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2024 Mid-term Symposium on spatial Information to Empower the Metaverse

作者： Jafary, Peyman Shojaei, Davood Pishgahi, Sara Rajabifard, Abbas Ngo, Tuan Building 4.0 CRC Caulfield EastVIC Australia Centre for Spatial Data Infrastructures and Land Administration Department of Infrastructure Engineering The University of Melbourne MelbourneVIC Australia Centre for Urban Research School of Global Urban and Social Studies RMIT University MelbourneVIC Australia Department of Infrastructure Engineering The University of Melbourne MelbourneVIC Australia

The escalating rental crisis in Australia, rooted in demographic dynamics, a scarcity of lands suitable for development and the influence of interest rates and government subsidies, necessitates innovative solutions for housing affordability. One promising approach is to increase the supply of affordable rental housing. Nevertheless, these efforts must be guided by well-informed policies as they are required to cater to the diverse needs of the stakeholders involved. Accordingly, this study aims to prioritize suburbs in the Melbourne Metropolitan area for the construction of apartment/unit buildings, benefiting both builders/investors and renters. Leveraging a hybrid Genetic Algorithm-Machine Learning (GA-ML) framework, multi-objective optimization models are developed to rank 105 suburbs based on key parameters for both builders/investors and renters. The optimization process seeks to maximize economic outlook and rental yields for builders/investors while minimizing rents and commuting distances for renters, in addition to proximity to essential amenities. First, an initial optimization using GA based on six key parameters is performed considering a linear multi-objective function. Subsequently, ML-based objective functions are defined using Random Forest (RF) and extreme Gradient Boosting (XGBoost) models to refine the optimization model for suburb rankings. The evaluation reveals strong correlations between GA and GA-XGB rankings, suggesting the effectiveness of the GA-XGBoost model in prioritizing suburbs. Notably, suburbs consistently prioritized across all models include Brunswick, Coburg, Preston and Reservoir, highlighting their suitability for apartment/unit building construction. By directing attention to specific suburbs aligned with different stakeholders' needs and preferences, this study contributes to a more sustainable and equitable housing landscape. © Author(s) 2024.

关键词： Genetic Algorithm Housing Affordability Machine Learning Multi-Objective Optimization Random Forest XGBoost

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Analysis Geomorphic Flood Index in Determining Flood-Prone Areas in East Kalimantan Province, Indonesia

Analysis Geomorphic Flood Index in Determining Flood-Prone A...

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2024 Annual Conference, ASPRS 2024

作者： Virtriana, Riantini Amalia, Annisa Fitri Ihsan, Kalingga Titon Nur Anggraini, Tania Septi Harto, Agung Budi Deliar, Albertus Geospatial Information Science and Technology Research Group Faculty of Earth Science and Technology Bandung Institute of Technology Bandung40132 Indonesia Center for Remote Sensing Bandung Institute of Technology Bandung40132 Indonesia Geodesy and Geomatic Engineering Faculty of Earth Sciences and Technology Bandung40132 Indonesia Doctor Program in Program Study of Geodesy and Geomatic Engineering Faculty of Earth Sciences and Technology Bandung40132 Indonesia Universitas Pendidikan Indonesia Bandung40111 Indonesia Center for Spatial Data Infrastructure Bandung Institute of Technology Bandung40132 Indonesia

In 2022, there are 1,524 flood events out of a total of 3,531 disasters occurring in Indonesia. East Kalimantan Province ranked 20th among the 35 provinces in Indonesia with 28 flood occurrences in 2022, resulting in two fatalities. This highlights the urgent need for addressing flood hazard to prevent future casualties. There are some methods that can be used to determine the flood hazard in a region. In this research aims to identify flood-prone areas in East Kalimantan Province and evaluate the effectiveness of different mapping methods. The methodology that compares are the Pairwise Comparison method and Geomorphic Flood Index (GFI). The study's results provide flood-prone area maps of East Kalimantan Province using both methods, recommend the more efficient method for flood-prone area identification, and offer mitigation strategies based on the findings. This research is intended to serve as a reference for selecting the best method for identifying flood-prone areas and for informing policy decisions to mitigate flood impacts in East Kalimantan Province. © Author(s) 2025.

关键词： Hazards

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Comparative analysis of the mcda and gfi methods in determining flood-prone areas in jatinangor district, sumedang

Comparative analysis of the mcda and gfi methods in determin...

