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Spatio-Temporal neighbors adaptive learning with two-point differences for ocean subsurface temperature reconstruction from 1960 to 2022

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International Journal of Digital Earth 2025年第1期18卷

作者： Wang, An Su, Hua Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education College of Computer and Data Science Fuzhou University Fuzhou China Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education The Academy of Digital China Fuzhou University Fuzhou China National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology Fuzhou University Fuzhou China Department of Geography Ghent University Ghent Belgium

Long time series and accurate subsurface temperature data in the global ocean are essential for ocean warming and climate change studies. The sparse in situ observations in the pre-Argo era hinder the reconstruction of long-time series observational data for the global ocean. This study proposes a novel Adaptive Spatio-TEmporal Neighbors with two-point differences (ASTEN) method for subsurface temperature reconstruction, which adaptively learns and adjusts spatio-temporal neighbors depending on the distribution of in situ observations to ensure robust gaps-filling performance across four dimensions. By integrating geoscience domain knowledge and utilizing spatiotemporal autocorrelation, ASTEN simultaneously learns the spatial pattern and temporal variation of subsurface temperature, and significantly enhances the interpretability and accuracy of ocean temperature reconstructions over a long time series compared to the DINCAE and DINEOF. The ASTEN reconstructed temperature data for the upper 1000 m from 1960 to 2022 can effectively track the ocean warming process for more than six decades. This study demonstrates the ASTEN method is well suited for subsurface temperature reconstruction, and holds great potential in the gaps-filling of sparse ocean observations with high missing rates over a large scale. The new reconstruction of subsurface temperature can effectively reduce the uncertainty of subsurface ocean warming analysis. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Global warming

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Counter-Intuitive Effect of Null Hypothesis on Moran’s I Tests Under Heterogenous Populations 12

Counter-Intuitive Effect of Null Hypothesis on Moran’s I Te...

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12th International Conference on Geographic Information science, GIscience 2023

作者： Nishi, Hayato Yamada, Ikuho Graduate School of Social Data Science Hitotsubashi University Tokyo Japan Center for Spatial Information Science The University of Tokyo Japan

ISBN: (纸本)9783959772884

We examine the effect of null hypothesis on spatial autocorrelation tests using Moran’s I statistic. There are two possible variable states that do not exhibit spatial autocorrelation. One is that they have the same average values in all small regions, and the other is that they are not the same, but their variations are spatially random. The second state is less restrictive than the first. Thus, it intuitively appears suitable for the null hypothesis of Moran’s I test. However, we found that it can make false discoveries more frequently than the nominal rate of the test when the first state is the true data generation process. © Hayato Nishi and Ikuho Yamada.

关键词： Autocorrelation

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Utilize cloud computing to support dust storm forecasting

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International Journal of Digital Earth 2013年第4期6卷 338-355页

作者： Qunying Huang Chaowei Yang Karl Benedict Songqing Chen Abdelmounaam Rezgui Jibo Xie Joint Center for Intelligent Spatial Computing Geography and Geoinformation ScienceGeorge Mason UniversityFairfaxVAUSA Earth Data Analysis Center(EDAC) University of New MexicoAlbuquerqueNMUSA Department of Computer Science George Mason UniversityFairfaxVAUSA Center for Earth Observation and Digital Earth BeijingChina

The simulations and potential forecasting of dust storms are of significant interest to public health and environment *** storms have interannual variabilities and are typical disruptive *** computing platform for a dust storm forecasting operational system should support a disruptive fashion by scaling up to enable high-resolution forecasting and massive public access when dust storms come and scaling down when no dust storm events occur to save energy and *** the capability of providing a large,elastic,and virtualized pool of computational resources,cloud computing becomes a new and advantageous computing paradigm to resolve scientific problems traditionally requiring a large-scale and high-performance *** paper examines the viability for cloud computing to support dust storm *** a holistic study by systematically comparing cloud computing using Amazon EC2 to traditional high performance computing(HPC)cluster,we find that cloud computing is emerging as a credible solution for(1)supporting dust storm forecasting in spinning off a large group of computing resources in a few minutes to satisfy the disruptive computing requirements of dust storm forecasting,(2)performing high-resolution dust storm forecasting when required,(3)supporting concurrent computing requirements,(4)supporting real dust storm event forecasting for a large geographic domain by using recent dust storm event in Phoniex,05 July 2011 as example,and(5)reducing cost by maintaining low computing support when there is no dust storm events while invoking a large amount of computing resource to perform high-resolution forecasting and responding to large amount of concurrent public accesses.

关键词： spatial cloud computing CyberGIS cloud GIS loosely coupled nested model Amazon EC2

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Evaluation of lahar flood hazard management policy using participatory planning in Putih watershed, Magelang regency, central java, Indonesia 3

Evaluation of lahar flood hazard management policy using par...

