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  • 1
    Online Resource
    Online Resource
    AQ-Bench: a benchmark dataset for machine learning on global air quality metrics
    Copernicus GmbH ; 2021
    In:  Earth System Science Data Vol. 13, No. 6 ( 2021-06-24), p. 3013-3033
    Details
    In: Earth System Science Data, Copernicus GmbH, Vol. 13, No. 6 ( 2021-06-24), p. 3013-3033
    Abstract: Abstract. With the AQ-Bench dataset, we contribute to the recent developments towards shared data usage and machine learning methods in the field of environmental science. The dataset presented here enables researchers to relate global air quality metrics to easy-access metadata and to explore different machine learning methods for obtaining estimates of air quality based on this metadata. AQ-Bench contains a unique collection of aggregated air quality data from the years 2010–2014 and metadata at more than 5500 air quality monitoring stations all over the world, provided by the first Tropospheric Ozone Assessment Report (TOAR). It focuses in particular on metrics of tropospheric ozone, which has a detrimental effect on climate, human morbidity and mortality, as well as crop yields. The purpose of this dataset is to produce estimates of various long-term ozone metrics based on time-independent local site conditions. We combine this task with a suitable evaluation metric. Baseline scores obtained from a linear regression method, a fully connected neural network and random forest are provided for reference and validation. AQ-Bench offers a low-threshold entrance for all machine learners with an interest in environmental science and for atmospheric scientists who are interested in applying machine learning techniques. It enables them to start with a real-world problem relevant to humans and nature. The dataset and introductory machine learning code are available at https://doi.org/10.23728/b2share.30d42b5a87344e82855a486bf2123e9f (Betancourt et al., 2020) and https://gitlab.version.fz-juelich.de/esde/machine-learning/aq-bench (Betancourt et al., 2021). AQ-Bench thus provides a blueprint for environmental benchmark datasets as well as an example for data re-use according to the FAIR principles.
    Type of Medium: Online Resource
    ISSN: 1866-3516
    URL: Article
    DOI: 10.5194/essd-13-3013-2021
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2021
    detail.hit.zdb_id: 2475469-9
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  • 2
    Online Resource
    Online Resource
    DETECTION OF DISEASE SYMPTOMS ON HYPERSPECTRAL 3D PLANT MODELS
    Copernicus GmbH ; 2016
    In:  ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences Vol. III-7 ( 2016-06-07), p. 89-96
    Details
    In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Copernicus GmbH, Vol. III-7 ( 2016-06-07), p. 89-96
    Abstract: Abstract. We analyze the benefit of combining hyperspectral images information with 3D geometry information for the detection of Cercospora leaf spot disease symptoms on sugar beet plants. Besides commonly used one-class Support Vector Machines, we utilize an unsupervised sparse representation-based approach with group sparsity prior. Geometry information is incorporated by representing each sample of interest with an inclination-sorted dictionary, which can be seen as an 1D topographic dictionary. We compare this approach with a sparse representation based approach without geometry information and One-Class Support Vector Machines. One-Class Support Vector Machines are applied to hyperspectral data without geometry information as well as to hyperspectral images with additional pixelwise inclination information. Our results show a gain in accuracy when using geometry information beside spectral information regardless of the used approach. However, both methods have different demands on the data when applied to new test data sets. One-Class Support Vector Machines require full inclination information on test and training data whereas the topographic dictionary approach only need spectral information for reconstruction of test data once the dictionary is build by spectra with inclination.
    Type of Medium: Online Resource
    ISSN: 2194-9050
    URL: Article
    DOI: 10.5194/isprs-annals-III-7-89-2016
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2016
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  • 3
    Online Resource
    Online Resource
    INVESTIGATION OF LATENT TRACES USING INFRARED REFLECTANCE HYPERSPECTRAL IMAGING
    Copernicus GmbH ; 2016
    In:  ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences Vol. III-7 ( 2016-06-07), p. 97-102
    Details
    In: ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, Copernicus GmbH, Vol. III-7 ( 2016-06-07), p. 97-102
    Abstract: The detection of traces is a main task of forensics. Hyperspectral imaging is a potential method from which we expect to capture more fluorescence effects than with common forensic light sources. This paper shows that the use of hyperspectral imaging is suited for the analysis of latent traces and extends the classical concept to the conservation of the crime scene for retrospective laboratory analysis. We examine specimen of blood, semen and saliva traces in several dilution steps, prepared on cardboard substrate. As our key result we successfully make latent traces visible up to dilution factor of 1:8000. We can attribute most of the detectability to interference of electromagnetic light with the water content of the traces in the shortwave infrared region of the spectrum. In a classification task we use several dimensionality reduction methods (PCA and LDA) in combination with a Maximum Likelihood classifier, assuming normally distributed data. Further, we use Random Forest as a competitive approach. The classifiers retrieve the exact positions of labelled trace preparation up to highest dilution and determine posterior probabilities. By modelling the classification task with a Markov Random Field we are able to integrate prior information about the spatial relation of neighboured pixel labels.