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2023 Annual Conference, ASPRS 2023

作者： Virtriana, R. Retnowati, D.A. Rohayani, P. Anggraini, T.S. Ihsan, K.T.N. Harto, A.B. Riqqi, A. Remote Sensing and Geographic Information Sciences Research Group Faculty of Earth Sciences and Technology Institut Teknologi Bandung Bandung40132 Indonesia Center for Remote Sensing Institut Teknologi Bandung Bandung40132 Indonesia Doctor Program of Geodesy and Geomatics Engineering Faculty of Earth Sciences and Technology Institut Teknologi Bandung Jl. Ganesha No.10 Bandung40132 Indonesia Master Program of Civil Engineering The University of Tokyo Japan Center for Spatial Data Infrastructure Institut Teknologi Bandung Bandung40132 Indonesia

Flood is one of the natural disasters which has a high intensity in terms of occurrence. Despite the loss value in each event which is not as high as some natural disasters, such as earthquakes or tsunamis, the high occurrence of floods may cause high loss in total. Floods damage property and infrastructure, disrupt economic activity, displace people, harm communities, and degrade ecosystems. This study aims to compare the flood hazard model using Geomorphic Flood Index (GFI) and Multi-criteria Decision Analysis (MCDA). GFI is an established method which already used globally to identify the flood-prone area and the depth of the flood. The parameter needed to calculate GFI are the elevation, river network, and historical flood event. Meanwhile, the MCDA method tries to combine environmental, physical, and hydrographic factors, such as land use/land cover, precipitation, and runoff. The study area is Jatinangor District in Sumedang Regency which part of West Java Province, Indonesia. This location is chosen based on historical and potential flood events. Besides, Jatinangor District is the center of industry and commerce which Sumedang Regency is very dependent on. The finding of this study is expected to identify suitable methods for assessing flood hazards in Jatinangor or other areas with similar characteristics, between GFI and MCDA. © 2023 International Society for Photogrammetry and Remote Sensing. All rights reserved.

关键词： Decision making

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Development of location suitability prediction for health facilities using random forest machine learning in 2030 integrating remote sensing and GIS in West Java, Indonesia

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Environmental Advances 2025年 19卷

作者： Virtriana, Riantini Ihsan, Kalingga Titon Nur Anggraini, Tania Septi Deliar, Albertus Harto, Agung Budi Riqqi, Akhmad Sakti, Anjar Dimara Remote Sensing and GIS Research Group Faculty of Earth Science and Technology Bandung Institute of Technology Bandung 40132 Indonesia Center for Remote Sensing Bandung Institute of Technology Bandung 40132 Indonesia Doctor Program in Program Study of Geodesy and Geomatic Engineering Faculty of Earth Sciences and Technology Bandung 40132 Indonesia Geographic Information Science Universitas Pendidikan Indonesia Bandung 40111 Indonesia Center for Spatial Data Infrastructure Bandung Institute of Technology Bandung 40132 Indonesia

Access to healthcare facilities is crucial in the present day. Healthcare facilities must be proportional to the population in a given area. Therefore, it is important to increase the number of healthcare facilities in regions where there is an imbalance. Proper planning and a sustainability review are necessary when determining locations for healthcare facilities. Environmental changes can affect the suitability of a location in the future. Thus, planning that can predict future suitability conditions is required to ensure that the built locations have high sustainability. This study predicts the suitability of healthcare facility locations in 2030 in West Java using remote sensing and Geographic Information System (GIS). Both static and dynamic data processed at 30×30 meter intervals across West Java will be used. Geospatial and remote sensing data are utilized in the study. Dynamic parameter extrapolation uses data from 2000 to 2018. The random forest machine learning method is employed to obtain the suitability values for healthcare facility locations using existing health facility training data in West Java. The results show changes in the suitability classes of healthcare facilities in each region from 2018 to 2030, with some areas experiencing an increase or decrease in class. This research highlights consistently suitable locations, ensuring their sustainability as healthcare facility sites. © 2024

关键词： GIS Health Facility Machine Learning Remote Sensing

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DEVELOPMENT of GEOspatial INFORMATION INTEGRATED with BIG data to AGRICULTURAL HAZARD MONITORING in WEST JAVA

DEVELOPMENT of GEOSPATIAL INFORMATION INTEGRATED with BIG DA...

