Recent advances in deep learning

Wang, Xizhao; Zhao, Yixuan; Pourpanah, Farhad

doi:10.1007/s13042-020-01096-5

“…Consequently, these birds might have been lost in the low-resolution feature map that was generated by the last convolutional layer after several max-pooling operations. Small object detection is also an open-problem in many other datasets [81]. Other main reasons for the misdetections were intra-species variation, unusual bird poses, occlusions by other birds and plants, cast shadows, and background clutter.…”

Section: Automated Bird Detection Discussionmentioning

confidence: 99%

Automated Bird Counting with Deep Learning for Regional Bird Distribution Mapping

Akçay

¹

,

Kabasakal

²

,

Aksu

³

et al. 2020

Animals

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A challenging problem in the field of avian ecology is deriving information on bird population movement trends. This necessitates the regular counting of birds which is usually not an easily-achievable task. A promising attempt towards solving the bird counting problem in a more consistent and fast way is to predict the number of birds in different regions from their photos. For this purpose, we exploit the ability of computers to learn from past data through deep learning which has been a leading sub-field of AI for image understanding. Our data source is a collection of on-ground photos taken during our long run of birding activity. We employ several state-of-the-art generic object-detection algorithms to learn to detect birds, each being a member of one of the 38 identified species, in natural scenes. The experiments revealed that computer-aided counting outperformed the manual counting with respect to both accuracy and time. As a real-world application of image-based bird counting, we prepared the spatial bird order distribution and species diversity maps of Turkey by utilizing the geographic information system (GIS) technology. Our results suggested that deep learning can assist humans in bird monitoring activities and increase citizen scientists’ participation in large-scale bird surveys.

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“…For example, CNN-based architectures are composed of a stack of layers or a stack of modules. Automated approaches can determine the optimal number of layers or the optimal number of modules required for a given application task, and to optimize the internal structure of a module [178].…”

Section: Automated Approachesmentioning

confidence: 99%

Machine learning for landslides prevention: a survey

Ma

¹

,

Mei

²

,

Piccialli

³

2020

Neural Comput & Applic

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Landslides are one of the most critical categories of natural disasters worldwide and induce severely destructive outcomes to human life and the overall economic system. To reduce its negative effects, landslides prevention has become an urgent task, which includes investigating landslide-related information and predicting potential landslides. Machine learning is a state-of-the-art analytics tool that has been widely used in landslides prevention. This paper presents a comprehensive survey of relevant research on machine learning applied in landslides prevention, mainly focusing on (1) landslides detection based on images, (2) landslides susceptibility assessment, and (3) the development of landslide warning systems. Moreover, this paper discusses the current challenges and potential opportunities in the application of machine learning algorithms for landslides prevention.

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“…For example, CNN-based architectures are composed of a stack of layers or a stack of modules. Automated approaches can determine the optimal number of layers or the optimal number of modules required for a given application task, and to optimize the internal structure of a module [198].…”

Section: Automated Approachesmentioning

confidence: 99%

Machine Learning for Landslides Prevention: A Survey

Ma¹,

Mei²

2020

Preprint

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Landslides are one of the most critical categories of natural disasters worldwide and induce severely destructive outcomes to human life and the overall economic system. To reduce its negative effects, landslides prevention has become an urgent task, which includes investigating landslide-related information and predicting potential landslides. Machine learning is a state-of-the-art analytics tool that has been widely used in landslides prevention. This paper presents a comprehensive survey of relevant research on machine learning applied in landslides prevention, mainly focusing on (1) landslides detection based on images, (2) landslides susceptibility assessment, and (3) the development of landslide warning systems. Moreover, this paper discusses the current challenges and potential opportunities in the application of machine learning algorithms for landslides prevention.

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Recent advances in deep learning

Cited by 178 publications

References 21 publications

Automated Bird Counting with Deep Learning for Regional Bird Distribution Mapping

Automated Bird Counting with Deep Learning for Regional Bird Distribution Mapping

Machine learning for landslides prevention: a survey

Machine Learning for Landslides Prevention: A Survey

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