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  • 1
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    Seeing the Forest for the Trees: Mapping Cover and Counting Trees from Aerial Images of a Mangrove Forest Using Artificial Intelligence (2023)
    MDPI AG
    In:  Remote Sensing vol. 15 no. 3334
    Details
    Publication Date: 2024-06-01
    Description: Mangrove forests provide valuable ecosystem services to coastal communities across tropical and subtropical regions. Current anthropogenic stressors threaten these ecosystems and urge researchers to create improved monitoring methods for better environmental management. Recent efforts that have focused on automatically quantifying the above-ground biomass using image analysis have found some success on high resolution imagery of mangrove forests that have sparse vegetation. In this study, we focus on stands of mangrove forests with dense vegetation consisting of the endemic Pelliciera rhizophorae and the more widespread Rhizophora mangle mangrove species located in the remote Utría National Park in the Colombian Pacific coast. Our developed workflow used consumer-grade Unoccupied Aerial System (UAS) imagery of the mangrove forests, from which large orthophoto mosaics and digital surface models are built. We apply convolutional neural networks (CNNs) for instance segmentation to accurately delineate (33% instance average precision) individual tree canopies for the Pelliciera rhizophorae species. We also apply CNNs for semantic segmentation to accurately identify (97% precision and 87% recall) the area coverage of the Rhizophora mangle mangrove tree species as well as the area coverage of surrounding mud and water land-cover classes. We provide a novel algorithm for merging predicted instance segmentation tiles of trees to recover tree shapes and sizes in overlapping border regions of tiles. Using the automatically segmented ground areas we interpolate their height from the digital surface model to generate a digital elevation model, significantly reducing the effort for ground pixel selection. Finally, we calculate a canopy height model from the digital surface and elevation models and combine it with the inventory of Pelliciera rhizophorae trees to derive the height of each individual mangrove tree. The resulting inventory of a mangrove forest, with individual P. rhizophorae tree height information, as well as crown shape and size descriptions, enables the use of allometric equations to calculate important monitoring metrics, such as above-ground biomass and carbon stocks.
    Keywords: mangrove forests ; forest inventory ; monitoring ; habitat mapping ; UAV ; UAS ; artificial ; intelligence ; machine learning ; instance segmentation ; semantic segmentation ; above ground biomass ; carbon stock
    Repository Name: National Museum of Natural History, Netherlands
    Type: info:eu-repo/semantics/article
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  • 2
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    Digitizing the coral reef: Machine learning of underwater spectral images enables dense taxonomic mapping of benthic habitats (2022)
    In:  Methods in Ecology and Evolution vol. 14 no. 2, pp. 596-613
    Details
    Publication Date: 2024-06-01
    Description: Coral reefs are the most biodiverse marine ecosystems, and host a wide range of taxonomic diversity in a complex spatial community structure. Existing coral reef survey methods struggle to accurately capture the taxonomic detail within the complex spatial structure of benthic communities. We propose a workflow to leverage underwater hyperspectral image transects and two machine learning algorithms to produce dense habitat maps of 1150 m2 of reefs across the Curaçao coastline. Our multi-method workflow labelled all 500+ million pixels with one of 43 classes at taxonomic family, genus or species level for corals, algae, sponges, or to substrate labels such as sediment, turf algae and cyanobacterial mats. With low annotation effort (only 2% of pixels) and no external data, our workflow enables accurate (Fbeta of 87%) survey-scale mapping, with unprecedented thematic detail and with fine spatial resolution (2.5 cm/pixel). Our assessments of the composition and configuration of the benthic communities of 23 image transects showed high consistency. Digitizing the reef habitat and community structure enables validation and novel analysis of pattern and scale in coral reef ecology. Our dense habitat maps reveal the inadequacies of point sampling methods to accurately describe reef benthic communities.