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2022 Annual Conference, ASPRS 2022

作者： Virtriana, R. Riqqi, A. Anggraini, T.S. Fauzan, K.N. Ihsan, K.T.N. Mustika, F.C. Atmaja, F.W. Suwardhi, D. Harto, A.B. Sakti, A.D. Deliar, A. Soeksmantono, B. Wikantika, K. Remote Sensing and Geographic Information Sciences Research Group Faculty of Earth Sciences and Technology Institut Teknologi Bandung Bandung40132 Indonesia Center for Remote Sensing Institut Teknologi Bandung Bandung40132 Indonesia Center for Spatial Data Infrastructure Institut Teknologi Bandung Bandung40132 Indonesia Master Program of Geodesy and Geomatics Engineering Faculty of Earth Sciences and Technology Institut Teknologi Bandung Jl. Ganesha No.10 Bandung40132 Indonesia Doctoral Program of Geodesy and Geomatics Engineering Faculty of Earth Sciences and Technology Institut Teknologi Bandung Jl. Ganesha No.10 Bandung40132 Indonesia

Food security is highly dependent on three aspects, namely food availability, food access, and food utilization. The availability aspect depends on food supply which is identical to agricultural productivity. West Java Province is the third national rice producer with 16.6%, but West Java Province is the most extensive rice consumer, around 21.1% of the total national rice consumption. Agricultural productivity can decline due to natural hazards such as floods and droughts. Monitoring floods and droughts in paddy fields are necessary to prevent decreased agricultural productivity. This study aims to monitor the rice fields from the dangers of flooding and drought every month. Agricultural hazard monitoring is divided into two parameters, namely static parameters and dynamic parameters. Dynamic parameters are observed every month so that the hazard index is generated on a monthly scale. GIS and Remote sensing data are integrated to perform agricultural hazard modelling. Furthermore, this agricultural hazard modelling results will be strengthened by using big to provide information about an almost real-Time event that can be accessed through the Application Program Interface (API) service. This study uses a data mining system from Drone Emprit that performs data mining on Twitter and news portals with machine learning technology (probabilistic classifier) and Natural Learning Process. The results obtained are around 15,000 data from January 1 to November 1, 2021, and 37.9% of them are identified by location based on the city or district level in West Java Province. It is hoped that the policy-maker can consider the area of agricultural land that requires assistance to increase productivity and plan a policy to support agriculture in West Java in the future. © 2022 R. Virtriana et al.

关键词： Agriculture

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Governing AI-Driven Health research: Are IRBs Up to the Task?

Ethics & human research

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Ethics & human research 2021年第2期43卷 35-42页

作者： Friesen, Phoebe Douglas-Jones, Rachel Marks, Mason Pierce, Robin Fletcher, Katherine Mishra, Abhishek Lorimer, Jessica Véliz, Carissa Hallowell, Nina Graham, Mackenzie Chan, Mei Sum Davies, Huw Sallamuddin, Taj Assistant professor in the Biomedical Ethics Unit and the Department of Social Studies of Medicine at McGill University Associate professor of anthropological approaches to data and infrastructure head of the Technologies in Practice research group the codirector of the ETHOS Lab at the IT University of Copenhagen Assistant professor of law at Gonzaga University and the Edmond J. Safra/Petrie-Flom Centers Joint Fellow-in-Residence at Harvard University Technology and Society at the Tilburg Law School at Tilburg University Coordinator of Cyber Security Oxford and the founding administrator of the Computer Science Department Research Ethics Committee in the Department of Computer Science at the University of Oxford DPhil candidate at the Uehiro Centre for Practical Ethics at the University of Oxford DPhil candidate on the NEUROSEC team in the Department of Psychiatry at the University of Oxford Associate professor in the Faculty of Philosophy at the Institute for Ethics in AI as well as a tutorial fellow at Hertford College at the University of Oxford Codirector of the EPSRC Centre for Doctoral Training in Health Data Science and a professor at the Ethox Centre and Wellcome Centre for Ethics and Humanities at the University of Oxford Senior research fellow in data ethics at the Wellcome Centre for Ethics and Humanities in the Nuffield Department of Population Health at the University of Oxford DPhil student in the Nuffield Department of Population Health at the University of Oxford and a research fellow in the Department of Applied Health Research at University College London Lecturer in education at the School of Education and Sport at the University of Edinburgh Data protection and information lawyer

Many are calling for concrete mechanisms of oversight for health research involving artificial intelligence (AI). In response, institutional review boards (IRBs) are being turned to as a familiar model of governance. Here, we examine the IRB model as a form of ethics oversight for health research that uses AI. We consider the model's origins, analyze the challenges IRBs are facing in the contexts of both industry and academia, and offer concrete recommendations for how these committees might be adapted in order to provide an effective mechanism of oversight for health-related AI research. © 2021 by The Hastings Center. All rights reserved.

