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Extent and spatial distribution of terrace abandonment in China

Journal of Geographical Sciences 2023年第7期33卷 1361-1376页

作者： DONG Shijie XIN Liangjie LI Shengfa XIE Hualin ZHAO Yuluan WANG Xue LI Xiubin SONG Hengfei LU Yahan Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources ResearchCASBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System/Guangdong Open Laboratory of Geospatial Information Technology and Application Guangzhou Institute of GeographyGuangdong Academy of SciencesGuangzhou 510070China Institute of Ecological Civilization Jiangxi University of Finance and EconomicsNanchang 330013China College of Geography and Environmental Science Guizhou Normal UniversityGuiyang 550001China

Driven by urbanization and industrialization,arable land in hilly and mountainous regions of China is gradually becoming marginalized,with the extent of arable land abandonment rapidly expanding from poor-quality sloping arable land to high-quality *** abandonment of large-scale terraces will lead to a series of socio-economic and ecological effects.A national sample survey was used to investigate the extent and spatial distribution of terrace abandonment in China,and a total of 560 valid village questionnaires from 329 counties were collected in the mountainous areas of *** main findings are as follows:(1)The phenomenon of terrace abandonment was widespread throughout the country,with 54% of the total surveyed villages exhibiting terrace abandonment,and the area of abandoned terraces accounting for 9.79% of the total.(2) The degree of terrace abandonment is high in the south and low in the *** most serious region with abandonment was the hilly and mountainous areas in the south,especially in the middle and lower Yangtze River region.(3)The main driving factors of terrace abandonment were rural labor migration,agricultural mechanization level,irrigation conditions,and transportation conditions for *** measures should be taken based on the specific conditions of each area to alleviate terrace *** such as improving terrace mechanization are universally ***,low-quality terraces can be withdrawn orderly,and for high-quality terraces,multiple measures are needed to consolidate agricultural production,such as adjusting the planting structure,strengthening agricultural infrastructure construction,and encouraging the transfer of land-use rights as well as large-scale operation.

关键词： terraces arable land abandonment national sample survey spatial variation China

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Detection of manganese nodule ore based on underwater hyperspectral imaging technology 6

Detection of manganese nodule ore based on underwater hypers...

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6th Conference on Frontiers in Optical Imaging and technology: applications of Imaging Technologies

作者： Shen, Mengling Men, Shaojie Liu, Bohan Li, Dewei Liu, Zhaojun School of Information Science and Engineering Shandong University China Key Laboratory of Laser and Infrared System Integration Technology Ministry of Education China Shandong Provincial Key Laboratory of Laser Technology and Application China National Deep Sea Center China

ISBN: (数字)9781510679757

ISBN: (纸本)9781510679740

Manganese nodules, widely distributed across the deep-sea floor, are emerging as a significant potential mineral resource. Addressing the need for improved exploration and classification methods, this study explores the application of underwater hyperspectral imaging technology for the detection and classification of manganese nodule ore and other rock types. The system, operating within a spectral range of 400 - 1000 nm and achieving a spectral resolution of less than 5 nm, captures the spectral characteristics and spatial information of manganese nodule ores. Different classifiers, including Spectral Angle Mapper (SAM), Support Vector Machine (SVM), and Convolutional Neural Networks (2D-CNN and 3D-CNN) to analyze the spectral data. Our results indicate that the four classifiers can effectively achieve ore classification, and CNN-based classifiers significantly outperform traditional SAM and SVM methods. The 2D-CNN model achieved the highest OA at 92.27%, closely followed by the 3D-CNN model at 91.77%. Our findings demonstrate the potential of underwater hyperspectral imaging combined with advanced machine learning techniques in marine mineral detection and environmental monitoring. © 2024 SPIE.

关键词： Support vector machines

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Extracting Named Entity Using Entity Labeling in Geological Text Using Deep Learning Approach

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Journal of Earth Science 2023年第5期34卷 1406-1417页

作者： Qinjun Qiu Miao Tian Zhong Xie Yongjian Tan Kai Ma Qingfang Wang Shengyong Pan Liufeng Tao State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR Chinese Academy of Surveying and MappingBeijing 100036China Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities MNRShanghai 20063China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang 443002China College of Computer and Information Technology China Three Gorges UniversityYichang 443002China Chengdu Geological Environment Monitoring Station Chengdu 610036China Wuhan Zondy Cyber Science&Technology Co. Ltd.Wuhan 430074China

Artificial intelligence(AI) is the key to mining and enhancing the value of big data, and knowledge graph is one of the important cornerstones of artificial intelligence, which is the core foundation for the integration of statistical and physical representations. Named entity recognition is a fundamental research task for building knowledge graphs, which needs to be supported by a high-quality corpus, and currently there is a lack of high-quality named entity recognition corpus in the field of geology, especially in Chinese. In this paper, based on the conceptual structure of geological ontology and the analysis of the characteristics of geological texts, a classification system of geological named entity types is designed with the guidance and participation of geological experts, a corresponding annotation specification is formulated, an annotation tool is developed, and the first named entity recognition corpus for the geological domain is annotated based on real geological reports. The total number of words annotated was 698 512 and the number of entities was 23 345. The paper also explores the feasibility of a model pre-annotation strategy and presents a statistical analysis of the distribution of technical and term categories across genres and the consistency of corpus annotation. Based on this corpus, a Lite Bidirectional Encoder Representations from Transformers(ALBERT)-Bi-directional Long Short-Term Memory(BiLSTM)-Conditional Random Fields(CRF) and ALBERT-BiLSTM models are selected for experiments, and the results show that the F1-scores of the recognition performance of the two models reach 0.75 and 0.65 respectively, providing a corpus basis and technical support for information extraction in the field of geology.

