Machine learning for data-driven discovery in solid Earth geoscience

Bergen, Karianne J.; Johnson, P. A.; Hoop, Maarten V. de; Beroza, Gregory C.

doi:10.1126/science.aau0323

Cited by 741 publications

(389 citation statements)

References 118 publications

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“…They are proven to work efficiently to identify various objects in remote sensing imagery [12][13][14][15][16]. Comprehensive overviews contextualizing the evolution of deep learning and CNNs in geoscience and remote sensing are provided by Bergen et al and Zhu et al [17,18]. In essence, CNNs are neural networks that incorporate the convolution and pooling operation as a layer.…”

Section: Cnn Deep Learning For Cadastral Mappingmentioning

confidence: 99%

Application of Deep Learning for Delineation of Visible Cadastral Boundaries from Remote Sensing Imagery

et al. 2019

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Cadastral boundaries are often demarcated by objects that are visible in remote sensing imagery. Indirect surveying relies on the delineation of visible parcel boundaries from such images. Despite advances in automated detection and localization of objects from images, indirect surveying is rarely automated and relies on manual on-screen delineation. We have previously introduced a boundary delineation workflow, comprising image segmentation, boundary classification and interactive delineation that we applied on Unmanned Aerial Vehicle (UAV) data to delineate roads. In this study, we improve each of these steps. For image segmentation, we remove the need to reduce the image resolution and we limit over-segmentation by reducing the number of segment lines by 80% through filtering. For boundary classification, we show how Convolutional Neural Networks (CNN) can be used for boundary line classification, thereby eliminating the previous need for Random Forest (RF) feature generation and thus achieving 71% accuracy. For interactive delineation, we develop additional and more intuitive delineation functionalities that cover more application cases. We test our approach on more varied and larger data sets by applying it to UAV and aerial imagery of 0.02-0.25 m resolution from Kenya, Rwanda and Ethiopia. We show that it is more effective in terms of clicks and time compared to manual delineation for parcels surrounded by visible boundaries. Strongest advantages are obtained for rural scenes delineated from aerial imagery, where the delineation effort per parcel requires 38% less time and 80% fewer clicks compared to manual delineation.

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Section: Cnn Deep Learning For Cadastral Mappingmentioning

confidence: 99%

Application of Deep Learning for Delineation of Visible Cadastral Boundaries from Remote Sensing Imagery

et al. 2019

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“…Machine learning (ML) techniques 8,9 have enabled broad advances in automated data processing and pata) mbianco@ucsd.edu tern recognition capabilities across many fields, including computer vision, image processing, speech processing, and (geo)physical science. 10,11 ML in acoustics is a rapidly developing field, with many compelling solutions to the aforementioned acoustics challenges. The potential impact of ML-based techniques in the field of acoustics, and the recent attention they have received, motivates this review.…”

Section: Introductionmentioning

confidence: 99%

Machine learning in acoustics: Theory and applications

Bianco

Gerstoft

Traer

et al. 2019

The Journal of the Acoustical Society of America

392

126

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Acoustic data provide scientific and engineering insights in fields ranging from biology and communications to ocean and Earth science. We survey the recent advances and transformative potential of machine learning (ML), including deep learning, in the field of acoustics. ML is a broad family of statistical techniques for automatically detecting and utilizing patterns in data. Relative to conventional acoustics and signal processing, ML is data-driven. Given sufficient training data, ML can discover complex relationships between features and desired labels or actions, or between features themselves. With large volumes of training data, ML can discover models describing complex acoustic phenomena such as human speech and reverberation. ML in acoustics is rapidly developing with compelling results and significant future promise. We first introduce ML, then highlight ML developments in five acoustics research areas: source localization in speech processing, source localization in ocean acoustics, bioacoustics, seismic exploration, and environmental sounds in everyday scenes.

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“…The application scope of machine learning is expanding rapidly in geospatial research. In recent studies, machine learning has been proven effective for extracting information from the immense amount of data collected within the geosciences [21][22][23][24][25]. The advantages of machine learning can support our efforts to develop the best possible predictive understanding of how the complex interacting geosystem works [23].…”

Section: Introductionmentioning

confidence: 99%

“…In recent studies, machine learning has been proven effective for extracting information from the immense amount of data collected within the geosciences [21][22][23][24][25]. The advantages of machine learning can support our efforts to develop the best possible predictive understanding of how the complex interacting geosystem works [23]. Bergen et al pointed out that the use of machine learning in geosciences could mainly focus on (1) performing the complex predictive tasks that are difficult for numeric models; (2) directly modeling the processes and interactions by approximating numerical simulations; and (3) revealing new and often unanticipated patterns, structures, or relationships [23].…”

Section: Introductionmentioning

confidence: 99%

“…The advantages of machine learning can support our efforts to develop the best possible predictive understanding of how the complex interacting geosystem works [23]. Bergen et al pointed out that the use of machine learning in geosciences could mainly focus on (1) performing the complex predictive tasks that are difficult for numeric models; (2) directly modeling the processes and interactions by approximating numerical simulations; and (3) revealing new and often unanticipated patterns, structures, or relationships [23]. In the foreseen future, machine learning will play a key role in the effort to better understand the complex interactions among solid earth, ocean, and atmosphere.…”

Section: Introductionmentioning

confidence: 99%

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Geoweaver: Advanced Cyberinfrastructure for Managing Hybrid Geoscientific AI Workflows

Sun

Burgess

et al. 2020

IJGI

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AI (artificial intelligence)-based analysis of geospatial data has gained a lot of attention. Geospatial datasets are multi-dimensional; have spatiotemporal context; exist in disparate formats; and require sophisticated AI workflows that include not only the AI algorithm training and testing, but also data preprocessing and result post-processing. This complexity poses a huge challenge when it comes to full-stack AI workflow management, as researchers often use an assortment of time-intensive manual operations to manage their projects. However, none of the existing workflow management software provides a satisfying solution on hybrid resources, full file access, data flow, code control, and provenance. This paper introduces a new system named Geoweaver to improve the efficiency of full-stack AI workflow management. It supports linking all the preprocessing, AI training and testing, and post-processing steps into a single automated workflow. To demonstrate its utility, we present a use case in which Geoweaver manages end-to-end deep learning for in-time crop mapping using Landsat data. We show how Geoweaver effectively removes the tedium of managing various scripts, code, libraries, Jupyter Notebooks, datasets, servers, and platforms, greatly reducing the time, cost, and effort researchers must spend on such AI-based workflows. The concepts demonstrated through Geoweaver serve as an important building block in the future of cyberinfrastructure for AI research.

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Machine learning for data-driven discovery in solid Earth geoscience

Cited by 741 publications

References 118 publications

Application of Deep Learning for Delineation of Visible Cadastral Boundaries from Remote Sensing Imagery

Application of Deep Learning for Delineation of Visible Cadastral Boundaries from Remote Sensing Imagery

Machine learning in acoustics: Theory and applications

Geoweaver: Advanced Cyberinfrastructure for Managing Hybrid Geoscientific AI Workflows

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