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

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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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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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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Unsupervised Color Segmentation with Reconstructed Spatial Weighted Gaussian Mixture Model and Random Color Histogram

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Computers, Materials & Continua 2024年第3期78卷 3323-3348页

作者： Umer Sadiq Khan Zhen Liu Fang Xu Muhib Ullah Khan Lerui Chen Touseef Ahmed Khan Muhammad Kashif Khattak Yuquan Zhang School of Computer and Information Science Hubei Engineering UniversityXiaogan432000China Institute for AI Industrial Technology Research Hubei Engineering UniversityXiaogan432000China Key Lab of Mountain Hazards and Surface Processes Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu610041China University of Chinese Academy of Sciences Beijing100049China College of Aviation Zhongyuan University of TechnologyZhengzhou451191China Research Center of Digital Mountain and Remote Sensing Application Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu610041China School of Computer Science University of PoonchAzad Jammu Kashmir12350Pakistan School of Computer Science and Information Engineering Hubei UniversityWuhan430062China

Image classification and unsupervised image segmentation can be achieved using the Gaussian mixture *** the Gaussian mixture model enhances the flexibility of image segmentation,it does not reflect spatial information and is sensitive to the segmentation *** this study,we first present an efficient algorithm that incorporates spatial information into the Gaussian mixture model(GMM)without parameter *** proposed model highlights the residual region with considerable information and constructs color ***,we incorporate the content-based color saliency as spatial information in the Gaussian mixture *** segmentation is performed by clustering each pixel into an appropriate component according to the expectation maximization and maximum ***,the random color histogram assigns a unique color to each cluster and creates an attractive color by default for segmentation.A random color histogram serves as an effective tool for data visualization and is instrumental in the creation of generative art,facilitating both analytical and aesthetic *** experiments,we have used the Berkeley segmentation dataset BSDS-500 and Microsoft Research in Cambridge *** the study,the proposed model showcases notable advancements in unsupervised image segmentation,with probabilistic rand index(PRI)values reaching 0.80,BDE scores as low as 12.25 and 12.02,compactness variations at 0.59 and 0.7,and variation of information(VI)reduced to 2.0 and 1.49 for the BSDS-500 and MSRC datasets,respectively,outperforming current leading-edge methods and yielding more precise segmentations.

关键词： Unsupervised segmentation color saliency spatial weighted GMM random color histogram

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Invariant learning as a pathway to robust potato yield prediction 26

Invariant learning as a pathway to robust potato yield predi...

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remote sensing for Agriculture, Ecosystems, and Hydrology XXVI Conference 2024

作者： Neophytides, Stelios P. Tsoumas, Ilias Tsalakou, Andria Christoforou, Michalakis Mavrovouniotis, Michalis Eliades, Marinos Papoutsa, Christiana Kontoes, Charalampos Hadjimitsis, Diofantos G. ERATOSTHENES Centre of Excellence 82 Franklinou Roosvelt Limassol Cyprus Beyond Centre of EO Research & Satellite Remote Sensing National Observatory of Athens 6 Karystou Athens Greece Department of Civil Engineering and Geomatics Cyprus University of Technology Limassol Cyprus Department of Agricultural Science Biotechnology and Food Science Cyprus University of Technology Limassol Cyprus Wagenigen University & Research 4 Droevendaalsesteeg Wagenigen Netherlands

ISBN: (纸本)9781510680906

Yield prediction is an essential task to sustain the food market and to ensure the food for the world in the upcoming decades. Potatoes (Solanum tuberosum L.) are a vital staple food for many countries in the world and the advancement of accurate yield prediction will aid in promoting the agricultural industry. Potato is one of the most exportable agricultural products in Cyprus. Artificial Intelligence (AI) and remote sensing (RS) based agriculture monitoring has showed a massive impact in yield estimation in recent years. Monitoring vegetation indices like Normalized Difference Vegetation Index during the phenological stages of potatoes can provide identical insights into crop growth and yield. In this study, our focus lies on robust yield prediction across varied spatial and temporal dimensions. Specifically, we explore two distinct regions in Cyprus (i.e seaside and interior), each characterized by unique local agroclimatic conditions. The dataset encompasses potato yield data, in-situ meteorological data and vegetation indices derived by Sentinel-2 for a 7-years period (2017-2023). Thus, we test invariant learning against traditional ML methods in terms of spatial robustness and data drift issues. © 2024 SPIE.