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3rd International Geography Seminar 2019, IGEOS 2019

作者： Permatasari, A.L. Suherningtyas, I.A. Wiguna, P.P.K. Faculty of Science and Technology Universitas Amikom Yogyakarta Indonesia Center for Spatial Data Infrastructure Development Universitas Udayana Bali Indonesia

The occurrence of Lahar floods after Merapi Volcano eruption in 2010 still continue to be a concern for the government and the community. Kali Putih is one of the rivers that originates in Merapi Volcano and is one of the Lahar flood hazard area. Non-structural mitigation efforts have been carried out by the government. Until now, there are still community activities in the forbidden hazard area. Regulations regarding to spatial planning regulation in the hazard area need to be evaluated. The aim of the study is to evaluate the Lahar flood hazard mitigation based on participatory mapping and Focus Group Discussions (FGD). The research area is Jumoyo Village, which is the highest Lahar hazard flood area. The results indicate that there are still residential areas and community activities in the Lahar flood hazard area. The community does not want to lose their livelihood or relocation of their homes. The community expect that the government is able to revive the economic activities of the community through the establishment of new economic centres without relocation of their homes. The community also expect that the government able to creating new employment. © Published under licence by IOP Publishing Ltd.

关键词： Floods

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RNN-Based Demand Awareness in Smart Library Using CRFID

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China Communications 2020年第5期17卷 284-294页

作者： Ruiqin Bai Jumin Zhao Dengao Li Xiaoyu Lv Qiang Wang Biaokai Zhu College of Information and Computer Taiyuan University of TechnologyJinzhong 030600China College of Data Science Taiyuan University of TechnologyJinzhong 030600China Technology Research Center of Spatial Information Network Engineering of Shanxi Jinzhong 030600China

To provide more intelligence service in the smart library, we need to better perceive the reader’s preferences. In addition to perceiving online records based on readers’ search history and borrowing records, advanced information technologies give us more chance to perceive the behavior of readers in the actual reading process and further discover the need for reading. In this paper, we use CRFID and RNN deep learning network to recognize book motions in the reading process, so as to judge readers’ need degree for the book, which can provide a basis for library book purchases and readers personalized service. In order to improve the recognition accuracy, we use the RSS as well as acceleration magnitude gathered from CRFID as the input data for RNN, and design a new encoding scheme. We trained and tested the deep learning network using real-world data, recorded during actual reading in our lab environment which mimics a typical reading room, from the experimental results, we conclude that our approach is feasible to recognize different reading phase to perceiving the needs of the readers.

关键词： demand awareness detection of phases CRFID RNN smart library

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Transition analysis of regional characteristics using building geo big data and national census data throughout Japan ∼focusing on compact city, re-urbanization, and suburban sprawl∼ 14

Transition analysis of regional characteristics using buildi...

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14th International Conference on Computers in Urban Planning and Urban Management, CUPUM 2015

作者： Akiyama, Yuki Shibasaki, Ryosuke Earth Observation Data Integration and Fusion Research Institute University of Tokyo Japan Center for Spatial Information Science University of Tokyo Japan

ISBN: (纸本)9780692474341

This study attempts to develop a method to understand situations of com-pact city, re-urbanization, and sprawl in all major Japanese cities using building geo big data and national census data for the whole of Japan. In this study we first developed a 500m square grid (TGD) that can monitor the time-series transition of the resident population, employees, building area, and building volume to the aggregation of building geo big data, population census, and economic census by grid polygons. Second, re-gional characteristics of mass grids were evaluated by x-means clustering based on the attributes of each grid. Finally, the development status of ur-banization promotion areas (UPA) and urbanization control areas (UCA) in Japanese 744 cities were elucidated by integration of TGD with the UPA and UCA polygon data. The situations of compact city, re-urbanization, and sprawl of all 744 cities can be monitored by combing the UPA and UCA development statuses.

关键词： Big data

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GPMBC-FZ: A Query Optimization Method in Distributed database 6

GPMBC-FZ: A Query Optimization Method in Distributed Databas...

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6th International Conference on Next Generation data-Driven Networks, NGDN 2024

作者： Du, Yan Ding, Zhiming Faculty of Information Technology Beijing University of Technology Beijing China Center of Spatial-Temporal Data Management and Data Sciecne Institute of Software Chinese Academy of Science Beijing China