关键词： AI research oversight artificial intelligence ethics committees health-related AI research institutional review boards research ethics

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Call to action for global access to and harmonization of quality information of individual earth science datasets

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data Science Journal 2021年第1期20卷

作者： Peng, Ge Downs, Robert R. Lacagnina, Carlo Ramapriyan, Hampapuram Ivánová, Ivana Moroni, David Wei, Yaxing Larnicol, Gilles Wyborn, Lesley Goldberg, Mitch Schulz, Jörg Bastrakova, Irina Ganske, Anette Bastin, Lucy Khalsa, Siri Jodha S. Wu, Mingfang Shie, Chung-Lin Ritchey, Nancy Jones, Dave Habermann, Ted Lief, Christina Maggio, Iolanda Albani, Mirko Stall, Shelley Zhou, Lihang Drévillon, Marie Champion, Sarah Hou, C. Sophie Doblas-Reyes, Francisco Lehnert, Kerstin Robinson, Erin Bugbee, Kaylin Earth System Science Center/NASA MSFC IMPACT The University of Alabama in Huntsville HuntsvilleAL United States Columbia University Palisades NY United States Spain Ramapriyan Science Systems and Applications Inc. Lanham MD United States NASA Goddard Space Flight Center GreenbeltMD United States Curtin University Australia Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States Environmental Sciences Division Oak Ridge National Laboratory Oak RidgeTN United States /Magellium Spain National Computational Infrastructure Australian National University ACT Australia Silver SpringMD United States Darmstadt Germany Geoscience Australia ACT Australia Hannover Germany Aston University United Kingdom Cooperative Institute for Research in Environmental Sciences NSIDC BoulderCO United States Australian Research Data Commons Melbourne Australia University of Maryland at Baltimore County BaltimoreMD United States AshevilleNC United States StormCenter Communications | GeoCollaborate HalethorpeMD United States Metadata Game Changers BoulderCO United States Rhea GROUP La Piramide Via di Grotte Portella 6/8 Frascati00044 Italy European Space Agency Frascati Italy American Geophysical Union WashingtonDC United States Mercator Ocean International France North Carolina Institute for Climate Studies North Carolina State University AshevilleNC United States Ronin Institute United States Spain Lamont-Doherty Earth Observatory of Columbia University Palisades NY United States NASA Marshall Space Flight Center HuntsvilleAL United States

Knowledge about the quality of data and metadata is important to support informed decisions on the (re)use of individual datasets and is an essential part of the ecosystem that supports open science. Quality assessments reflect the reliability and usability of data. They need to be consistently curated, fully traceable, and adequately documented, as these are crucial for sound decision-and policy-making efforts that rely on data. Quality assessments also need to be consistently represented and readily integrated across systems and tools to allow for improved sharing of information on quality at the dataset level for individual quality attribute or dimension. Although the need for assessing the quality of data and associated information is well recognized, methodologies for an evaluation framework and presentation of resultant quality information to end users may not have been comprehensively addressed within and across disciplines. Global interdisciplinary domain experts have come together to systematically explore needs, challenges and impacts of consistently curating and representing quality information through the entire lifecycle of a dataset. This paper describes the findings of that effort, argues the importance of sharing dataset quality information, calls for community action to develop practical guidelines, and outlines community recommendations for developing such guidelines. Practical guidelines will allow for global access to and harmonization of quality information at the level of individual Earth science datasets, which in turn will support open science. © 2021 The Author(s).

关键词： Information dissemination

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Climate-driven ‘species-on-the-move’ provide tangible anchors to engage the public on climate change

People and Nature

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People and Nature 2023年第5期5卷