关键词： Agri-food

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UAV and Deep Learning: Detection of selected riparian species along the Ganga River

UAV and Deep Learning: Detection of selected riparian specie...

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ISPRS Technical Commission I Midterm Symposium on Intelligent sensing and remote sensing Application

作者： Tripathi, Ravindra Nath Ramachandran, Aishwarya Agarwal, Karan Tripathi, Vikas Badola, Ruchi Hussain, Syed Ainul Ganga Aqualife Conservation Monitoring Center Wildlife Institute of India Dehradun248001 India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun248002 India Dept. of Remote sensing Birla Institute of Technology Mesra Ranchi835215 India

Environmental protection and sustainable natural resource management are being recognised worldwide as essential goals to safe guard human health and wellbeing. Riparian zones, that face the highest decline in freshwater biodiversity, are of prime conservation priority because they are essential for regulating climate, preserving aquatic-terrestrial biodiversity, maintaining ground water recharge and restoring rivers. In today's fast-paced data-driven environment, artificial intelligence (AI) is the precise answer to a wide range of problems including biodiversity conservation and wildlife management. Leveraging advancements like Uncrewed/Unmanned Aerial Vehicles (UAVs) and AI has resulted innovative strides in wildlife conservation. This study utilised UAV imagery to record high-resolution data of aquatic habitat and species along the Ganga River and employed deep learning algorithms to analyse the data. Through extensive field surveys in the Hastinapur Wildlife Sanctuary, 7,025 photos representing a variety of environments, including 20,000 annotated samples of aquatic animals such as turtles and gharials were generated. Vision based computing capabilities such as pattern recognition model were developed to identify these species. To enrich and enhance the dataset and model, we used different image pre-processing techniques. Slight rotation (±5 degrees), minor cropping (up to 10%), and adjustments in brightness, saturation, and shear (±15%) were applied. Controlled blur (up to 0.5%) and exposure modifications (±5%) were also implemented on the image dataset to improve accuracy. Three Convolutional Neural Network (CNN) architectures, single-stage detectors named YOLO v7, YOLO v8, and Roboflow 3.0, were used for detecting the select species. Results show YOLO v8 excels, achieving mean average precision (mAP) of 98.8% for gharial and 92.2% for turtle detection, with a rapid average detection time of 0.308 seconds per frame at 3200 x 3200 resolution. Additionally, our

关键词： Object detection

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Research on Hyperspectral Surface Reflectance Dataset of Typical Ore Concentration Area in Hami remote sensing Test Field

Research on Hyperspectral Surface Reflectance Dataset of Typ...

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2024 ISPRS Technical Commission I Mid-term Symposium on Intelligent sensing and remote sensing Application

作者： Liang, Shuneng Li, Yang Wei, Hongyan Dong, Lina Zhang, Jiaheng Xiao, Chenchao Land Satellite Remote Sensing Application Center Ministry of Natural Resources Beijing100048 China Key Laboratory of Education Ministry of Precision Opto-mechatromics Technology School of Instrumentation Science and Opto-electronics Ngineering Beihang University Beijing100191 China Beijing100083 China

