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2024 Symposium on Insight to Foresight via geospatial Technologies

作者： Santillan, Jojene R. Heipke, Christian Leibniz University Hannover Institute of Photogrammetry and GeoInformation Nienburger Straße 1 Hannover30167 Germany Caraga State University Caraga Center for Geo-Informatics Department of Geodetic Engineering College of Engineering and Geosciences Ampayon Butuan City8600 Philippines

This paper presents the development and implementation of a web GIS-based application designed to visualize the status and progress of Sustainable Development Goals (SDGs). Focusing specifically on SDG 11.3.1, which pertains to sustainable urbanization, we propose a prototype (accessible at https://***/sdg1131ph/) that leverages open-source geospatial software and earth observation (EO) datasets. By following the "where-when-what-how" framework of SDG visualization system development, we address the need for effective visualization methods while showcasing the potential of accessible datasets and technology in supporting sustainable development monitoring efforts across different administrative levels. We demonstrate the feasibility of the system in aiding policymakers and stakeholders in their decision-making processes toward achieving the SDGs by 2030. Additionally, our work exemplified how open-source software and EO datasets can address the challenges associated with SDG reporting, particularly in areas where both data and visualization tools are scarce or inaccessible. This paper also contributes to the ongoing discourse on SDG visualization and underscores the importance of localized analysis and visualization in understanding and addressing the challenges of sustainable urbanization using the example of the Philippines. © Author(s) 2024.

关键词： Sustainable development goals

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Large-scale geospatial Analytics: Problems, challenges, and Opportunities 23

Large-scale Geospatial Analytics: Problems, Challenges, and ...

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2023 ACM/SIGMOD International Conference on Management of Data, SIGMOD 2023

作者： Chan, Tsz Nam Leong Hou, U. Choi, Byron Xu, Jianliang Cheng, Reynold Hong Kong Baptist University Hong Kong Hong Kong University of Macau China The University of Hong Kong Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities Hong Kong

ISBN: (纸本)9781450395076

geospatial analytics is an important field in many communities, including crime science, transportation science, epidemiology, ecology, and urban planning. However, with the rapid growth of big geospatial data, most of the commonly used geospatial analytic tools are not efficient (or even feasible) to support large-scale datasets. As such, domain experts have raised the concerns about the inefficiency issues for using these tools. In this tutorial, we aim to arouse the attention of database researchers for this important, emerging, database-related, and interdisciplinary topic, which consists of four parts. In the first part, we will discuss different problems and highlight the challenges for two types of geospatial analytic tools, which are (1) hotspot detection and (2) correlation analysis. In the second and third parts, we will specifically discuss two geospatial analytic tools, namely kernel density visualization (the representative hotspot detection method) and K-function (the representative correlation analysis method), respectively, and their variants. In the fourth part, we will highlight the future opportunities for this topic. © 2023 ACM.

关键词： Large dataset

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HPAN: Hierarchical Part-Aware Network for Fine-Grained Segmentation of Street View Imagery

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 7794-7810页

作者： Zhong, Leiyang Guo, Wenhao Zheng, Jiayi Yan, Liyue Xia, Jizhe Zhang, Dejin Li, Qingquan Shenzhen Univ Coll Civil & Transportat Engn Shenzhen 518060 Peoples R China Shenzhen Univ Guangdong Key Lab Urban Informat Shenzhen 518060 Peoples R China Shenzhen Univ Shenzhen Key Lab Spatial Smart Sensing & Serv Shenzhen 518060 Peoples R China Shenzhen Univ Key Lab Geoenvironm Monitoring Great Bay Area Minist Nat Resources MNR Shenzhen 518060 Peoples R China Southwest Jiaotong Univ Fac Geosci & Environm Engn Chengdu 611756 Peoples R China