作者： Gretta T. Pecl Rachel Kelly Chloe Lucas Ingrid van Putten Renuka Badhe Curtis Champion I-Ching Chen Omar Defeo Juan Diego Gaitan-Espitia Birgitta Evengård Damien A. Fordham Fengyi Guo Romina Henriques Sabine Henry Jonathan Lenoir Henry McGhie Tero Mustonen Stephen Oliver Nathalie Pettorelli Malin L. Pinsky Warren Potts Julia Santana-Garcon Warwick Sauer Anna-Sofie Stensgaard Morgan W. Tingley Adriana Verges Centre for Marine Socioecology University of Tasmania Hobart Tasmania Australia Institute for Marine and Antarctic Studies University of Tasmania Hobart Tasmania Australia CSIRO Oceans and Atmosphere Castray Esplanade Battery Point Tasmania Australia Future Ocean and Coastal Infrastructure Consortium Memorial University of Newfoundland St. John's Newfoundland and Labrador Canada School of Geography Planning and Spatial Sciences University of Tasmania Hobart Tasmania Australia European Polar Board The Hague The Netherlands Fisheries Research NSW Department of Primary Industries National Marine Science Centre Coffs Harbour New South Wales Australia National Marine Science Centre Southern Cross University Coffs Harbour New South Wales Australia Department of Life Sciences National Cheng Kung University Tainan City Taiwan UNDECIMAR. Faculty of Sciences Montevideo Uruguay The Swire Institute of Marine Science and School of Biological Sciences The University of Hong Kong Hong Kong SAR China Department of Clinical Microbiology Umeå University Umeå Sweden The Environment Institute and School of Biological Sciences University of Adelaide Adelaide South Australia Australia Center for Macroecology Evolution & Climate GLOBE Institute University of Copenhagen Copenhagen Denmark Department of Ecology and Evolutionary Biology Princeton University Princeton New Jersey USA Technical University of Denmark National Institute of Aquatic Resources Silkeborg Denmark Marine Genomics Group Department of Biochemistry Genetics and Microbiology University of Pretoria Hatfield South Africa Department of Geography and Institute of Life Earth and Environment (ILEE) University of Namur Belgium UMR CNRS 7058« Ecologie et Dynamique des Systèmes Anthropisés » (EDYSAN) Université de Picardie Jules Verne Amiens France Curating Tomorrow Liverpool UK Snowchange Cooperative Selkie Finland Australian Broadcasting Corporation Ultimo New South Wales Australia Zoological Society

Over recent decades, our understanding of climate change has accelerated greatly, but unfortunately, observable impacts have increased in tandem. Both mitigation and adaptation have not progressed at the level or scale warranted by our collective knowledge on climate change. More effective approaches to engage people on current and future anthropogenic climate change effects are urgently needed. Here, we show how species whose distributions are shifting in response to climate change, that is, ‘species-on-the-move’, present an opportunity to engage people with climate change by linking to human values, and our deep connections with the places in which we live, in a locally relevant yet globally coherent narrative. Species-on-the-move can impact ecosystem structure and function, food security, human health, livelihoods, culture and even the climate itself through feedback to the climate system, presenting a wide variety of potential pathways for people to understand that climate change affects them personally as individuals. Citizen science focussed on documenting changes in biodiversity is one approach to foster a deeper engagement on climate change. However, other possible avenues, which may offer potential to engage people currently unconnected with nature, include arts, games or collaborations with rural agriculture (e.g. new occurrences of pest species) or fisheries organisations (e.g. shifting stocks) or healthcare providers (e.g. changing distributions of disease vectors). Through the importance we place on the aspects of life impacted by the redistribution of species around us, species-on-the-move offer emotional pathways to connect with people on the complex issue of climate change in profound ways that have the potential to engender interest and action on climate change. Read the free Plain Language Summary for this article on the Journal blog. 近几十年，我们对气候变化的认识有了很大进展，然而气候变化造成的影响也与日俱增。目前的减缓和适应措施仍远低于学界共识下应对气候变化所需的水平。我们亟需更有效的方法来让人们关注当前和未来由人类活动造成的气候变化影响。本文展示了如何借

关键词： biodiversity climate change communication climate change engagement environmental communication human values message framing place attachment species redistribution

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Teaching VGI as a strategy to promote the production of urban digital cartographic databases

Teaching VGI as a strategy to promote the production of urba...

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Joint Urban Remote Sensing Event

作者： Homero Fonseca Filho Beatriz Paula Leite Guilherme Andretta Pompermayer Werner Leyh Marcelo Gaioso Werneck Group of Studies and Research into Spatial Data Infrastructure Geospatial Computer Lab University of S?o Paulo (USP) / School of Arts Science and Humanities (EACH) S?o Paulo Brazil

ISBN: (纸本)9781479902132

Exploring a "real life use case", the present study analyzed Internet free and open map systems and elected OpenStreetMap as appropriate for undergraduate students who have studied and tested it. OpenStreetMap (OSM) proved to be easily used by students, mainly due to the community-oriented characteristics of this "Volunteered geographic information" - system (VGI). The test conducted by this research work was the adoption of tasks such as mapping towns within cartography disciplines, which confirmed the ability of OSM to contribute to the learning process, using their "local knowledge", motivated for involving real-life problems of their local neighborhoods as problem-oriented learning approach. A learning step flow (script) indicated that such conditions can be explored in formal academic (cyber-) cartographic education in both ways: cartographic production and education can mutually benefit from each other - based on a collaborative framework pushed by local real-life challenges. Overall, this paper will be of interest to lecturers and students considering using OSM as a "learn by doing" platform for "Location-based service applications" (LBS).

关键词： LBS OSM VGI

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