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Design of Internet Opinion Analysis System for Emergencies in Big Data Environment Based on Hadoop Platform 1

作者： Ren, Yongchang Han, Lihua Li, Jun College of Information Science and Technology Bohai University Jinzhou Liaoning China Land Satellite Remote Sensing Application Center Ministry of Natural Resources Beijing China School of Physical Education Bohai University Jinzhou Liaoning China

ISBN: (数字)9789811674693

ISBN: (纸本)9789811674686

Public opinion analysis under the big data environment pays more attention to the collection, storage and cleaning of Internet data, and combines text mining technology to obtain opinion information from a large amount of low-value data. Based on the Hadoop platform, this paper develops the Internet opinion analysis system for emergencies, timely predicts and intervenes in the development trend of Internet opinion, effectively responds to negative opinions, and improves the level of social governance. First, the Hadoop ecosystem architecture consisting of "low-level components, data storage, distributed computing, data analysis and processing, and result output" was designed;then, the JDBC database access and optimization technology was designed;finally, it was designed by data system functions such as "data collection, data storage, data processing, data analysis, and data display". © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Big data

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Dominant Leaf Type Classification Using Sentinel-1 Time Series

Dominant Leaf Type Classification Using Sentinel-1 Time Seri...

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Qian Song Rıdvan Salih Kuzu Xiao Xiang Zhu Chair of Data Science in Earth Observation Technical University of Munich (TUM) Munich Germany Remote Sensing Technology Institute (IMF) German Aerospace Center (DLR) Weßling Germany

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

The classification of dominant leaf types, which distinguishes forests based on their leaf conditions, is beneficial for forest management and policymakers. This paper proposes a model based on U-Net to classify the land into non-tree areas, broadleaf forests, and coniferous forests. The dual-pol Sentinel-1 data from January, May, August, and October of 2018 were stacked as a time series. Due to the class imbalance issue, where the non-tree area category dominates (52.69%) the dataset, the model tends to be biased. Thus, re-weighting is introduced to balance the loss. We tested and compared two types of methods: class-aware and task-aware re-weighting. The results indicate that re-weighting effectively mitigates the class imbalance issue.

关键词： Forests Accuracy Time series analysis Sentinel-1 Geoscience and remote sensing Sensors Task analysis

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Robust Hierarchical Point Matching Between Aerial and Ground Imagery Through Depth Map-Based Partitioned Attention Aggregation

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Photogrammetric Record 2025年第190期40卷

作者： Chen, Min Zhang, Zhanhao Liu, Kai Guo, Wei Fang, Tong Li, Wen Zhu, Qing Ge, Xuming Xu, Bo Hu, Han Gong, Jianhui Rao, Yangli Wang, Yanjun Faculty of Geosciences and Engineering Southwest Jiaotong University Chengdu China China Railway Eryuan Engineering Group CO. LTD. Chengdu China Technology Innovation Center for Remote Sensing Monitoring of Natural Resources in Southwest China Mountains Ministry of Natural Resources Chengdu China Hunan Provincial Key Laboratory of geo-Information Engineering in Surveying Mapping and Remote Sensing Hunan University of Science and Technology Xiangtan China

Reliable feature point matching is a pivotal step in the integrated 3D reconstruction of aerial and ground images. Nonetheless, the substantial viewpoint and scale differences between aerial and ground images introduce significant challenges in feature point matching. To address this issue, this study presents a robust matching method for feature points from these two types of images, employing partitioned attention aggregation along with a coarse-to-fine hierarchical matching approach, enabling efficient matching of large-scale aerial-ground images even with hardware limitations. In the coarse matching phase, region segmentation is carried out on the ground image based on its depth map, which is computed using a monocular depth estimation network. Subsequently, a graph neural network serves as an intermediary for information exchange between features, and partitioned attention aggregation is performed while adhering to the constraints imposed by the segmented image regions. During this process, feature points are grouped to form distinct subgraphs, enhancing the structural similarity between corresponding graphs when there are significant depth and viewpoint changes. This also reduces redundant connections within graph neural networks, leading to efficient feature matching. Corresponding local patches are generated from the full-sized aerial-ground images using the initial matches, and further feature point extraction and matching from these patches yield accurate point matches. The experimental results indicate the proposed method performs better than state-of-the-art matching methods. Specifically, compared with the best-performing method among the comparative ones, our method enhances the average number of correct matches and the average matching precision by approximately 6-fold and 36%, respectively. © 2025 remote sensing and Photogrammetry Society and John Wiley & Sons Ltd.

