James
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    Gowanlock, Michael
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    Robinson, Tyler
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    Trilling, David E.
    Can multicellular life be distinguished from single cellular life on an exoplanet? We hypothesize that abundant upright photosynthetic multicellular life (trees) will cast shadows at high sun angles that will distinguish them from single cellular life and test this using Earth as an exoplanet. We first test the concept using unmanned aerial vehicles at a replica moon-landing site near Flagstaff, Arizona and show trees have both a distinctive reflectance signature (red edge) and geometric signature (shadows at high sun angles) that can distinguish them from replica moon craters. Next, we calculate reflectance signatures for Earth at several phase angles with POLDER (Polarization and Directionality of Earth's reflectance) satellite directional reflectance measurements and then reduce Earth to a single pixel. We compare Earth to other planetary bodies (Mars, the Moon, Venus and Uranus) and hypothesize that Earth's directional reflectance will be between strongly backscattering rocky bodies with no weathering (like Mars and the Moon) and cloudy bodies with more isotropic scattering (like Venus and Uranus). Our modelling results put Earth in line with strongly backscattering Mars, while our empirical results put Earth in line with more isotropic scattering Venus. We identify potential weaknesses in both the modelled and empirical results and suggest additional steps to determine whether this technique could distinguish upright multicellular life on exoplanets.
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    Scopus© Citations 1
  • Publication
    Masked Vision Transformers for Hyperspectral Image Classification
    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.
  • Publication
    Traffic Noise Estimation from Satellite Imagery with Deep Learning
    (IEEE Geoscience and Remote Sensing Society, 2022-07-20)
    Eicher, Leonardo
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    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
  • Publication
    Estimation of Power Generation and CO2 Emissions Using Satellite Imagery
    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.
  • Publication
    A Multimodal Approach for Event Detection: Study of UK Lockdowns in the Year 2020.
    (IEEE Geoscience and Remote Sensing Society, 2022-07-19) ; ; ;
    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.
  • Publication
    Contrastive Self-Supervised Data Fusion for Satellite Imagery
    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.
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  • 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
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  • 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) ; ; ;
    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.