Automated terrain feature identification from remote sensing imagery: a deep learning approach

Li, Wenwen; Hsu, Chia Yu

doi:10.1080/13658816.2018.1542697

Cited by 94 publications

(36 citation statements)

References 21 publications

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“…For example, Guo and Feng (2018) used multiscale and hierarchical deep convolutional features to assign meaningful semantic labels to the points in a three-dimensional (3D) point cloud, which is essential for generating 3D models. Li and Hsu (2018) proposed a deep learning approach to automatically identify terrain features (i.e., sand dunes, craters) from remote sensing imagery. Compared with traditional induction-based approaches, the deep learning approach could detect diverse and complex terrain features more accurately and process massive available geospatial data more efficiently.…”

Section: Deep Learningmentioning

confidence: 99%

Big Data and Cloud Computing

et al. 2019

Manual of Digital Earth

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Big data emerged as a new paradigm to provide unprecedented content and value for Digital Earth. Big Earth data are increasing tremendously with growing heterogeneity, posing grand challenges for the data management lifecycle of storage, processing, analytics, visualization, sharing, and applications. During the same time frame, cloud computing emerged to provide crucial computing support to address these challenges. This chapter introduces Digital Earth data sources, analytical methods, and architecture for data analysis and describes how cloud computing supports big data processing in the context of Digital Earth. Keywords Geoscience • Spatial data infrastructure • Digital transformation • Big data architecture 9.1 Introduction Digital Earth refers to the virtual representation of the Earth we live in. It represents the Earth in the digital world from data to model. Data are collected and models are abstracted to build the digital reality. Massive amounts of data are generated from various sensors deployed to observe our home planet while building Digital Earth. The term "big data" was first presented by NASA researchers to describe the massive amount of information that exceeds the capacities of main memory, local disk, and even remote disk (Friedman 2012). According to the National Institute of Standards and Technology (NIST), "Big Data is a term used to describe the large amount of data in the networked, digitized, sensor-laden, information-driven world" (Chang and Grady 2015). This definition refers to the bounty of digital data from various data sources in the context of Digital Earth, which focus on big data's geographical aspects of social information, Earth observation (EO), sensor observation service (SOS), cyber infrastructure (CI), social media and business information (Guo 2017; Guo et al. 2017; Yang et al. 2017a, b). Digital Earth data are collected from satellites,

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Section: Deep Learningmentioning

confidence: 99%

Big Data and Cloud Computing

et al. 2019

Manual of Digital Earth

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“…For reducing the impact of limited training data, data augmentation techniques, e.g. flipping, distorting, or cropping training images, have been applied to increase the size and diversity of training data (Li and Hsu 2018). However, these data augmentation techniques do not fundamentally address the challenge of the limited training data issue.…”

Section: Limited Training/benchmark Datamentioning

confidence: 99%

Spatiotemporal event detection: a review

Bambacus

Cervone

et al. 2020

International Journal of Digital Earth

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The advancements of sensing technologies, including remote sensing, in situ sensing, social sensing, and health sensing, have tremendously improved our capability to observe and record natural and social phenomena, such as natural disasters, presidential elections, and infectious diseases. The observations have provided an unprecedented opportunity to better understand and respond to the spatiotemporal dynamics of the environment, urban settings, health and disease propagation, business decisions, and crisis and crime. Spatiotemporal event detection serves as a gateway to enable a better understanding by detecting events that represent the abnormal status of relevant phenomena. This paper reviews the literature for different sensing capabilities, spatiotemporal event extraction methods, and categories of applications for the detected events. The novelty of this review is to revisit the definition and requirements of event detection and to layout the overall workflow (from sensing and event extraction methods to the operations and decision-supporting processes based on the extracted events) as an agenda for future event detection research. Guidance is presented on the current challenges to this research agenda, and future directions are discussed for conducting spatiotemporal event detection in the era of big data, advanced sensing, and artificial intelligence.

