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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： Astha Sul Sanjay Patil School of Computer Science and Engineering Vellore Institute of Technology Vellore India Maharashtra Remote Sensing Applications Centre Pune Maharashtra

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

Landslides are a common natural disaster that can result in substantial casualties. One of the region’s most vulnerable to landslides in India is Western Ghats. The risk casualty is reduced with the use of the landslide susceptibility map (LSM) and prediction of landslide points. The aim of this research is to explore and compare the 4 ML algorithms (K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR)) for generating the most promising LSM. This study focuses on 12 Landslide conditioning factors (LCFs) such as Elevation, Slope, Lithology, LULC, Distance to Road, Topographic Wetness Index (TWI), Stream Power Index (SPI), Topographic Roughness Index (TRI), Drainage Networks, NDVI, Rainfall, Sediment Transport Index (STI). It has been found that the slope of the ground, the distance to the road, and the annual rainfall all have a significant impact on the occurrence of landslides. The models made using different approaches have been analysed and compared using the following metrics: prediction accuracy, precision, recall, F1-score, and True Skill Statistics (TSS). Overall accuracy for LR, SVM, KNN, RF are 82.30, 84.00, ${8 2. 5 0, 8 7. 2 0}$ respectively. The findings of the study show that there are more landslide-prone locations in Bhor, Velhe, Ambegaon, and Junnar in Pune District, Maharashtra.

关键词： Support vector machines Radio frequency Landslides Accuracy Roads Predictive models Nearest neighbor methods Terrain factors Indexes Geographic information systems

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Soil Moisture Assessment and Drought Monitoring in Arid Environments from GNSS-Reflectometry: A Comparative Analysis between Africa and China

Soil Moisture Assessment and Drought Monitoring in Arid Envi...

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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： El Rhadiouini, Charafa Jin, Shuanggen Nanjing University of Information Science and Technology School of Remote Sensing and Geomatics Engineering Nanjing210044 China Henan Polytechnic University School of Surveying and Land Information Engineering Jiaozuo454003 China Chinese Academy of Sciences Shanghai Astronomical Observatory Shanghai200030 China

ISBN: (纸本)9798350375909

Global Navigation Satellite System-Reflectometry (GNSS-R) has emerged as a promising tool for remote sensing applications, e.g., soil moisture and drought monitoring. In this paper, soil moisture assessment and drought monitoring are conducted in arid regions of Africa and China from GNSS-R, aiming to enhance the accuracy and spatial resolution of soil moisture retrievals through the integration GNSS-R data in with other available observations. Here it employs an integrated approach, utilizing GNSS-R, LiDAR, and Synthetic Aperture Radar (SAR) data to assess soil moisture dynamics in regions facing water scarcity challenges. The study explores the potential of GNSS-R in combination with other sensors, such as Scatterometry and Altimetry, for comprehensive monitoring, comparison, and calibration. Comparison between African and Chinese arid region contributes to a nuanced understanding of soil moisture dynamics in diverse geographical settings. Furthermore, the implications of these insights for geohazard surveillance and the judicious management of water resources in arid landscapes are given and discussed. This study presents a focused inquiry that underscores the capabilities of GNSS-R technology and its potential to proactively address environmental challenges. The comparative analysis of two distinct arid regions provides a unique vantage point, contributing to the broader global initiative aimed at meticulous monitoring and adept management of soil moisture and drought occurrences. © 2024 IEEE.