关键词： ontology geological reports named entity recognition geological corpus construction semi-automated annotation platforms deep learning

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Debris flow susceptibility mapping in mountainous area based on multi-source data fusion and CNN model-taking Nujiang Prefecture,China as an example

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International Journal of Digital Earth 2022年第1期15卷 1966-1988页

作者： Fanshu Xu Baoyun Wang School of Information Science and Technology Yunnan Normal UniversityKunmingPeople’s Republic of China School of Mathematic Yunnan Normal UniversityKunmingPeople’s Republic of China Key Laboratory of Complex System Modelling and Application Yunnan Normal UniversityKunmingPeople’s Republic of China

Efforts to evaluate the susceptibility of debris flows in large areas,especially in mountainous regions,are often hampered by the alpine and canyon *** paper proposes a convolution neural network(CNN)model named dense residual shuffle net(DRSNet).It is successfully applied to Nujiang Prefecture in Yunnan Province of China,a typical alpine area with frequent debris *** uses digital elevation model,remote sensing,lithology,soil type and precipitation data as ***,dense connection and residual structure were used to extract the shallow features of various ***,channel shuffle,fuse block and fully connection were applied to strengthen the correlation between different shallow features and give inner danger ***,precipitation as the activation factor was introduced giving the valleys *** verify the feasibility of DRSNet,comparative tests were conducted on 7 CNN models and 3 other machine learning(ML)*** results show that DRSNet can achieve 78.6%accuracy in debris flow valley classification,which is at least 7.4%higher than common CNN models and 15.2%higher than other ML *** article provides new ideas for debris flow susceptibility evaluation.

关键词： Debris flow disaster prediction convolutional neural networks mountainous valleys

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Investigations of the dynamics of road pavement reflectance

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Chinese Optics Letters 2012年第4期10卷 58-62页

作者：姜亢于凯赵永超 Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Institute of ElectronicsChinese Academy of Sciences Graduate University of Chinese Academy of Sciences

Road pavement reflectance is usually assumed to be invariant in short periods of time in some quantitative remote sensing *** examine its variability,reflectance sequences of concrete and asphalt pavement are measured in field for half a day in visible and near-infarecd（VNIR）spectral range using dual-beam *** much as 20.7%and 3.52%of relative changes are found in asphalt and concrete reflectance data at 550 nm,and all VNIR bands demonstrate similar variations found to correlate with both illumination geometry and the relative portion of diffuse *** this letter,this effect is interpreted from a mathematic *** studies are needed to model the dynamics of reflectance physically.

关键词： Dynamics Remote sensing Roads and streets

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Deep supervised qantization by self-organizing map 25

Deep supervised qantization by self-organizing map

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25th ACM International Conference on Multimedia, MM 2017

作者： Wang, Min Zhou, Wengang Tian, Qi Pu, Junfu Li, Houqiang CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China China Computer Science Department University of Texas San Antonio United States

ISBN: (纸本)9781450349062

Approximate Nearest Neighbour (ANN) search is an important research topic in multimedia and computer vision fields. In this paper, we propose a new deep supervised quantization method by Self-Organizing Map (SOM) to address this problem. Our method integrates the Convolutional Neural Networks (CNN) and Self-Organizing Map into a unified deep architecture. The overall training objective includes supervised quantization loss and classification loss. With the supervised quantization loss, we minimize the differences on the maps between similar image pairs, and maximize the differences on the maps between dissimilar image pairs. By optimization, the deep architecture can simultaneously extract deep features and quantize the features into the suitable nodes in the Self-Organizing Map. The experiments on several public standard datasets prove the superiority of our approach over the existing ANN search methods. Besides, as a byproduct, our deep architecture can be directly applied to classification task and visualization with little modification, and promising performances are demonstrated on these tasks in the experiments. © 2017 ACM.

关键词： Self organizing maps

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A Novel Gradient Descent Least-Squares (GDLSs) Algorithm for Efficient Gridless Line Spectrum Estimation With applications in Tomographic SAR Imaging

A Novel Gradient Descent Least-Squares (GDLSs) Algorithm for...