    Keywords: coral reefs ; habitat mapping ; hyperspectral imaging ; machine learning ; survey scale mapping ; thematic detail
    Repository Name: National Museum of Natural History, Netherlands
    Type: info:eu-repo/semantics/article
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  • 3
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    “Seeing” Beneath the Clouds—Machine‐Learning‐Based Reconstruction of North African Dust Plumes (2024)
    Details
    Publication Date: 2024-05-22
    Description: 〈title xmlns:mml="http://www.w3.org/1998/Math/MathML"〉Abstract〈/title〉〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉Mineral dust is one of the most abundant atmospheric aerosol species and has various far‐reaching effects on the climate system and adverse impacts on air quality. Satellite observations can provide spatio‐temporal information on dust emission and transport pathways. However, satellite observations of dust plumes are frequently obscured by clouds. We use a method based on established, machine‐learning‐based image in‐painting techniques to restore the spatial extent of dust plumes for the first time. We train an artificial neural net (ANN) on modern reanalysis data paired with satellite‐derived cloud masks. The trained ANN is applied to cloud‐masked, gray‐scaled images, which were derived from false color images indicating elevated dust plumes in bright magenta. The images were obtained from the Spinning Enhanced Visible and Infrared Imager instrument onboard the Meteosat Second Generation satellite. We find up to 15% of summertime observations in West Africa and 10% of summertime observations in Nubia by satellite images miss dust plumes due to cloud cover. We use the new dust‐plume data to demonstrate a novel approach for validating spatial patterns of the operational forecasts provided by the World Meteorological Organization Dust Regional Center in Barcelona. The comparison elucidates often similar dust plume patterns in the forecasts and the satellite‐based reconstruction, but once trained, the reconstruction is computationally inexpensive. Our proposed reconstruction provides a new opportunity for validating dust aerosol transport in numerical weather models and Earth system models. It can be adapted to other aerosol species and trace gases.〈/p〉
    Description: Plain Language Summary: Most dust and sand particles in the atmosphere originate from North Africa. Since ground‐based observations of dust plumes in North Africa are sparse, investigations often rely on satellite observations. Dust plumes are frequently obscured by clouds, making it difficult to study the full extent. We use machine‐learning methods to restore information about the extent of dust plumes beneath clouds in 2021 and 2022 at 9, 12, and 15 UTC. We use the reconstructed dust patterns to demonstrate a new way to validate the dust forecast ensemble provided by the World Meteorological Organization Dust Regional Center in Barcelona, Spain. Our proposed method is computationally inexpensive and provides new opportunities for assessing the quality of dust transport simulations. The method can be transferred to reconstruct other aerosol and trace gas plumes.〈/p〉
    Description: Key Points: 〈list list-type="bullet"〉 〈list-item〉 〈p xml:lang="en"〉We present the first fast reconstruction of cloud‐obscured Saharan dust plumes through novel machine learning applied to satellite images〈/p〉〈/list-item〉 〈list-item〉 〈p xml:lang="en"〉The reconstruction algorithm utilizes partial convolutions to restore cloud‐induced gaps in gray‐scaled Meteosat Second Generation‐Spinning Enhanced Visible and Infrared Imager Dust RGB images〈/p〉〈/list-item〉 〈list-item〉 〈p xml:lang="en"〉World Meteorological Organization dust forecasts for North Africa mostly agree with the satellite‐based reconstruction of the dust plume extent〈/p〉〈/list-item〉 〈/list〉 〈/p〉
    Description: GEOMAR Helmholtz Centre for Ocean Research Kiel
    Description: University of Cologne
    Description: https://doi.org/10.5281/zenodo.6475858
    Description: https://github.com/tobihose/Masterarbeit
    Description: https://dust.aemet.es/
    Description: https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-reanalysis-eac4?tab=overview
    Description: https://navigator.eumetsat.int/product/EO:EUM:DAT:MSG:DUST
    Description: https://navigator.eumetsat.int/product/EO:EUM:DAT:MSG:CLM
    Description: https://doi.org/10.5067/KLICLTZ8EM9D
    Description: https://disc.gsfc.nasa.gov/datasets?project=MERRA-2
    Description: https://doi.org/10.5067/MODIS/MOD08_D3.061
    Description: https://doi.org/10.5067/MODIS/MYD08_D3.061
    Description: https://doi.org/10.5281/ZENODO.8278518
    Keywords: ddc:551.5 ; mineral dust ; North Africa ; MSG SEVIRI ; machine learning ; cloud removal ; satellite remote sensing
    Language: English
    Type: doc-type:article
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    CITATION GEO-LEO
  • 4
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    Towards a standardized framework for AI-assisted, image-based monitoring of nocturnal insects (2024)
    The Royal Society
    In:  Philosophical Transactions of the Royal Society B: Biological Sciences vol. 379 no. 1904
    Details
    Publication Date: 2024-05-17
    Description: Automated sensors have potential to standardize and expand the monitoring of insects across the globe. As one of the most scalable and fastest developing sensor technologies, we describe a framework for automated, image-based monitoring of nocturnal insects—from sensor development and field deployment to workflows for data processing and publishing. Sensors comprise a light to attract insects, a camera for collecting images and a computer for scheduling, data storage and processing. Metadata is important to describe sampling schedules that balance the capture of relevant ecological information against power and data storage limitations. Large data volumes of images from automated systems necessitate scalable and effective data processing. We describe computer vision approaches for the detection, tracking and classification of insects, including models built from existing aggregations of labelled insect images. Data from automated camera systems necessitate approaches that account for inherent biases. We advocate models that explicitly correct for bias in species occurrence or abundance estimates resulting from the imperfect detection of species or individuals present during sampling occasions. We propose ten priorities towards a step-change in automated monitoring of nocturnal insects, a vital task in the face of rapid biodiversity loss from global threats. This article is part of the theme issue ‘Towards a toolkit for global insect biodiversity monitoring’.
