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  1. Article ; Online: Flood Detection Using Multiple Chinese Satellite Datasets during 2020 China Summer Floods

    Lianchong Zhang / Junshi Xia

    Remote Sensing, Vol 14, Iss 51, p

    2022  Volume 51

    Abstract: Multiple source satellite datasets, including the Gaofen (GF) series and Zhuhai-1 hyperspectral, are provided to detect and monitor the floods. Considering the complexity of land cover changes within the flooded areas and the different characteristics of ...

    Abstract Multiple source satellite datasets, including the Gaofen (GF) series and Zhuhai-1 hyperspectral, are provided to detect and monitor the floods. Considering the complexity of land cover changes within the flooded areas and the different characteristics of the multi-source remote sensing dataset, we proposed a new coarse-to-fine framework for detecting floods at a large scale. Firstly, the coarse results of the water body were generated by the binary segmentation of GF-3 SAR, the water indexes of GF-1/6 multispectral, and Zhuhai-1 hyperspectral images. Secondly, the fine results were achieved by the deep neural networks with noisy-label learning. More specifically, the Unet with the T-revision is adopted as the noisy label learning method. The results demonstrated the reliability and accuracy of water mapping retrieved by the noisy learning method. Finally, the differences in flooding patterns in different regions were also revealed. We presented examples of Poyang Lake to show the results of our framework. The rapid and robust flood monitoring method proposed is of great practical significance to the dynamic monitoring of flood situations and the quantitative assessment of flood disasters based on multiple Chinese satellite datasets.
    Keywords flood mapping ; multiple-source ; Chinese satellites ; summer flood ; Science ; Q
    Subject code 550
    Language English
    Publishing date 2022-12-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Publishing China satellite data on the GEOSS Platform

    Roberto Roncella / Lianchong Zhang / Enrico Boldrini / Mattia Santoro / Paolo Mazzetti / Stefano Nativi

    Big Earth Data, Vol 7, Iss 2, Pp 398-

    2023  Volume 412

    Abstract: ABSTRACTThis paper is the first of a series that describes some of the main dataset resources presently shared through the GEOSS Platform. The GEOSS Platform has been created to provide the technological tool to implement the Global Earth Observation ... ...

    Abstract ABSTRACTThis paper is the first of a series that describes some of the main dataset resources presently shared through the GEOSS Platform. The GEOSS Platform has been created to provide the technological tool to implement the Global Earth Observation System of Systems (GEOSS); it is a brokering infrastructure that presently brokers more than 190 autonomous data catalogs and information systems. The paper analyses the China Satellite datasets and describes the data publishing process from China GEOSS Data Provider to the GEOSS Platform considering both administrative registration as well as the technical registration. The China Satellite datasets are considered as one of the most important satellite data shared by the GEOSS Platform. The analysis provides some insights as well about GEOSS user searches for China Satellite datasets.
    Keywords GEOSS ; data interoperability ; data sharing ; satellite data ; Earth observation ; Geography. Anthropology. Recreation ; G ; Geology ; QE1-996.5
    Subject code 020
    Language English
    Publishing date 2023-04-01T00:00:00Z
    Publisher Taylor & Francis Group
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: A Precision Evaluation Index System for Remote Sensing Data Sampling Based on Hexagonal Discrete Grids

    Yue Ma / Guoqing Li / Xiaochuang Yao / Qianqian Cao / Long Zhao / Shuang Wang / Lianchong Zhang

    ISPRS International Journal of Geo-Information, Vol 10, Iss 194, p

    2021  Volume 194

    Abstract: With the rapid development of earth observation, satellite navigation, mobile communication, and other technologies, the order of magnitude of the spatial data we acquire and accumulate is increasing, and higher requirements are put forward for the ... ...