Street view imagery (SVI) has become a valuable geospatial data source for urban analysis, offering rich information about urban environments from a human-centric perspective. However, existing segmentation methods face significant challenges due to the inherent complexities of SVI, including scale variations, occlusions, and diverse semantic hierarchies. Drawing inspiration from the hierarchical nature of human visual cognition, this study proposes the hierarchical part-aware network (HPAN) to address these challenges in the fine-grained segmentation of SVI. The HPAN framework integrates four key components: (1) a hierarchical consistency learning module (HCLM), which ensures consistency across different levels of segmentation through novel loss functions;(2) a topology-aware graph matching module (TGMM), designed to model spatial relationships between object parts;(3) an edge-guided feature enhancement module (EFEM), which incorporates fine-grained edge information;and (4) a multilevel joint attention module (MLJAM), which adaptively fuses global scene semantics with local object details. Extensive experiments conducted on the cityscapes panoptic parts dataset demonstrate that HPAN outperforms existing methods across multiple panoptic quality metrics, particularly excelling in part-level segmentation tasks. Further evaluations on the mapillary vistas dataset and the cityscapes dataset validate HPAN's robust semantic segmentation performance across diverse street scenes. Generalization tests on different SVI sources, including challenging scenarios, such as low-light conditions and occluded environments, highlight the model's strong adaptability and effectiveness.

关键词： Semantics visualization Image segmentation Urban areas Semantic segmentation Remote sensing Pedestrians Layout Visual systems Object detection Fine-grained segmentation hierarchical part-aware network (HPAN) street view imagery (SVI) urban scene understanding

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GeoCROW: Immersive geospatial Data visualization and Interaction in Virtual Reality

GeoCROW: Immersive Geospatial Data Visualization and Interac...

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Virtual Reality and 3D User Interfaces Abstracts and workshops (VRW), IEEE Conference on

作者： Tom Lautenbach Oskar Perl Hugo Brument Hannes Kaufmann VR&AR Research Unit TU Wien TU Wien

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

Integrating geosemantically annotated, crawled data into virtual reality (VR) poses several challenges, particularly in presenting and interacting with information. Unlike desktop visualizations that dominate a screen, VR visualizations must coexist with the immersive environment, occupying limited space without disrupting natural surroundings. Representing relationships between spatially distributed entities, such as those derived from news articles and social media, adds complexity, requiring innovative approaches to highlight connections effectively. Furthermore, user interaction with these visualizations must leverage free movement and sensory feedback while addressing the constraints of a smaller physical space and avoiding motion sickness. This paper introduces GeoCROW, an immersive geospatial data visualization and interaction platform. We describe our VR prototype designed to meet these challenges, including a user study and stakeholder workshop feedback. Our results contribute to the visualization of geospatial data, and we discuss the future iterations of the platform to improve it.

关键词： Human computer interaction visualization Three-dimensional displays Conferences Data visualization Prototypes Virtual reality Spatial databases geospatial analysis Stakeholders

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City2Twin: an open urban digital twin from data integration to visualization and analysis 8

City2Twin: an open urban digital twin from data integration ...

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8th International ISPRS workshop LowCost 3D - Sensors, Algorithms, Applications

作者： Rafamatanantsoa, Benirina Parfait Jeddoub, Imane Yarroudh, Anass Hajji, Rafika Billen, Roland College of Geomatic Sciences and Surveying Engineering Hassan II Institute of Agronomy and Veterinary Medicine Rabat10101 Morocco GeoScITY Spheres Research Unit University of Liège Liège4000 Belgium