关键词： Reflectometers

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Robust graph‐based localization for industrial Internet of things in the presence of flipping ambiguities

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CAAI Transactions on Intelligence technology 2023年第4期8卷 1140-1149页

作者： Mian Imtiaz ul Haq Ruhul Amin Khalil Muhannad Almutiry Ahmad Sawalmeh Tanveer Ahmad Nasir Saeed Faculty of Electrical Engineering Swedish College of Engineering and TechnologyWah CanttPakistan Department of Electrical Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawarPakistan Department of Electrical Engineering Remote Sensing UnitNorthern Border UniversityArarSaudi Arabia Data Science and Artificial Intelligence Department‐College of Science and Information Technology Irbid National UniversityIrbadJordan Innovation Education and Research Center for On‐Device AI Software(Bk21) Department of Computer Science and EngineeringChungnam National UniversityDaejeonRepublic of Korea Department of Electrical and Communication Engineering United Arab Emirates University(UAEU)Al AinUnited Arab Emirates

Localisation of machines in harsh Industrial Internet of Things(IIoT)environment is necessary for various ***,a novel localisation algorithm is proposed for noisy range measurements in IIoT *** position of an unknown machine device in the network is estimated using the relative distances between blind machines(BMs)and anchor machines(AMs).Moreover,a more practical and challenging scenario with the erroneous position of AM is considered,which brings additional uncertainty to the final position ***,the AMs selection algorithm for the localisation of BMs in the IIoT network is *** those AMs will participate in the localisation process,which increases the accuracy of the final location ***,the closed‐form expression of the proposed greedy successive anchorization process is derived,which prevents possible local convergence,reduces computation,and achieves Cramér‐Rao lower bound accuracy for white Gaussian measurement *** results are compared with the state‐of‐the‐art and verified through numerous simulations.

关键词： Cramér‐Rao lower bound greedy successive anchorization industrial internet of things localization

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Low-Complexity Reconfigurable Computing Based Online One-Class Classification Using High-Resolution Hyperspectral Imagery

Low-Complexity Reconfigurable Computing Based Online One-Cla...

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2021 IEEE India Geoscience and remote sensing Symposium, InGARSS 2021

作者： Gyaneshwar, Dubacharla Rao Nidamanuri, Rama Indian Institute of Space Science and Technology Remote Sensing Laboratory Trivandrum695547 India

ISBN: (纸本)9781665442497

Material characterization and recognition can be accomplished with the help of hyperspectral remote sensing techniques. In recent years, the practical implementation of online classification using hyperspectral imagery has been gaining attention, especially in the remote sensing community, due to its ability to enable a plethora of novel applications. In the coming years, there will be considerable demand for still image inspection and moving image monitoring applications, particularly in the aerospace and industrial automation fields. They require cost-effective, low-power usage and portable image analysis systems with high-performance computing framework abilities. Field-programmable gate arrays (FPGAs) are a suitable solution that can accommodate the above-listed requirements. But, designing and developing the FPGA-based hardware design is known to be a complex process. This paper presents a low-complexity FPGA design approach that performs the one-class support vector machine (OCSVM) classification using ultra-high resolution hyperspectral imagery. © 2021 IEEE.

关键词： remote sensing

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An Interactive platform for low-cost 3D building modeling from VGI data using convolutional neural network

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Big Earth Data 2021年第1期5卷 49-65页

作者： Hongchao Fan Gefei Kong Chaoquan Zhang Department of Civil and Environmental Engineering Norwegian University of Science and TechnologyTrondheimNorway School of Remote Sensing and Information Engineering Wuhan UniversityWuhanChina

The applications of 3D building models are limited as producing them requires massive labor and time costs as well as expensive *** this paper,we aim to propose a novel and web-based interactive platform,VGI3D,to overcome these *** platform is designed to reconstruct 3D building models by using free images from internet users or volunteered geographic informa-tion(VGI)platform,even though not all these images are of high *** interactive platform can effectively obtain each 3D building model from images in 30 seconds,with the help of user interaction module and convolutional neural network(CNN).The user interaction module provides the boundary of building facades for 3D building *** this CNN can detect facade elements even though multiple architectural styles and complex scenes are within the ***,user interaction module is designed as simple as possible to make it easier to use for both of expert and non-expert ***,we conducted a usability testing and collected feedback from participants to better optimize platform and user *** general,the usage of VGI data reduces labor and device costs,and CNN simplifies the process of elements extraction in 3D building ***,our proposed platform offers a promising solution to the 3D modeling community.