    Abstract With the rapid development of earth observation, satellite navigation, mobile communication, and other technologies, the order of magnitude of the spatial data we acquire and accumulate is increasing, and higher requirements are put forward for the application and storage of spatial data. As a new form of data management, the global discrete grid can be used for the efficient storage and application of large-scale global spatial data, which is a digital multiresolution georeference model that helps to establish a new model of data association and fusion. It is expected to make up for the shortcomings in the organization, processing, and application of current spatial data. There are different types of grid systems according to the grid division form, including global discrete grids with equal latitude and longitude, global discrete grids with variable latitude and longitude, and global discrete grids based on regular polyhedrons. However, there is no accuracy evaluation index system for remote sensing images expressed on the global discrete grid to solve this problem. This paper is dedicated to finding a suitable way to express remote sensing data on discrete grids, as well as establishing a suitable accuracy evaluation system for modeling remote sensing data based on hexagonal grids to evaluate modeling accuracy. The results show that this accuracy evaluation method can evaluate and analyze remote sensing data based on hexagonal grids from multiple levels, and the comprehensive similarity coefficient of the images before and after conversion is greater than 98%, which further proves the availability of the hexagonal-grid-based remote sensing data of remote sensing images. This evaluation method is generally applicable to all raster remote sensing images based on hexagonal grids, and it can be used to evaluate the availability of hexagonal grid images.
    Keywords remote sensing ; global discrete grid ; accuracy evaluation ; hexagonal grid ; Geography (General) ; G1-922
    Subject code 518
    Language English
    Publishing date 2021-03-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Improved Method to Detect the Tailings Ponds from Multispectral Remote Sensing Images Based on Faster R-CNN and Transfer Learning

    Dongchuan Yan / Hao Zhang / Guoqing Li / Xiangqiang Li / Hua Lei / Kaixuan Lu / Lianchong Zhang / Fuxiao Zhu

    Remote Sensing, Vol 14, Iss 103, p

    2022  Volume 103

    Abstract: The breaching of tailings pond dams may lead to casualties and environmental pollution; therefore, timely and accurate monitoring is an essential aspect of managing such structures and preventing accidents. Remote sensing technology is suitable for the ... ...

    Abstract The breaching of tailings pond dams may lead to casualties and environmental pollution; therefore, timely and accurate monitoring is an essential aspect of managing such structures and preventing accidents. Remote sensing technology is suitable for the regular extraction and monitoring of tailings pond information. However, traditional remote sensing is inefficient and unsuitable for the frequent extraction of large volumes of highly precise information. Object detection, based on deep learning, provides a solution to this problem. Most remote sensing imagery applications for tailings pond object detection using deep learning are based on computer vision, utilizing the true-color triple-band data of high spatial resolution imagery for information extraction. The advantage of remote sensing image data is their greater number of spectral bands (more than three), providing more abundant spectral information. There is a lack of research on fully harnessing multispectral band information to improve the detection precision of tailings ponds. Accordingly, using a sample dataset of tailings pond satellite images from the Gaofen-1 high-resolution Earth observation satellite, we improved the Faster R-CNN deep learning object detection model by increasing the inputs from three true-color bands to four multispectral bands. Moreover, we used the attention mechanism to recalibrate the input contributions. Subsequently, we used a step-by-step transfer learning method to improve and gradually train our model. The improved model could fully utilize the near-infrared (NIR) band information of the images to improve the precision of tailings pond detection. Compared with that of the three true-color band input models, the tailings pond detection average precision (AP) and recall notably improved in our model, with the AP increasing from 82.3% to 85.9% and recall increasing from 65.4% to 71.9%. This research could serve as a reference for using multispectral band information from remote sensing images in the construction and application of deep learning models.
    Keywords tailings pond ; Faster R-CNN ; transfer learning ; multispectral ; Science ; Q
    Subject code 006
    Language English
    Publishing date 2022-12-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Extracting Disaster-Related Location Information through Social Media to Assist Remote Sensing for Disaster Analysis

    Tengfei Yang / Jibo Xie / Guoqing Li / Lianchong Zhang / Naixia Mou / Huan Wang / Xiaohan Zhang / Xiaodong Wang

    Remote Sensing, Vol 14, Iss 1199, p

    The Case of the Flood Disaster in the Yangtze River Basin in China in 2020

    2022  Volume 1199

    Abstract: Social media texts spontaneously produced and uploaded by the public contain a wealth of disaster information. As a supplementary data source for remote sensing, they have played an important role in disaster reduction and emergency response in recent ... ...