    Keywords: biodiversity monitoring ; machine learning ; moths ; camera trap
    Repository Name: National Museum of Natural History, Netherlands
    Type: info:eu-repo/semantics/article
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  • 5
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    Monitoring sources of volcanic activity at Mount Etna using pattern recognition techniques on infrasound signals (2024)
    Oxford University Press on behalf of the Royal Astronomical Society and the German Geophysical Society
    Details
    Publication Date: 2024-05-10
    Description: Infrasound monitoring plays an important role in the framework of the surveillance of Mt. Etna, Europe’s largest active volcano. Compared to seismic monitoring, which is particularly effective for buried sources, infrasound signals mirror the activity of shallow sources like Strombolian explosions or degassing. The interpretation of infrasound signals is difficult to the untrained eye, as we have to account for volcanic and non-volcanic sources. The problem of handling large and complex data sets can be tackled with machine learning, namely pattern recognition techniques. Here, we focus on so-called ‘Unsupervised Learning’, where we identify groups of patterns being similar to each other. The degree of similarity is based on a metric measuring the distance among the features of the patterns. This work aims at the identification of typical regimes of infrasound radiation and their relation to the state of volcanic activity at Mt. Etna. For this goal, we defined features describing any infrasound pattern. These features were obtained using wavelet transform. We applied ‘Self-Organizing Maps’ (SOM) to the features projecting them to a 2-D representation space—the ‘map’. An intriguing aspect of SOM resides in the fact that the position of the patterns on the map can be expressed by a colour code, in a manner that similar patterns are assigned a similar colour code. This simplified representation of multivariate patterns allows to follow the development of their characteristics with time efficiently. During a training phase we considered a reference data set, which encompassed a large variety of scenarios. We identified typical groups of patterns which correspond to a specific regime of activity, being representative of the state of the volcano or noise conditions. These groups form areas on the 2-D maps. In a second step, we considered a test data set, which was not used during the training phase. Applying the same pre-processing as for the training data, we blindly assigned the test patterns to the regimes found before, identifying the one whose colour code is most similar to the one calculated to the test pattern. We are thus able to assess the validity of the prediction. The classification scheme presented provides a reliable assessment of the state of activity and adds useful and supplementary details to the results of the real-time automatic system in operation at Istituto Nazionale di Geofisica e Vulcanologia—Osservarorio Etneo. This is of particular importance when no visible information of the volcanic activity is available either for unfavourable meteorological conditions or during night time.
    Description: IMPACT PROJECT (INGV Department strategic Projects - 2019)
    Description: Published
    Description: 1-16
    Description: JCR Journal
    Keywords: infrasound ; volcano monitoring ; self-organization ; time-series analysis ; machine learning ; volcanic hazards and risks ; 04.06. Seismology ; 04.08. Volcanology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 6
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    Probabilistic inter-scheme compatibility matrices for buildings. An application using existing vulnerability models for earthquakes and tsunami from synthetic datasets constructed using the AeDEs form through expert-based heuristics (2022)
    GFZ Data Services
    Details
    Publication Date: 2024-04-29
    Description: Abstract
    Description: This folder contains the scripts, input and output files required to calculate the inter-scheme conversion matrices for building types and the implicit damage states of their respective fragility models for two selected vulnerability schemes: one for earthquakes and the other for tsunamis. They were used in previous studies to characterize the residential building stock of Lima. The outcomes generated in this data repository are valuable inputs to then calculate the disaggregated and cumulative damage and losses expected for cascading hazard scenarios.
    Description: Other
    Description: In recent decades, the risk to society due to natural hazards has increased globally. To counteract this trend, effective risk management is necessary, for which reliable information is essential. Most existing natural hazard and risk information systems address only single components of a complex risk assessment chain, such as, for instance, focusing on specific hazards or simple loss measures. Complex interactions, such as cascading effects, are typically not considered, as well as many of the underlying sources of uncertainty. This can lead to inadequate or even miss-leading risk management strategies, thus hindering efficient prevention and mitigation measures, and ultimately undermining the resilience of societies. Therefore, experts from different disciplines work together in the joint project RIESGOS 2.0 (Scenario-based multi-risk assessment in the Andes region) and develop innovative scientific methods for the evaluation of complex multi-risk situations with the aim to transfer the results as web services into a demonstrator for a multi-risk information system.