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作者： Shi, Ruizhe Zhang, Zhe Qiu, Xiaolan Ding, Chibiao University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100190 China Suzhou Aerospace Information Research Institute Suzhou Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou Jiangsu215123 China CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Beijing100190 China

This article presents a novel efficient method for gridless line spectrum estimation problems with single snapshot and sparse signals, namely the gradient descent least-squares (GDLS) method. Conventional single-snapshot (a.k.a. single-measure vector or SMV) line spectrum estimation methods either rely on smoothing techniques that sacrifice the range and/or azimuth resolution or adopt the sparsity constraint and utilize the compressed sensing (CS) method by defining prior grids and resulting in the off-grid problem. Recently emerged atomic norm minimization (ANM) methods achieved gridless SMV line spectrum estimation, but its computational complexity is extremely high;thus, it is practically infeasible in real applications with large problem scales. Our proposed GDLS method reformulates the line spectrum estimations problem into a least-squares (LSs) estimation problem and solves the corresponding objective function via a gradient descent algorithm in an iterative fashion with efficiency. The convergence guarantee, computational complexity, and performance analysis for evenly distributed antenna array cases are discussed in this article. Numerical simulations show that the proposed GDLS algorithm outperforms the state-of-the-art methods, for example, CS and ANM, in terms of estimation performances. It can completely avoid the off-grid problem, and its computational complexity is significantly lower than ANM. Our method has been tested in tomographic SAR (TomoSAR) imaging applications via simulated and real experimental data. Results show the great potential of the proposed method in terms of better cloud point performance and eliminating the gridding effect. © 1980-2012 IEEE.

关键词： Spectrum analysis

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Linear distance preserving pseudo-supervised and unsupervised hashing 16

Linear distance preserving pseudo-supervised and unsupervise...

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24th ACM Multimedia Conference, MM 2016

作者： Wang, Min Zhou, Wengang Tian, Qi Zha, Zhengjun Li, Houqiang CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China China Computer Science Department University of Texas at San Antonio United States

ISBN: (纸本)9781450336031

With the advantage in compact representation and efficient comparison, binary hashing has been extensively investigated for approximate nearest neighbor search. In this paper, we propose a novel and general hashing framework, which simultaneously considers a new linear pair-wise distance preserving objective and point-wise constraint. The direct distance preserving objective aims to keep the linear relationships between the Euclidean distance and the Hamming distance of data points. Based on different pointwise constraints, we propose two methods to instantiate this framework. The first one is a pseudo-supervised hashing method, which uses existing unsupervised hashing methods to generate binary codes as pseudo-supervised information. The second one is an unsupervised hashing method, in which quantization loss is considered. We validate our framework on two large-scale datasets. The experiments demonstrate that our pseudo-supervised method achieves consistent improvement for the state-of-the-art unsupervised hashing methods, while our unsupervised method outperforms the state-of-the-art methods. ©2016 ACM.

关键词： Hamming distance

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An automatic method on detecting image control points from SAR imagery based on optical image patches

An automatic method on detecting image control points from S...

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2009 Joint Urban Remote Sensing Event

作者： Hui, Long Kun, Fu Chuanzhao, Han Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing China Beijing Remote Sensing Institute Beijing China

ISBN: (纸本)9781424434619

In this paper, we propose a novel procedure of detecting control points from SAR image based on Optical Image Patches procedure. The method presented here is a three-step procedure. In the first stage, a local image patch from the SAR image is estimated from the geo-information of the SAR image and the GCP geo-coordinate, and the local patch is performed an affine transformation by using the four geo-coordinates of the SAR image in advance. In the second stage, the NCC procedure is performed on the GCP and the preprocessed local SAR image patch to find the candidate image control point(ICP). In the third stage, the pairs of ICP and GCP are refined, and the false pairs are discarded by the RANSAC algorithm. Experiments on SAR-Optical satellite images demonstrate the reliability and effectiveness of the presented method. ©2009 IEEE.

关键词： Synthetic aperture radar

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REVERSE-RANGE-DOPPLER METHOD FOR AUTOMATED GEOCODING SAR IMAGES

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Journal of Electronics(China) 2012年第3期29卷 242-247页

作者： Liu Jiayin Hong Wen Key Laboratory of Technology on Geo-spatial Information Processing and Application System Chinese Academy of SciencesBeijing 100190China National Key Laboratory of Microwave Imaging Technology Beijing 100190China Institute of Electronics Chinese Academy of SciencesBeijing 1001q0China

Range-Doppler (RD) method and Reverse-Range-Doppler (RRD) method are combined together to achieve automatic geocoding of Synthetic Aperture Radar (SAR) image quickly and accurately in the paper. The RD method is firstly used to locate the four corners of the image, then the other pixels of the image can be located by Reverse-Range-Doppler (RRD) method. Resampling is performed at last. The approach has an advantage over previous techniques in that it does not require ground control points and is independent of spacecraft attitude knowledge or control. It can compensate the shift due to the assumed Doppler frequency in SAR image preprocessing. RRD simplifies the process of RD, therefore speeds up the computation. The experimental results show that a SAR image can be automated geocoded in 30 s using the single CPU (3 GHz) with 1 G memory and an accuracy of 10 m is attainable with this method.

关键词： Range-Doppler (RD) Reverse-Range-Doppler (RRD) Geolocation Automated geocoding Space-borne Synthetic Aperture Radar (SAR)

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