关键词： Graph neural networks

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MEET: A Million-Scale Dataset for Fine-Grained Geospatial Scene Classification with Zoom-Free remote sensing Imagery

arXiv

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arXiv 2025年

作者： Li, Yansheng Wu, Yuning Cheng, Gong Tao, Chao Dang, Bo Wang, Yu Zhang, Jiahao Zhang, Chuge Liu, Yiting Tang, Xu Ma, Jiayi Zhang, Yongjun Information and computing science from Shandong University Weihai China Pattern recognition and intelligent system from the Huazhong University of Science and Technology Wuhan China Computer science and technology from Wuhan University Wuhan China Xidian University Xi’an China Pattern recognition and intelligent systems from Northwestern Polytechnical University Xi’an China School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan China Institute for Pattern Recognition and Artificial Intelligence Huazhong China University of Science and Technology Remote sensing science and technology from Wuhan University Wuhan China School of Remote Sensing and Information Engineering Wuhan University Wuhan China Electronic circuit and system from Xidian University Xi’an China University of Colorado at Boulder BoulderCO United States Control science and engineering from the Huazhong University of Science and Technology Wuhan China Geodesy and photography from Wuhan University Wuhan China

Accurate fine-grained geospatial scene classification using remote sensing imagery is essential for a wide range of applications. However, existing approaches often rely on manually zooming remote sensing images at different scales to create typical scene samples. This approach fails to adequately support the fixed-resolution image interpretation requirements in real-world scenarios. To address this limitation, we introduce the Million-scale finE-grained geospatial scEne classification dataseT (MEET), which contains over 1.03 million zoom-free remote sensing scene samples, manually annotated into 80 fine-grained categories. In MEET, each scene sample follows a scene-in-scene layout, where the central scene serves as the reference, and auxiliary scenes provide crucial spatial context for fine-grained classification. Moreover, to tackle the emerging challenge of scene-in-scene classification, we present the Context-Aware Transformer (CAT), a model specifically designed for this task, which adaptively fuses spatial context to accurately classify the scene samples. CAT adaptively fuses spatial context to accurately classify the scene samples by learning attentional features that capture the relationships between the center and auxiliary scenes. Based on MEET, we establish a comprehensive benchmark for fine-grained geospatial scene classification, evaluating CAT against 11 competitive baselines. The results demonstrate that CAT significantly outperforms these baselines, achieving a 1.88% higher balanced accuracy (BA) with the Swin-Large backbone, and a notable 7.87% improvement with the Swin-Huge backbone. Further experiments validate the effectiveness of each module in CAT and show the practical applicability of CAT in the urban functional zone mapping. The source code and dataset will be publicly available at https://***/project/***. Copyright © 2025, The Authors. All rights reserved.

关键词： Image classification

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Microstructured Gel Polymer Electrolyte and an Interdigital Electrode-Based Iontronic Barometric Pressure Sensor with High Resolution over a Broad Range

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ACS Applied Materials and Interfaces 2023年第50期15卷 58976-58983页

作者： Liu, Yulu Yang, Xi Li, Jie Zhang, Kaihang Wang, Haobin Zeng, Xiangyu Rajagopalan, Pandey Chu, Fengjian Shuaibu, Nazifi Sani Zhang, Liang Zhang, Lei Luo, Jikui Wang, Xiaozhi College of Information Science & Electronic Engineering Zhejiang University Hangzhou310027 China International Joint Innovation Center Zhejiang University Haining314400 China Hangzhou Institute of Technology Xidian University Hangzhou311200 China Cross College of Elite Program National Cheng Kung University 701 Taiwan Research Center for Humanoid Sensing and Perception Zhejiang Lab Hangzhou311100 China State Key Laboratory of Modern Optical Instrumentation College of Optical Science and Engineering Zhejiang University Hangzhou310027 China

We present a novel iontronic barometric pressure sensor based on a gel polymer electrolyte and interdigital electrodes with a much simpler structure than that of existing devices. By introducing high-density microstructures on the gel polymer electrolyte and one side electrode arrangement configuration, the developed sensor offers high performances with an ultrahigh resolution of 10 Pa, an ultrawide barometric pressure-response range from −92 to 7 kPa, a fast response time of ∼15 ms, and excellent long-term stability. The single pressure sensor is able to detect positive and negative barometric pressures without needing any additional means and can operate as a barometric altimeter with a resolution of about one-floor height. The performances of the sensors significantly surpass those of existing barometric pressure sensors. This work provides a new strategy for making high-performance barometric pressure sensors that are highly sought for commercial applications such as altitude detection, negative pressure ambulance, and consumer electronics. © 2023 American Chemical Society