    Keywords: machine learning ; vulnerability ; multi-hazard ; earthquake fragility ; tsunami fragility ; cumulative damage ; Bayesian approach ; RIESGOS ; Scenario-based multi-risk assessment in the Andes region ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 EARTHQUAKES ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 TSUNAMIS
    Type: Dataset , Dataset
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    DATA GFZ
  • 7
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    Process Mining Workshops (2023)
    Springer Nature | Springer Nature Switzerland
    Details
    Publication Date: 2024-04-14
    Description: This open access book constitutes revised selected papers from the International Workshops held at the 4th International Conference on Process Mining, ICPM 2022, which took place in Bozen-Bolzano, Italy, during October 23–28, 2022. The conference focuses on the area of process mining research and practice, including theory, algorithmic challenges, and applications. The co-located workshops provided a forum for novel research ideas. The 42 papers included in this volume were carefully reviewed and selected from 89 submissions. They stem from the following workshops: – 3rd International Workshop on Event Data and Behavioral Analytics (EDBA) – 3rd International Workshop on Leveraging Machine Learning in Process Mining (ML4PM) – 3rd International Workshop on Responsible Process Mining (RPM) (previously known as Trust, Privacy and Security Aspects in Process Analytics) – 5th International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) – 3rd International Workshop on Streaming Analytics for Process Mining (SA4PM) – 7th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI) – 1st International Workshop on Education meets Process Mining (EduPM) – 1st International Workshop on Data Quality and Transformation in Process Mining (DQT-PM)
    Keywords: process mining ; process discovery ; process analytics ; process querying ; conformance checking ; predictive process monitoring ; data science ; knowledge graphs ; event data ; streaming analytics ; machine learning ; deep learning ; business process management ; health informatics ; thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining ; thema EDItEUR::K Economics, Finance, Business and Management::KJ Business and Management::KJQ Business mathematics and systems ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning ; thema EDItEUR::U Computing and Information Technology::UB Information technology: general topics ; thema EDItEUR::U Computing and Information Technology::UB Information technology: general topics::UBH Digital and information technologies: Health and safety aspects
    Language: English
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    E-BOOK
  • 8
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    Emotion Recognition (2023)
    IntechOpen | IntechOpen
    Details
    Publication Date: 2024-04-14
    Description: Emotion is a complex phenomenon that varies from person to person. Different emotional states of a person can be inferred through external and internal reactions that change in different situations. Emotion recognition has become a research milestone in cognitive science, neuroscience, computer science, psychology, artificial intelligence, and other areas. Emotion recognition research uses non-physiological signals such as facial expression, speech, and body movement, as well as physiological signals and images such as electrical skin resistance (GSR), heart rate (HR), electrocardiogram (ECG), functional magnetic resonance imaging (fMRI), electroencephalogram (EEG) and magnetoencephalogram (MEG). This book provides a comprehensive overview of the different techniques used in emotion recognition and discusses recent developments, perspectives, and applications in the field.
    Keywords: machine learning ; deep learning ; feature extraction ; emotional intelligence ; creativity ; consumer behavior ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYZ Human–computer interaction::UYZG User interface design and usability
    Language: English
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    E-BOOK
  • 9
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    Internet of Things (2023)
    IntechOpen | IntechOpen
    Details
    Publication Date: 2024-04-14
    Description: The Internet of Things (IoT) has emerged as a popular area of research and has piqued the interest of academics and scholars worldwide. As such, many works have been done on IoT in a variety of application areas. Written by leading experts in the field, this book serves as a showcase of the breadth of IoT research conducted in recent years for people who, while not experts in the field, do have prior knowledge of the IoT. The book also serves curious, non-technical readers, enabling them to understand necessary concepts and terminologies associated with the IoT.
    Keywords: artificial intelligence ; iot ; machine learning ; healthcare ; ai ; ehealth ; thema EDItEUR::U Computing and Information Technology::UD Digital Lifestyle and online world: consumer and user guides::UDF Email: consumer / user guides
    Language: English
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    E-BOOK
  • 10
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    Data and Decision Sciences (2024)
    IntechOpen | IntechOpen
    Details
    Publication Date: 2024-04-14
    Description: This book provides an overview of Data and Decision Sciences (DDS) and recent advances and applications in space-based systems and business, medical, and agriculture processes, decision optimization modeling, and cognitive decision-making. Written by experts, this volume is organized into four sections and seven chapters. It is a valuable resource for educators, engineers, scientists, and researchers in the field of DDS.
    Keywords: machine learning ; simulation ; sustainable agriculture ; regression ; decision support system ; data analytics ; thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining
    Language: English
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    E-BOOK
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