关键词： 3D building modeling VGI convolutional neural network user interaction low cost

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Study on a NURBS surface fitting algorithm 2

Study on a NURBS surface fitting algorithm

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2nd International Conference on Algorithms, High Performance Computing and Artificial Intelligence, AHPCAI 2022

作者： Lula, Zhibing Bai, Lidong Guo, Zhongyuan Yanld, Jiaqi Wang, Zhe Sun, Weiwei Beijing Institute of Remote Sensing Equipment Beijing100854 China School of Mechanic and Electric Engineering Beijing Information Science & Technology University Beijing100192 China

ISBN: (纸本)9798350334784

Curve and surface fitting technology is one of the key technologies of 3-dimensional modeling and 3-dimensional design technique. Based on the B-spline method and the corresponding boundary conditions, the method and steps of a cubic NURBS curve and surface fitting were presented in this paper. And then the algorithm was verified by the example of surface fitting. The results show that the algorithm has certain accuracy and it can provide a technical support for surface reverse reconstruction and digital manufacturing in engineering practice. © 2022 IEEE.

关键词： Curve fitting

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Detecting Anomalies in Quick Access Recorder Data 4

Detecting Anomalies in Quick Access Recorder Data

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4th IEEE International Conference on Civil Aviation Safety and Information technology, ICCASIT 2022

作者： Wu, Binwen Zhang, Xiaoyue Xiao, Ming Sun, Huabo Lu, Binbin Wuhan University School of Remote Sensing and Information Engineering Wuhan China Institute of Aviation Safety China Academy of Civil Aviation Science and Technology Beijing China

ISBN: (数字)9781665467667

ISBN: (纸本)9781665467667

For the civil aviation industry, QAR provides a gold mine of data for a wide range of applications. The data quality is an essential precondition for its usage. Thus, to detect the abnormalities or outliers is an important procedure for its further utilization. In this study, we adopted four approaches to detect the abnormalities, i.e. Pauta criterion, boxplot, K-means algorithm and LOF algorithms. Results show that these approaches perform differently for various parameters in a QAR data set. The Pauta criterion seems to be not working well for most of the QAR parameters. In contrast, the K-means clustering and LOF algorithms generally work well with the selected QAR parameters. © 2022 IEEE.

关键词： Civil aviation

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Urban spatial structure analysis:quantitative identification of urban social functions using building footprints

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Frontiers of Earth science 2021年第3期15卷 507-525页

作者： Zhiyao ZHAO Xianwei ZHENG Hongchao FAN Mengqi SUN School of Remote Sensing and Information Engineering Wuhan UniversityWuhan 430072China State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhan 430072China Department of Civil and Environmental Engineering Norwegian University of Science and TechnologyTrondheim 7491Norway

Analysis of urban spatial structures is an effective way to explain and solve increasingly serious urban ***,many of the existing methods are limited because of data quality and availability,and usually yield inaccurate results due to the unclear description of urban social *** this paper,we present an investigation on urban social function based spatial structure analysis using building footprint *** improved turning function(TF)algorithm and a selforganizing clustering method are presented to generate the variable area units(VAUs)of high-homogeneity from building footprints as the basic research *** on the generated VAUs,five spatial metrics are then developed for measuring the morphological characteristics and the spatial distribution patterns of buildings in an urban *** these spatial metrics,three models are formulated for calculating the social function likelihoods of each urban block to describe mixed social functions in an urban block,***,the urban structures can be clearly observed by an analysis of the spatial distribution patterns,the development trends,and the hierarchy of different social *** results of a case study conducted for Munich validate the effectiveness of the proposed method.