ISBN: (纸本)9798350388374

The rapid growth in the scale of distributed databases has led to a deeper impact of the multi-join query optimization problem on data query efficiency. The increase in the number of relations causes a significant increase in the search space complexity of distributed database multi-join optimization, which greatly affects the efficiency of finding the optimal execution plan, thus hindering the further optimization of multi-join queries. In this regard, a genetic multiple bee colony algorithm combined with fuzzy logic is proposed to solve the problem of further optimizing multi-join queries of distributed databases. Firstly, the fast convergence property of the genetic algorithm is utilized to obtain a set of relatively optimal solutions in order to overcome the blindness of the artificial bee algorithm's pre-search, and then the parallel multiple bee algorithm is introduced, which makes use of the information interaction between multiple bee colonies to better prevent the algorithm from falling into the local optimal situation as well as the parallelization of the execution of the multiple bee colonies to improve the search efficiency and convergence speed of the algorithm. Secondly, the fuzzy logic system is also used to optimize the parameters of the algorithm and set the corresponding algorithmic control parameters for different dimensional queries. Finally, a comparison experiment is conducted to test the average execution cost and algorithm execution time of the Top-K query plans output by the algorithm in the case of six query statements involving different lengths, and the experimental results show that the K optimal query plans generated by the method proposed in this paper have lower query excution cost and algorithm execution time, and can effectively improve the query efficiency. © 2024 IEEE.

关键词： Genetic algorithms

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A multistage method for road extraction from optical remotely sensed imagery

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Journal of Information Hiding and Multimedia Signal Processing 2016年第2期7卷 438-447页

作者： Xu, Rui Key Lab of Spatial Data Mining and Information Sharing of Ministry of Education Spatial Information Research Center of Fujian Province Fuzhou University No.523 Gongye Road Fuzhou350002 China College of Information Science and Engineering Fujian University of Technology No.3 Xueyuan Road University Town Fuzhou350108 China

This paper presents a multistage framework for road extraction from remotely sensed imagery. The framework is a combination of multifeature-based mean shift, SVM classifier, and shape feature filter. Mean shift is first employed to cluster the remotely sensed images in the fusion of spectral-texture features and then a classifier of SVM is applied to classify the segmented images into two classes: the road class and the non-road class. Finally, the road class is refined by using road shape features. The experimental results show that this method is efficient in extracting road from remotely sensed imary. © 2016.

关键词： Classification (of information)

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Spatiotemporal database of US congressional elections, 1896-2014

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Scientific data 2017年第1期4卷 170108-170108页

作者： Wolf, Levi John Spatial Analysis Research Center School of Geographical Sciences and Urban Planning Arizona State University. 975 S. Myrtle Avenue Tempe 85281 AZ United States Center for Spatial Data Science University of Chicago. 5737 S. Ellis Ave Chicago 60630 IL United States

High-quality historical data about US Congressional elections has long provided common ground for electoral studies. However, advances in geographic information science have recently made it efficient to compile, distribute, and analyze large spatio-temporal data sets on the structure of US Congressional districts. A single spatio-temporal data set that relates US Congressional election results to the spatial extent of the constituencies has not yet been developed. To address this, existing high-quality data sets of elections returns were combined with a spatiotemporal data set on Congressional district boundaries to generate a new spatio-temporal database of US Congressional election results that are explicitly linked to the geospatial data about the districts themselves.

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Adaptive disentangled target representation for unsupervised domain adaptation in remote sensing segmentation

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Engineering Applications of Artificial Intelligence 2025年 156卷

作者： Runuo Lu Shoubin Dong Jianxin Jia Xusheng Wang Kai Liu Jinsong Chen Shanxin Guo Xiaorou Zheng Guangdong Provincial Key Laboratory of Multimodal Big Data Intelligent Analysis School of Computer Science and Engineering South China University of Technology Guangzhou 510641 China Department of Photogrammetry and Remote Sensing Finnish Geospatial Research Institute Espoo FI-02150 Finland Center for Geo-Spatial Information Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China Shenzhen Engineering Laboratory of Ocean Environmental Big Data Analysis and Application Shenzhen 518055 China

Due to the significant differences between the source and target domains, semantic segmentation models for remote sensing images trained on the source domain often struggle to generalize effectively to new target domains, leading to performance degradation. Most solutions disentangle features into domain-invariant features shared by both domains and domain-specific features unique to each domain, relying primarily on the domain-invariant features for model construction and inference. However, during this process, critical domain-specific features as essential information are often discarded as noise, leading to a reduction in segmentation accuracy. To address these issues, this study proposes an unsupervised domain adaptation, called ChameleonRS 1 . ChameleonRS separates the domain-invariant features and the domain-specific features by disentanglement learning, and introduces a multi-level feature alignment module to enhance the representation of domain-invariant features. More importantly, it aligns predictions based on domain-invariant features with those based on fused invariant and specific features using contrastive learning, effectively capturing target-specific features. This study explores two cross-domain semantic segmentation tasks: (I) Geographic location variation. (II) Geographic location and imaging mode variation. Results show that under setting (I), ChameleonRS outperforms the state-of-the-art method by 4.79% in Intersection over Union (IoU) and 6.51% in the F1 score. Under setting (II), it achieves improvements of 4.46% and 4.82% in IoU and F1 score. Results demonstrate the superiority and adaptability of ChameleonRS under varying geographical and imaging conditions, effectively mitigating the generalization issues caused by distribution differences, and thereby promoting the development and application of remote sensing technologies in practical scenarios.

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