关键词： Pressure sensors

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High-frequency Water Level Estimation in the Yangtze River from GNSS-Interferometric Reflectometry

High-frequency Water Level Estimation in the Yangtze River f...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Zilong Chen Shuanggen Jin School of Remote Sensing and Geomatics Engineering Nanjing University of Information Science and Technology Nanjing China School of Surveying and Land Information Engineering Henan Polytechnic University Jiaozuo China Shanghai Astronomical Observatory Chinese Academy of Science Shanghai China

ISBN: (数字)9798350375909

ISBN: (纸本)9798350375916

Monitoring the water level is of great importance for flood monitoring, warning, water resource management and ecological protection. Traditional monitoring techniques have some limitation in costs or labors. Currently, Global Navigation Satellite System-Interferometric Reflectometry (GNSS-IR) technology has become a crucial means for water level monitoring. In this paper, the high-frequency water level is estimated using multi-GNSS observations at BADO GNSS station, upstream of the Yangtze River. Firstly, we employed the classic Lomb-Scargle Periodogram method to estimate the water level using the signal-to-noise ratio (SNR) data from different frequency bands of GPS, GLONASS, GALILEO, and BDS systems. Subsequently, we used the weighted iterative least squares method to linearly fit the results from each system and generate water level data at 10-minute intervals. The results show that GLONASS-S1C has the best performance among single-system results. The accuracy of estimated water levels shows a significant enhancement through the combined algorithm. Compared to single-frequency methods, the accuracy of water level estimation is improved by 43%–72%, the RMSE is reduced to 0.06m, and the correlation coefficient reaches 0.99.

关键词： Global navigation satellite system Accuracy Satellites Estimation Reflectometry Rivers Iterative methods

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Cloud-based satellite remote sensing for enhancing seagrass monitoring and ecosystem management

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ISPRS Journal of Photogrammetry and remote sensing 2025年 227卷 508-518页

作者： Luis Lizcano-Sandoval Marcus W. Beck Sheila Scolaro Edward T. Sherwood Frank Muller-Karger Institute for Marine Remote Sensing College of Marine Science University of South Florida St. Petersburg FL 33701 USA Environmental Mapping Spatial Informatics Group Pleasanton CA 94566 USA Tampa Bay Estuary Program St. Petersburg FL 33701 USA

We examined the feasibility of monitoring interannual and intra-annual changes in seagrass extent in Tampa Bay, Florida between 1987 and 2023 using remote sensing with the Landsat 5, Landsat 7, Landsat 8, and Sentinel-2 satellite sensor series. This study filled gaps and extended the time series developed for the period 1990–2021 by Lizcano-Sandoval et al. (2022). Seagrass extent was evaluated for six Tampa Bay segments: Hillsborough Bay (HB), Old Tampa Bay (OTB), Middle Tampa Bay (MTB), Lower Tampa Bay (LTB), Boca Ciega Bay (BCB), Manatee River + Terra Ceia Bay (MRTC). Results were compared with reference data from the biennial mapping program conducted by the Southwest Florida Water Management District (SWFWMD). Overall, seagrass showed long-term increases in extent in Tampa Bay. The highest increasing rates over 1987–2023 were observed in HB (+5.7 % yr −1 ) and OTB (+3.5 % yr −1 ). Smaller increases were observed in BCB (0.9 % yr −1 ). However, in HB and MRTC seagrass extent showed decreases in particular years between 2015 and 2023 (34 % and 11 %, respectively), and in OTB between 2017 and 2023 (41 %). Intra-annual changes in seagrass extent were observed during 2021–2023. Intra-annual coefficient of variation was estimated to be as low as 3 % in BCB and as high as 60 % in HB. Seagrass extent estimated by remote sensing was highly correlated with the reference data (r > 0.74), except in the MRTC segment (r = -0.29). A Google Earth Engine app was developed to allow public access to the temporal and spatial changes of seagrass extent and distribution in Tampa Bay. The results showed that seagrass extent assessments to complement existing field and airborne seagrass monitoring programs are feasible at low cost with public satellite imagery in the cloud.

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Monitoring of Surface Deformation of Transmission Tower Based on SBAS-InSAR technology 5

Monitoring of Surface Deformation of Transmission Tower Base...