    Type of Medium: Online Resource
    ISSN: 2194-9050
    URL: Article
    DOI: 10.5194/isprsannals-III-7-97-2016
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2016
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  • 4
    Online Resource
    Online Resource
    DETECTION OF DISEASE SYMPTOMS ON HYPERSPECTRAL 3D PLANT MODELS
    Copernicus GmbH ; 2016
    In:  ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences Vol. III-7 ( 2016-06-07), p. 89-96
    Details
    In: ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, Copernicus GmbH, Vol. III-7 ( 2016-06-07), p. 89-96
    Abstract: We analyze the benefit of combining hyperspectral images information with 3D geometry information for the detection of 〈i〉Cercospora〈/i〉 leaf spot disease symptoms on sugar beet plants. Besides commonly used one-class Support Vector Machines, we utilize an unsupervised sparse representation-based approach with group sparsity prior. Geometry information is incorporated by representing each sample of interest with an inclination-sorted dictionary, which can be seen as an 1D topographic dictionary. We compare this approach with a sparse representation based approach without geometry information and One-Class Support Vector Machines. One-Class Support Vector Machines are applied to hyperspectral data without geometry information as well as to hyperspectral images with additional pixelwise inclination information. Our results show a gain in accuracy when using geometry information beside spectral information regardless of the used approach. However, both methods have different demands on the data when applied to new test data sets. One-Class Support Vector Machines require full inclination information on test and training data whereas the topographic dictionary approach only need spectral information for reconstruction of test data once the dictionary is build by spectra with inclination.
    Type of Medium: Online Resource
    ISSN: 2194-9050
    URL: Article
    DOI: 10.5194/isprsannals-III-7-89-2016
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2016
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  • 5
    Online Resource
    Online Resource
    Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties
    Copernicus GmbH ; 2022
    In:  Geoscientific Model Development Vol. 15, No. 11 ( 2022-06-03), p. 4331-4354
    Details
    In: Geoscientific Model Development, Copernicus GmbH, Vol. 15, No. 11 ( 2022-06-03), p. 4331-4354
    Abstract: Abstract. Tropospheric ozone is a toxic greenhouse gas with a highly variable spatial distribution which is challenging to map on a global scale. Here, we present a data-driven ozone-mapping workflow generating a transparent and reliable product. We map the global distribution of tropospheric ozone from sparse, irregularly placed measurement stations to a high-resolution regular grid using machine learning methods. The produced map contains the average tropospheric ozone concentration of the years 2010–2014 with a resolution of 0.1∘ × 0.1∘. The machine learning model is trained on AQ-Bench (“air quality benchmark dataset”), a pre-compiled benchmark dataset consisting of multi-year ground-based ozone measurements combined with an abundance of high-resolution geospatial data. Going beyond standard mapping methods, this work focuses on two key aspects to increase the integrity of the produced map. Using explainable machine learning methods, we ensure that the trained machine learning model is consistent with commonly accepted knowledge about tropospheric ozone. To assess the impact of data and model uncertainties on our ozone map, we show that the machine learning model is robust against typical fluctuations in ozone values and geospatial data. By inspecting the input features, we ensure that the model is only applied in regions where it is reliable. We provide a rationale for the tools we use to conduct a thorough global analysis. The methods presented here can thus be easily transferred to other mapping applications to ensure the transparency and reliability of the maps produced.
    Type of Medium: Online Resource
    ISSN: 1991-9603
    URL: Article
    DOI: 10.5194/gmd-15-4331-2022
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2022
    detail.hit.zdb_id: 2456725-5
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  • 6
    Online Resource
    Online Resource
    INVESTIGATION OF LATENT TRACES USING INFRARED REFLECTANCE HYPERSPECTRAL IMAGING
    Copernicus GmbH ; 2016
    In:  ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences Vol. III-7 ( 2016-06-07), p. 97-102
    Details
    In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Copernicus GmbH, Vol. III-7 ( 2016-06-07), p. 97-102
    Abstract: Abstract. The detection of traces is a main task of forensics. Hyperspectral imaging is a potential method from which we expect to capture more fluorescence effects than with common forensic light sources. This paper shows that the use of hyperspectral imaging is suited for the analysis of latent traces and extends the classical concept to the conservation of the crime scene for retrospective laboratory analysis. We examine specimen of blood, semen and saliva traces in several dilution steps, prepared on cardboard substrate. As our key result we successfully make latent traces visible up to dilution factor of 1:8000. We can attribute most of the detectability to interference of electromagnetic light with the water content of the traces in the shortwave infrared region of the spectrum. In a classification task we use several dimensionality reduction methods (PCA and LDA) in combination with a Maximum Likelihood classifier, assuming normally distributed data. Further, we use Random Forest as a competitive approach. The classifiers retrieve the exact positions of labelled trace preparation up to highest dilution and determine posterior probabilities. By modelling the classification task with a Markov Random Field we are able to integrate prior information about the spatial relation of neighboured pixel labels.
    Type of Medium: Online Resource
    ISSN: 2194-9050
    URL: Article
    DOI: 10.5194/isprs-annals-III-7-97-2016
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2016
    Location Call Number Limitation Availability
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    Online Resource
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