Urban Digital Twins have gained significant interest in the urban and geospatial fields, enabling interactive visualization and advanced analysis of cities across various domains. However, current implementation approaches are heterogeneous in terms of data and approaches. Furthermore, most implementations are based on specific needs. This project develops a comprehensive framework for Urban Digital Twins, focusing on data integration, storage, visualization, and analysis, all using open-source tools. Our approach integrates various data types, including 3D city models, dynamic air quality data, and external data imported from the client side, such as vector data, 3D city models, and point clouds. We conducted a series of experiments for each step and tackled various challenges. Many configurations are applied before integrating the 3D models, including ground reprojection, geometry type conversion, and format conversion. For the data storage and management, we performed several comparative tests between 3DCityDB and CJDB, which led us to choose CJDB for its simplicity and lightweight nature. A client interface built with the Giro3D framework (based on ***) connects directly to the 3D model database via a Flask server. Dynamic data is retrieved via external APIs and stored in a separate database following the SensorThings API standard, allowing time-series analysis. Our framework is standardized and designed based on open-source software, emphasizing the openness, transferability, reusability, and maintainability of Urban Digital Twin. The City2Twin project proposes significant improvements in data analysis, highlighting the importance of having a separate database for storing static and dynamic data, as well as the importance of direct interaction between the client interface and the 3D database for data updates and management. To the best of our knowledge, this work is the first Urban Digital Twin initiative that relies on the CityJSON format, which defines i

关键词： Computer software reusability

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Mobility Data Mining: the Maritime Use Case

Mobility Data Mining: the Maritime Use Case

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IEEE Metrology for Aerospace

作者： Alexandros Troupiotis-Kapeliaris Dimitris Zissis Sean Kohlbrenner Christos Kastrisios Department of Product and Systems Design Engineering Intelligent Transportation Systems Lab (Smart MOVE) University of the Aegean Syros Greece Center for Coastal & Ocean Mapping / Joint Hydrographic Center University of New Hampshire Durham NH USA

ISBN: (数字)9798350385045

ISBN: (纸本)9798350385052

Maritime mobility monitoring can be achieved through remote sensing and self-reporting systems, which produce large datasets enabling the extraction of valuable information on global shipping trends. This extraction can be defined as a data mining task of transforming huge amounts of geospatial data, into a descriptive and compact data model, that can then be used for identifying the underlying relationships or patterns. In this work, we present a brief overview of mobility data mining as applied to the maritime use case. We highlight its unique aspects, identify six major data mining tasks, discuss indicative approaches found in literature, and address some challenges for future work.

关键词： Conferences Metrology Market research Data models geospatial analysis Data mining Task analysis

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Keynote: In Varietate Concordia: How Software visualization and Information visualization Have Evolved From, Around, and Along Each Other

Keynote: In Varietate Concordia: How Software Visualization ...

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IEEE International workshop on Visualizing Software for Understanding and analysis (VISSOFT)

作者： Alexandru C. Telea Utrecht University

Software visualization (softvis) and information visualization (infovis) have a long, interconnected, and complex joint history. Originally appearing as a subdomain of infovis which focuses on solving problems coming from the software engineering domain, softvis has grown in the last two decades to become a selfstanding field with distinct challenges, key results, events, and community. In the same time, the independent growth of the two fields has made the transfer of ideas, techniques, methods, application cases, and researchers between the two domains increasingly challenges. In this talk, I will present a history of this highly dynamic process and argue about the need for raprochemment of infovis and softvis. This need is supported by two key aspects identified and further discussed: (1) Complementarity of the two fields advocates for more interaction, as shown by success stories from softvis which led to entirely novel branches of development into the infovis field and, conversely, recent key developments in infovis which offer strong potential to be picked up to address existing key challenges in softvis. (2) Commonality, in terms of both fields essentially aiming to solve very similar visualization problems that address very similar data and using related visualization pipelines, advocates on an increasingly joint approach in their further development.

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A Biomimetic Ankle-Foot Prosthesis for Improving Postural Balance in Lower Limb Amputees: A Proof-of-Concept Study

A Biomimetic Ankle-Foot Prosthesis for Improving Postural Ba...