    Abstract Social media texts spontaneously produced and uploaded by the public contain a wealth of disaster information. As a supplementary data source for remote sensing, they have played an important role in disaster reduction and emergency response in recent years. However, social media also has certain flaws, such as insufficient location information, etc. This affects the efficiency of combining these data with remote sensing data. For flood disasters in particular, extensively flooded areas limit the distribution of social media data, which makes it difficult for these data to function as they should. In this paper, we propose a disaster reduction framework to solve these problems. We first used an approach that was based on search engine and lexical rules to automatically extract disaster-related location information from social media texts. Then, we combined the extracted information with the upload location of social media itself to construct location-pointing relationships. These relationships were used to build a new social network, which can be used in combination with remote sensing images for disaster analysis. The analysis integrated the advantages of social media and remote sensing. It can not only provide macro disaster information in the study area but can also assist in evaluating the disaster situation in different flooded areas from the perspective of public observation. In addition, the timeliness of social media data also improved the continuity and situational awareness of flood monitoring. A case study of the flood disaster in the Yangtze River Basin in China in 2020 was used to verify the effectiveness of the method described in this paper.
    Keywords social media ; remote sensing ; information mining ; flood disaster ; disaster reduction ; Science ; Q
    Subject code 710
    Language English
    Publishing date 2022-02-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: GF-1 Satellite Imagery Data Service and Application Based on Open Data Cube

    Qianqian Cao / Guoqing Li / Xiaochuang Yao / Tao Jia / Guojiang Yu / Lianchong Zhang / Dan Xu / Hao Zhang / Xiaojun Shan

    Applied Sciences, Vol 12, Iss 15, p

    2022  Volume 7816

    Abstract: With the application of big data in Earth observation, satellite imagery data are gradually becoming important means of observation for monitoring changes in vegetation, water bodies, and urbanization. Therefore, new satellite imagery data organization ... ...

    Abstract With the application of big data in Earth observation, satellite imagery data are gradually becoming important means of observation for monitoring changes in vegetation, water bodies, and urbanization. Therefore, new satellite imagery data organization and management paradigms are urgently needed to fully mine the useful information from these data and provide new ways to better quantify and serve the sustainable development of resources and the environment. In this paper, a framework for processing and analyzing Chinese GF-1 satellite imagery data was developed using the latest technologies such as Open Data Cube (ODC) grids, Analysis Ready Data (ARD) generation, and space subdivision, which extended the data loading and processing capacities of the ODC grids for Chinese satellite imagery data. Using the proposed framework, we conducted a case study to investigate the spatial and temporal changes in vegetation and water mapping with GF-1 data collected from 2014 to 2021 covering the Miyun Reservoir, Beijing, China. The experimental results showed that the proposed framework had significantly improved temporal and spatial efficiency compared with the traditional scene-based data management approach, thus demonstrating the advantages and potential of the ODC grids as a new data management paradigm.
    Keywords Open Data Cube ; GF-1 data ; water change mapping ; remote sensing data management ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
    Subject code 710
    Language English
    Publishing date 2022-08-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Gap analysis on open data interconnectivity for disaster risk research

    Guoqing Li / Jing Zhao / Virginia Murray / Carol Song / Lianchong Zhang

    Geo-spatial Information Science, Vol 22, Iss 1, Pp 45-

    2019  Volume 58

    Abstract: Open data strategies are being adopted in disaster-related data particularly because of the need to provide information on global targets and indicators for implementation of the Sendai Framework for Disaster Risk Reduction 2015–2030. In all phases of ... ...