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5th IEEE International Conference on Electronics technology, ICET 2022

作者： Geng, Hao Peng, Jing Wen, Gang Zhou, Fangrong Zhe, Meilin Wu, Xingle Dai, Sheng Electric Power Research Institute Joint Laboratory of Power Remote Sensing Technology Yunnan Power Grid Co. Ltd Kunming China Transmission Branch of Yunnan Power Grid Co. Ltd Engineering Department Kunming China Nanjing University of Information Science and Technology School of Remote Sensing and Geomatics Enginerring Nanjing China Ratr Intelligent Co. Ltd Marketing Department Shanghai China

ISBN: (纸本)9781665485081

As an essential part of transmission lines, some transmission towers have to be built on mountains, hills, and other areas prone to surface deformation. Geological hazards caused by surface deformation will threaten the stability of transmission towers and thereby threat the safety of power transmission. This paper focuses on the ground surface deformation problems in the area around the transmission towers in Lincang city. It uses SBAS-InSAR technology to process 70 scenes Sentinel-IA data from January 7, 2019, to May 8, 2021. The results contain the average deformation velocity maps and deformation time series of the surface around the transmission tower during the monitoring period. The consistencies conducted from the SBAS-InSAR results and the optical images prove the reliability of the SBAS-InSAR monitoring results. Based on the SBAS-InSAR monitoring results, the cumulative deformation of the foundation surface of six transmission towers reached 40mm with different deformation processes, posing a threat to the safe operation of the electric power grid. In conclusion, the research results of this paper provide a reference for geological hazard warnings and site selection of transmission towers. © 2022 IEEE.

关键词： Site selection

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Urban Land Cover Classification from Sentinel-2 Images with Quantum-Classical Network

Urban Land Cover Classification from Sentinel-2 Images with ...

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Urban remote sensing Joint Event

作者： Fan Fan Yilei Shi Xiao Xiang Zhu Data Science in Earth Observation Technical University of Munich (TUM) Munich Germany Remote Sensing Technology Technical University of Munich (TUM) Munich Germany

Exploiting deep learning techniques to automatically analyze multi-spectral remote sensing imagery plays an essential role in urban land cover and land use classification. However, the computation power required to analyze large earth observation data with complex machine learning models for this task becomes an intractable bottleneck. Leveraging quantum computing might tackle this challenge. In this paper, we present two hybrid quantum-classical deep learning frameworks. They both exploit quantum computing to extract features from multi-spectral images efficiently and classical computing for final classification. The effectiveness of our models is verified with the LCZ42 dataset through the TensorFlow Quantum platform.

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Investigating the structure under the Pingting Terrace from the co-seismic surface rupture of the 2022 Guanshan earthquake

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Terrestrial, Atmospheric and Oceanic sciences 2024年第1期35卷 1-18页

作者： Hsu, Yi-Chun Chang, Chung-Pai Yen, I-Chin Huang, Shao-Yi Yen, Jiun-Yee Center for Space and Remote Sensing Research National Central University Taoyuan City Taiwan Department of Earth Sciences National Central University Taoyuan City Taiwan YIC Geological Office Taipei City Taiwan Science and Technology Research Institute for DE-Carbonization Unit National Taiwan University Taipei City Taiwan Department of Natural Resources and Environmental Studies National Dong Hwa University Hualien City Taiwan

On September 17th and 18th, 2022, shallow earthquakes with magnitudes of 6.6 (ML) and 6.8 (ML) occurred in the eastern Taiwan Longitudinal Valley, which marks the collision zone between the Philippine Sea plate and th... 详细信息

On September 17th and 18th, 2022, shallow earthquakes with magnitudes of 6.6 (M_L) and 6.8 (M_L) occurred in the eastern Taiwan Longitudinal Valley, which marks the collision zone between the Philippine Sea plate and the Eurasian plate, and led to noticeable surface deformation and ruptures within 70 km. This study primarily focuses on the southernmost section of the rupture zone—the Pingting Terrace. Surface rupture locations and behaviors correspond to changes in topography, providing mutual confirmation that the deformation behavior of Pingting Terrace is complex. Based on the distribution of surface ruptures and topography changes, this study roughly divides the Pingting Terrace into northern and southern segments, using the central concave feature as a boundary. The Riedel shear model analysis results show that the principal shear directions in the northern and southern segments are N–S trending and azimuth 20°, respectively. The maximum principal stress orientations are around 135° for the northern and 155° for the southern segments. These findings align with the fault mechanical investigation of the Lichi Mélange in the northern Muken River area of the Pingting Terrace. This suggests spatial changes in shear zone orientations within the Lichi Mélange, which contribute to developing pressure ridges due to transpressional forces. As a result, the Pingting Terrace experiences rapid uplift, causing the Luliao River to migrate southward into the Beinan River, while the eastern Beinan River turns to the eastward edge of the Pingting Terrace. © The Author(s) 2024.

关键词： 2022 Guanshan earthquake Co-seismic surface rupture Flower structure Lichi Mélange Pingting Terrace

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