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Article
Environmental Sciences
Xiaoyi Han et al.
Summary: DEM super-resolution method based on deep learning has found wide applications in various fields, but it lacks global information. To address this issue, we propose a global-information-constrained deep learning network for DEM super-resolution. Our method outperforms other methods in retaining terrain features and generating results closer to ground truth DEM data.
Article
Environmental Sciences
Guodong Chen et al.
Summary: In this study, an enhanced residual feature fusion network (ERFFN) is proposed for digital elevation model (DEM) super-resolution (SR). The network effectively utilizes local residual features by grouping residual modules and inserting enhanced spatial residual attention modules. Terrain weight modules are also integrated to improve efficiency. Experimental results show that ERFFN outperforms state-of-the-art methods with significant reduction in mean absolute error and improved accuracy in terrain features.
Article
Computer Science, Software Engineering
Jian Zhang et al.
Summary: This paper proposes a novel neural network approach for the rapid synthesis of multi-style terrains. By utilizing a conditional generative adversarial network and multiple discriminators, the method is able to learn and infer different styles from real terrain data. Experimental results demonstrate the effectiveness of the approach in generating realistic terrains with high fidelity.
Article
Chemistry, Analytical
Yifan Zhang et al.
Summary: This paper investigates the applicability of state-of-the-art image super-resolution methods for digital elevation model (DEM) super-resolution. It compares traditional interpolation methods with three excellent super-resolution methods based on neural networks. The experimental results suggest that SRGAN, based on generative adversarial networks, performs best in DEM super-resolution.
Article
Environmental Sciences
Sijin Li et al.
Summary: This study constructs a deep learning-based model to generate simulated terrain data with similar characteristics to loess landforms. The results show that the simulated data have similar elevation and slope probability distributions to the reference data, although there may be some inaccuracies in terrain details.
Article
Geography, Physical
Yifan Zhang et al.
Summary: This paper proposes a terrain feature-aware superresolution model (TfaSR) for terrain data superresolution, aiming to extract and optimize terrain features. Experimental results show that TfaSR achieves state-of-the-art performance in preserving terrain features during DEM SR.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2022)
Article
Geosciences, Multidisciplinary
Liyang Xiong et al.
Summary: Terrain is a crucial natural geographic feature that plays a significant role in physical processes. The fields of geomorphometry and terrain analysis have provided abundant topographic data and tools, which have contributed to the understanding of geomorphology, hydrology, soil science, and geographic information systems (GIS). Recent advancements in analysis theory, methods, data acquisition techniques, and analysis platforms have facilitated the interpretation of topographic characteristics and associated mechanisms and processes. Future directions include addressing challenges in data collection and construction, expanding the application of efficient analysis frameworks and tools, and integrating terrain analysis with other disciplines for cross-analysis purposes.
EARTH-SCIENCE REVIEWS
(2022)
Article
Computer Science, Artificial Intelligence
Qulin Tan et al.
Summary: This study proposes a research on the application of GIS-based artificial neural network models in geological disaster risk zoning, constructing a hierarchical model using the analytic hierarchy process and combining artificial neural networks with BP neural networks for prediction. Through simulation experiments in the low-mountain and hilly areas in Wanli District, Nanchang, the method predicted a high proportion of high and medium-risk areas with results consistent with actual disaster occurrences.
NEURAL COMPUTING & APPLICATIONS
(2021)
Review
Geography, Physical
Teja Kattenborn et al.
Summary: Identifying and characterizing vascular plants in time and space is essential for various disciplines such as forestry, conservation, and agriculture. Remote sensing technology, specifically utilizing deep learning methods like Convolutional Neural Networks (CNN), is crucial in improving efficiency and accuracy in extracting vegetation properties from remote sensing imagery. The modularity and flexibility of CNN frameworks allow for adaptation to different architectures, particularly for multi-modal or multi-temporal applications, ultimately leading to a new era of vegetation remote sensing.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2021)
Article
Environmental Sciences
Yongxiu Sun et al.
Summary: Disentangling the driving factors of biodiversity is critical for understanding biogeographical patterns of vegetation and ecosystem function. Climate was found to be the main driving force of biodiversity on the Qinghai-Tibet Plateau, with terrain and human activity showing stronger indirect effects on biodiversity. The study contributes to exploring the interactive effects and driving mechanisms of human-natural attributes on biodiversity and provides further effective guidance and support for biodiversity conservation and restoration.
SCIENCE OF THE TOTAL ENVIRONMENT
(2021)
Article
Environmental Sciences
Annan Zhou et al.
Summary: This paper proposes an enhanced double-filter deep residual neural network method for generating high-resolution DEMs, which can better match the original DEMs, reduce MAE, and improve the accuracy of terrain parameters.
Review
Computer Science, Theory & Methods
Laith Alzubaidi et al.
Summary: Deep learning has become the gold standard in the machine learning community, widely used in various domains and capable of learning massive data. Through a comprehensive survey, a better understanding of the most important aspects of deep learning is provided.
JOURNAL OF BIG DATA
(2021)
Article
Computer Science, Information Systems
Di Zhu et al.
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
(2020)
Article
Computer Science, Hardware & Architecture
Ian Goodfellow et al.
COMMUNICATIONS OF THE ACM
(2020)
Article
Computer Science, Information Systems
Yaoshiang Ho et al.
Article
Engineering, Electrical & Electronic
Antonia Creswell et al.
IEEE SIGNAL PROCESSING MAGAZINE
(2018)
Article
Computer Science, Software Engineering
Eric Guerin et al.
ACM TRANSACTIONS ON GRAPHICS
(2017)
Review
Automation & Control Systems
Kunfeng Wang et al.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2017)
Article
Computer Science, Artificial Intelligence
Chao Dong et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2016)
Article
Geography, Physical
Christian Hauck
PERMAFROST AND PERIGLACIAL PROCESSES
(2013)
Article
Environmental Sciences
Aykut Akgun et al.
ENVIRONMENTAL MONITORING AND ASSESSMENT
(2012)
Article
Ecology
Jin Li et al.
ECOLOGICAL INFORMATICS
(2011)
Article
Computer Science, Interdisciplinary Applications
Lanny Lin et al.
COMPUTATIONAL AND MATHEMATICAL ORGANIZATION THEORY
(2010)
Article
Soil Science
Dick J. Brus et al.