    Abstract Open data strategies are being adopted in disaster-related data particularly because of the need to provide information on global targets and indicators for implementation of the Sendai Framework for Disaster Risk Reduction 2015–2030. In all phases of disaster risk management including forecasting, emergency response and post-disaster reconstruction, the need for interconnected multidisciplinary open data for collaborative reporting as well as study and analysis are apparent, in order to determine disaster impact data in timely and reportable manner. The extraordinary progress in computing and information technology in the past decade, such as broad local and wide-area network connectivity (e.g. Internet), high-performance computing, service and cloud computing, big data methods and mobile devices, provides the technical foundation for connecting open data to support disaster risk research. A new generation of disaster data infrastructure based on interconnected open data is evolving rapidly. There are two levels in the conceptual model of Linked Open Data for Global Disaster Risk Research (LODGD) Working Group of the Committee on Data for Science and Technology (CODATA), which is the Committee on Data of the International Council for Science (ICSU): data characterization and data connection. In data characterization, the knowledge about disaster taxonomy and data dependency on disaster events requires specific scientific study as it aims to understand and present the correlation between specific disaster events and scientific data through the integration of literature analysis and semantic knowledge discovery. Data connection concepts deal with technical methods to connect distributed data resources identified by data characterization of disaster type. In the science community, interconnected open data for disaster risk impact assessment are beginning to influence how disaster data are shared, and this will need to extend data coverage and provide better ways of utilizing data across domains where innovation ...
    Keywords Open data ; interconnectivity ; gap analysis ; disaster risk ; Mathematical geography. Cartography ; GA1-1776 ; Geodesy ; QB275-343
    Subject code 710
    Language English
    Publishing date 2019-01-01T00:00:00Z
    Publisher Taylor & Francis Group
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Enabling the Big Earth Observation Data via Cloud Computing and DGGS

    Xiaochuang Yao / Guoqing Li / Junshi Xia / Jin Ben / Qianqian Cao / Long Zhao / Yue Ma / Lianchong Zhang / Dehai Zhu

    Remote Sensing, Vol 12, Iss 1, p

    Opportunities and Challenges

    2019  Volume 62

    Abstract: In the era of big data, the explosive growth of Earth observation data and the rapid advancement in cloud computing technology make the global-oriented spatiotemporal data simulation possible. These dual developments also provide advantageous conditions ... ...

    Abstract In the era of big data, the explosive growth of Earth observation data and the rapid advancement in cloud computing technology make the global-oriented spatiotemporal data simulation possible. These dual developments also provide advantageous conditions for discrete global grid systems (DGGS). DGGS are designed to portray real-world phenomena by providing a spatiotemporal unified framework on a standard discrete geospatial data structure and theoretical support to address the challenges from big data storage, processing, and analysis to visualization and data sharing. In this paper, the trinity of big Earth observation data (BEOD), cloud computing, and DGGS is proposed, and based on this trinity theory, we explore the opportunities and challenges to handle BEOD from two aspects, namely, information technology and unified data framework. Our focus is on how cloud computing and DGGS can provide an excellent solution to enable big Earth observation data. Firstly, we describe the current status and data characteristics of Earth observation data, which indicate the arrival of the era of big data in the Earth observation domain. Subsequently, we review the cloud computing technology and DGGS framework, especially the works and contributions made in the field of BEOD, including spatial cloud computing, mainstream big data platform, DGGS standards, data models, and applications. From the aforementioned views of the general introduction, the research opportunities and challenges are enumerated and discussed, including EO data management, data fusion, and grid encoding, which are concerned with analysis models and processing performance of big Earth observation data with discrete global grid systems in the cloud environment.
    Keywords big earth observation data ; cloud computing ; discrete global grid systems ; Science ; Q
    Subject code 550
    Language English
    Publishing date 2019-12-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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