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“…Hyperspectral Image Analysis for efficient and accurate object detection using Deep Learning is one of the timely topics of GeoAI. The most recent research examples include detection of soil characteristics [18], detailed ways of capturing densely populated areas [19], extracting information from scanned historical maps [20], semantic point sorting [21], innovative spatial interpolation methods [22] and traffic forecasting [23].…”

Section: Application Of the Mame-zsl In Hyperspectral Image Analysismentioning

confidence: 99%

“…Max-pooling was performed over 3 × 3 pixels windows with stride equal to three. All of the CON layers were developed using the Rectified Linear Unit (ReLU) nonlinear Activation Function (ACF), except for the last layer where the Softmax ACF [3] was applied, as it performs better on multi-classification problems like the one under consideration (18).…”

Section: Design Principles and Novelties Of The Introduced Mame-zsl Amentioning

confidence: 99%

GeoAI: A Model-Agnostic Meta-Ensemble Zero-Shot Learning Method for Hyperspectral Image Analysis and Classification

Demertzis

Iliadis

2020

Algorithms

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Deep learning architectures are the most effective methods for analyzing and classifying Ultra-Spectral Images (USI). However, effective training of a Deep Learning (DL) gradient classifier aiming to achieve high classification accuracy, is extremely costly and time-consuming. It requires huge datasets with hundreds or thousands of labeled specimens from expert scientists. This research exploits the MAML++ algorithm in order to introduce the Model-Agnostic Meta-Ensemble Zero-shot Learning (MAME-ZsL) approach. The MAME-ZsL overcomes the above difficulties, and it can be used as a powerful model to perform Hyperspectral Image Analysis (HIA). It is a novel optimization-based Meta-Ensemble Learning architecture, following a Zero-shot Learning (ZsL) prototype. To the best of our knowledge it is introduced to the literature for the first time. It facilitates learning of specialized techniques for the extraction of user-mediated representations, in complex Deep Learning architectures. Moreover, it leverages the use of first and second-order derivatives as pre-training methods. It enhances learning of features which do not cause issues of exploding or diminishing gradients; thus, it avoids potential overfitting. Moreover, it significantly reduces computational cost and training time, and it offers an improved training stability, high generalization performance and remarkable classification accuracy. Algorithms 2020, 13, 61 2 of 25 Algorithms 2020, 13, 61 3 of 25In contrast to the learning process of the traditional neural networks, it is not sufficient for the network to learn good representations of the training classes, as the testing classes are distinct and they are not presented in training. However, it is desirable to learn features which distinguish the existing classes.The evaluation process consists of two distinct stages of the following format [4]:Step 1: Given k examples (value of k-shot), if k = 1, then the process is called one-shot; if k = 5, five-shot, and so on. The parameter k represents the number of labeled samples given to the algorithm by each class. By considering these samples, which comprise the support set, the network is required to classify and eventually adapt to existing classes.Step 2: Unknown examples of the labeled classes are presented randomly, unlike the ones presented in the previous step, which the network is called to correctly classify. The set of examples in this stage is known as the query set.The above procedure (steps) is repeated many times using random classes and examples which are sampled from the testing-evaluation set.As it is immediately apparent from the description of the evaluation process, as the number of classes increases, the task becomes more difficult, because the network has to decide between several alternatives. This means that Zero-shot Learning [5] is clearly more difficult than the one-shot, which is more difficult than the five-shot, and so on. Although humans have the ability to cope with this process, traditional ANN require many more examples to genera...

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Automated terrain feature identification from remote sensing imagery: a deep learning approach

Cited by 94 publications

References 21 publications

Big Data and Cloud Computing

Big Data and Cloud Computing

Spatiotemporal event detection: a review

GeoAI: A Model-Agnostic Meta-Ensemble Zero-Shot Learning Method for Hyperspectral Image Analysis and Classification

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