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Proceedings of the IEEE Dallas Circuits and Systems workshop (DCAS)

作者： Sirsendu Sekhar Mishra Deepak Joshi Bijaya Ketan Panigrahi Department of Electrical Engineering Indian Institute of Technology Delhi New Delhi India Centre for Biomedical Engineering Indian Institute of Technology Delhi New Delhi India

ISBN: (数字)9798350349535

ISBN: (纸本)9798350349542

Millions of individuals with lower limb amputations encounter considerable challenges in maintaining postural stability due to limited joint movement in passive prostheses. This instability leads to frequent falls and hinders rehabilitation progress. Current prosthetic control models lack precision in accounting for diverse biomechanical variations, further impacting the efficacy of the rehabilitation process. To address this, we developed a novel biomimetic ankle-foot prosthesis prototype with an external ankle joint control to maintain postural balance. Our biomimetic design utilizes a biomechanically relevant neural control approach, i.e., proportional integral derivative controller, guided by the intact limb trajectories, enabling the prosthesis to mimic natural movement patterns and achieve postural balance during weight-shifting exercises. This approach holds promising potential for enhancing human-prosthetic interaction and ultimately improving rehabilitation outcomes and acceptance of advanced prosthetic technologies by individuals with lower limb amputation.

关键词： Biomechanics visualization Biomimetics Biological system modeling Prototypes Process control Stability analysis

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High-Dimensional Spatial Simulation Ensemble analysis 9

High-Dimensional Spatial Simulation Ensemble Analysis

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9th ACM SIGSPATIAL International workshop on Analytics for Big geospatial Data (BigSpatial)

作者： Dahshan, Mai House, Leanna Polys, Nicholas Virginia Tech Blacksburg VA 24061 USA

ISBN: (纸本)9781450381628

Advancements in computational power enable scientists to study complex spatial systems using varying models or initial conditions in what is known as spatial ensemble. Analyzing all facets of such high dimensional spatial ensembles poses challenges for its representation and visualization. This requires the design of interactive analysis tool(s) that supports visualization, analysis, and exploration of spatial ensembles in multiple ways and at multiple levels of abstraction. The difficulty in such representation is the need to consider both data dimensionality and spatiality simultaneously. Much research has been done for visually analyzing spatial ensembles;however, the majority of these visualizations capture differences in the spatial ensemble through descriptive statistics, neglecting spatial data characteristics, which severely limits the exploration process. In this paper, we propose a visual analysis approach that employs statistical and visual analytics methodologies for a more effective understanding and exploration of spatial ensembles without the need for in-depth knowledge of the underlying computational models and ensemble features and association.

关键词： Simulation Ensembles Spatial Data Visual Analytics Large Scale visualization Gaussian Process Parallel Computation

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PTQ4VM: Post-Training Quantization for Visual Mamba

PTQ4VM: Post-Training Quantization for Visual Mamba

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IEEE workshop on Applications of Computer Vision (WACV)

作者： Younghyun Cho Changhun Lee Seonggon Kim Eunhyeok Park Pohang University of Science and Technology (POSTECH) Republic of Korea

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Visual Mamba is an approach that extends the selective space state model, Mamba, to vision tasks. It processes image tokens sequentially in a fixed order, accumulating information to generate outputs. Despite its growing popularity for delivering high-quality outputs at a low computational cost across various tasks, Visual Mamba is highly susceptible to quantization, which makes further performance improvements challenging. Our analysis reveals that the fixed token access order in Visual Mamba introduces unique quantization challenges, which we categorize into three main issues: 1) token-wise variance, 2) channel-wise outliers, and 3) a long tail of activations. To address these challenges, we propose Post-Training Quantization for Visual Mamba (PTQ4VM), which introduces two key strategies: Per-Token Static (PTS) quantization and joint Learning of Smoothing Scale and Step Size (JLSS). To the our best knowledge, this is the first quantization study on Visual Mamba. PTQ4VM can be applied to various Visual Mamba backbones, converting the pre-trained model to a quantized format in under 15 minutes without notable quality degradation. Extensive experiments on large-scale classification and regression tasks demonstrate its effectiveness, achieving up to 1.83x speedup on GPUs with negligible accuracy loss compared to FP16. Our code is available at https://***/YoungHyun197/ptq4vm.

关键词： Degradation visualization Computer vision Quantization (signal) Smoothing methods Codes Computational modeling Tail Computer architecture Computational efficiency

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