关键词： urban spatial structure variable area unit(VAU) spatial metric social function likelihood Open-StreetMap

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T4DT: Tensorizing Time for Learning Temporal 3D Visual Data 33

T4DT: Tensorizing Time for Learning Temporal 3D Visual Data

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33rd British Machine Vision Conference Proceedings, BMVC 2022

作者： Usvyatsov, Mikhail Ballester-Ripoll, Rafael Bashaeva, Lina Schindler, Konrad Ferrer, Gonzalo Oseledets, Ivan Photogrammetry and Remote Sensing ETH Zürich Zürich Switzerland School of Science and Technology IE University Madrid Spain CAIT Skolkovo Institute of Science and Technology Moscow Russia Artificial Intelligence Research Institute Moscow Russia

Unlike 2D raster images, there is no single dominant representation for 3D visual data processing. Different formats like point clouds, meshes, or implicit functions each have their strengths and weaknesses. Still, grid representations such as signed distance functions have attractive properties also in 3D. In particular, they offer constant-time random access and are eminently suitable for modern machine learning. Unfortunately, the storage size of a grid grows exponentially with its dimension. Hence they often exceed memory limits even at moderate resolution. This work proposes using low-rank tensor formats, including the Tucker, tensor train, and quantics tensor train decompositions, to compress time-varying 3D data. Our method iteratively computes, voxelizes, and compresses each frame's truncated signed distance function and applies tensor rank truncation to condense all frames into a single, compressed tensor that represents the entire 4D scene. We show that low-rank tensor compression is extremely compact to store and query time-varying signed distance functions. It significantly reduces the memory footprint of 4D scenes while remarkably preserving their geometric quality. Unlike existing, iterative learning-based approaches like DeepSDF and NeRF, our method uses a closed-form algorithm with theoretical guarantees. © 2022. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

关键词： Tensors

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Atmospheric Rivers and Cloudburst Events over North-West Himalaya: A Case Study

Atmospheric Rivers and Cloudburst Events over North-West Him...

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remote sensing Symposium (InGARSS), 2020 IEEE India Geoscience and

作者： Soumyadeep Roy Charu Singh Atmospheric Science Department Marine and Atmospheric Science Group Indian Institute of Remote Sensing ISRO Dehradun Currently in Centre for Climate Studies Indian Institute of Technology Bombay

ISBN: (数字)9798350390346

ISBN: (纸本)9798350390353

Atmospheric Rivers (AR) are widely recognised as one of the main drivers of moisture transport globally. In this study the effect of AR is analysed over the North-West Himalayan (NWH) region during a series of extreme rainfall events (ERE) that occurred during $14^{\text {th }}$ to $18^{\text {th }}$ August 2019, over the North West Himalaya region. In this study, INSAT 3D Imager data of rainfall and OLR, GPM IMERG Rainfall data and ERA5 reanalysis data are used to investigate the synoptic scale feature of the event. High resolution WRF simulations resolved interesting physical processes associated with the series of the cloud burst events along with the large integrated vapor transport (IVT) signifying the presence of Atmospheric River (AR). The results show significant influence of AR over the moisture transport in NWH region that caused the ERE. The steep topography is also playing an important role in the genesis of cloudburst. Vertical profile of cloud shows heavy moisture loading in mid-troposphere ($600-700 \mathrm{hPa}$). The vertical wind velocity shows strong ascent as high as (-1 $\mathrm{Pa} / \mathrm{s}$) in some areas. Strong vertical ascent together with weak temperature gradient shows signs of rapid orographic lifting. The WRF-ARW simulation shows underestimation of rainfall rate (40 to $70 \mathrm{~mm} / \mathrm{hr}$). Overall, the results show that the unique characteristics of Atmospheric Rivers over the Indian subcontinent and its interaction with High Mountain Asia (HMA) has caused this series of cloudburst events.

关键词： Rain Image resolution Three-dimensional displays Temperature Clouds Atmospheric modeling Wind speed Moisture Surfaces Rivers

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