Surface reflectance data is the basic data source for the hyperspectral parametric remote sensing products and remote sensing quantitative application, which is widely used in various application fields such as natural resources and ecological environment monitoring. At present, multispectral data takes the leading role among the common land surface reflectance datasets and the reflectance data mainly involves the types of ground objects such as farmland, forest land, water body, soil, etc., while the datasets relatively less targets the types of rock and mineral surface objects, yet especially the reflectance datasets with the combination of time series and multi-scale satellite-earth are even more scarce. In order to better promote the application of hyperspectral surface reflectance and explore the advantages of joint application of satellite-earth multi-scale reflectance data, on the basis of field-measured rock and mineral target spectral, a comprehensive surface reflectance dataset was generated by using domestically produced hyperspectral satellite data as the data source in this study, mainly focusing on the typical ore concentration area in the Hami remote sensing test field in Xinjiang. The dataset includes multi-period hyperspectral satellite surface reflectance images, field measured rock and mineral spectral data, and multi-period sub-pixel spectral data collected based on ground spectral measured points, which can provide significant support for the research and development and accuracy verification as well as performance evaluation of algorithms such as surface reflectance inversion, mineral identification and ground object classification. © 2024 Shuneng Liang et al.

关键词： Ores

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Hyperspectral and Multispectral Images Fusion Based on Pyramid Swin Transformer

Hyperspectral and Multispectral Images Fusion Based on Pyram...

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Han Lang Wenxing Bao Wei Feng Shasha Sun Xuan Ma Xiaowu Zhang School of Computer Science and Engineering North Minzu University Yinchuan China Department of Remote Sensing Science and Technology School of Electronic Engineering Xidian University Xi’an China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

remote sensing image fusion aims to generate high spatial resolution hyperspectral images (HR-HSI) by integrating low spatial resolution hyperspectral images (LR-HSI) and high spatial resolution multispectral images (HR-MSI). This paper propose a HSI-MSI fusion method based on the Pyramid Swin Transformer (PSTF). The pyramid design of the PSTF facilitates the extraction of multi-scale information, and it incorporates the Spatial-Spectral Crossed Attention (SSCA) module. The SSCA module is composed of the Cross Spatial Attention (CSA) module and the Spectral Feature Integration (SFI) module. The CSA module uses a cross-shaped self-attention mechanism, enhancing modeling flexibility for different spatial scales and non-local structures. The SFI module integrates global spectral information and local spatial-spectral correlation by introducing a global memory block, efficiently extracting and preserving spectral information. Compared with the state-of-the-art (SOTA) methods, experimental results demonstrate the effectiveness of the PSTF method.

关键词： Location awareness Correlation Memory management Transformers Feature extraction Information retrieval Data mining

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Mapping of lakes in the Qinghai-Tibet Plateau from 2016 to 2021: trend and potential regularity

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International Journal of Digital Earth 2022年第1期15卷 1692-1714页

作者： Zhichong Yang Si-Bo Duan Xiaoai Dai Yingwei Sun Meng Liu College of Earth Science Chengdu University of technologyChengduPeople’s Republic of China Key Laboratory of Agricultural Remote Sensing Ministry of Agriculture and Rural AffairsInstitute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural SciencesBeijingPeople’s Republic of China

Lakes over the Qinghai-Tibet Plateau (TP) have large quantities and areas. As an important component of fragile plateau ecosystems,these lakes have attracted increasing attention. However,owing to the limitations of technology and methods,changes in smaller lakes on the TP have received less attention. In this study,we used Google Earth Engine (GEE) with the Analysis Ready Data (ARD) preparation framework to obtain preprocessed Sentinel-1 data covering the plateau. The D-LinkNet framework was introduced to achieve lake extraction,and the lake dataset was completed from 2016 to 2021. The lake dataset showed an area accuracy of 86.49% and Intersection over Union (IoU) of 0.72-0.99 in different regions. The findings were as follows: during the study period,the TP lakes tended to be stable after increasing,with an increase in area and number of +7.6% and +14.8%. Except for the northwest TP,the other regions show the same general trend. In particular,the Tarim Basin exhibited a lake variation pattern independent of the TP. Significantly,we found frequent lake activity in the Kunlun Mountains,Qaidam Basin,Mountain Qogir,etc. Effects of the ongoing La Niña event on the TP lakes may occur in the next few years.

关键词： Sentinel-1 Qinghai-Tibet Plateau lake mapping Google Earth Engine deep learning

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