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The 2022 report of the Lancet Countdown on health and climate change: health at the mercy of fossil fuels (vol 400, pg 1619, 2022)

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LANCET 2022年第10365期400卷 1766-1766页

作者： Romanello, M. Di Napoli, C. Drummond, P. Institute for Global Health University College London London UK School of Agriculture Policy and Development University of Reading Reading UK Institute for Sustainable Resources University College London London UK Department of Global Health Centre for Health and the Global Environment University of Washington Seattle WA USA UCL Energy Institute University College London London UK Department of Health Sciences University of York York UK Department of Meteorology University of Reading Reading UK Institute for Risk and Disaster Reduction University College London London UK School of Earth and Environment University of Leeds Leeds UK Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London UK School of Population Health University of Melbourne Melbourne VIC Australia Department of Earth System Science Tsinghua University Beijing China Mercator Research Institute on Global Commons and Climate Change Berlin Germany Department of Environment Climate Change and Health World Health Organization Geneva Switzerland Institute of Environmental Sciences University of Geneva Geneva Switzerland Cardiovascular Epidemiology Unit Department of Public Health & Primary Care University of Cambridge Cambridge UK School of Government University of Birmingham Birmingham UK Economic Analysis of Climate Impacts and Policy Division Centro Euro-Mediterraneo sui Cambiamenti Climatici Venice Italy Institute for Environmental Design and Engineering University College London London UK Natural Resources Institute University of Greenwich London UK Department of Environmental Health Sciences and Yale Center on Climate Change and Health Yale University New Haven CT USA Department of Civil and Environmental Engineering Northeastern University Boston MA USA Department of Fish and Wildlife Conservation Virginia Polytechnic Institute and State University Blacksburg VA USA Department of Geography University College London London UK NU

With advancements in the science of detection and attribution studies, the influence of climate change over many events has now been quantified. Because of the rapidly increasing temperatures, vulnerable populations (adults older than 65 years, and children younger than one year of age) were exposed to 3·7 billion more heatwave days in 2021 than annually in 1986–2005 (indicator 1.1.2), and heat-related deaths increased by 68% between 2000–04 and 2017–21 (indicator 1.1.5), a death toll that was significantly exacerbated by the confluence of the COVID-19 pandemic. Despite the local cooling and overall health benefits of urban greenspaces, only 277 (27%) of 1038 global urban centres were at least moderately green in 2021 (indicator 2.2.3), and the number of households with air conditioning increased by 66% from 2000 to 2020, a maladaptive response that worsens the energy crisis and further increases urban heat, air pollution, and greenhouse gas emissions. [...]millions of people do not have access to the energy needed to keep their homes at healthy temperatures, preserve food and medication, and meet the seventh Sustainable Development Goal (to ensure access to affordable, reliable, sustainable, and modern energy for all). Aggravating this situation even further, governments continue to incentivise fossil fuel production and consumption: 69 (80%) of 86 countries reviewed had net-negative carbon prices (ie, provided a net subsidy to fossil fuels) for a net total of US$400 billion

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Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

arXiv

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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

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On the instability of further pre-training: Does a single sentence matter to BERT?

Natural Language Processing Journal

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Natural Language Processing Journal 2023年 5卷

作者： Luca Bacco Gosse Minnema Tommaso Caselli Felice Dell’Orletta Mario Merone Malvina Nissim Unit of Computer Systems and Bioinformatics Department of Engineering University “Campus Bio-Medico di Roma” Via Alvaro del Portillo 21 Rome 00128 Italy ItaliaNLP Lab Institute of Computational Linguistics “Antonio Zampolli” National Research Council Via Giuseppe Moruzzi 1 Pisa 56124 Italy Center for Language and Cognition Faculty of Arts University of Groningen Oude Kijk in’t Jatstraat 26 Groningen 9712 EK The Netherlands

We observe a remarkable instability in BERT-like models: minimal changes in the internal representations of BERT, as induced by one-step further pre-training with even a single sentence, can noticeably change the behaviour of subsequently fine-tuned models. While the pre-trained models seem to be essentially the same, also by means of established similarity assessment techniques, the measurable tiny changes appear to substantially impact the models’ tuning path, leading to significantly different fine-tuned systems and affecting downstream performance. After testing a very large number of combinations, which we briefly summarize, the experiments reported in this short paper focus on an intermediate phase consisting of a single-step and single-sentence masked language modeling stage and its impact on a sentiment analysis task. We discuss a series of unexpected findings which leave some open questions over the nature and stability of further pre-training.

关键词： Natural language processing Large language models Further pre-training Transformers Instability BERT

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Single perceptron at 12 GigaOPs based on a microcomb for versatile, high-speed scalable, optical neural networks 23

Single perceptron at 12 GigaOPs based on a microcomb for ver...

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Smart Photonic and Optoelectronic Integrated Circuits XXIII 2021

作者： Tan, M. Xu, X. Wu, J. Boes, A. Corcoran, B. Nguyen, T.G. Chu, S.T. Little, B.E. Morandotti, R. Mitchell, A. Hicks, D.G. Moss, D.J. Optical Sciences Centre Swinburne University of Technology HawthornVIC3122 Australia Dept. of Electrical and Computer Systems Engineering Monash University ClaytonVIC3800 Australia School of Engineering RMIT University MelbourneVIC3001 Australia Department of Physics and Material Science City University of Hong Kong Tat Chee Avenue Hong Kong Hong Kong Xi'An Institute of Optics and Precision Mechanics Precision Mechanics CAS Xi'an China INRS-Énergie Matériaux et Télécommunications 1650 Boulevard Lionel-Boulet VarennesQCJ3X1S2 Canada Adjunct Frontier Sciences University of Electronic Science and Technology of China Chengdu610054 China Bioinformatics Division Walter and Eliza Hall Institute of Medical Research ParkvilleVIC3052 Australia

ISBN: (数字)9781510642164

ISBN: (纸本)9781510642157

Optical artificial neural networks (ONNs) have significant potential for ultra-high computing speed and energy efficiency. We report a novel approach to ONNs that uses integrated Kerr optical micro-combs. This approach is programmable and scalable and is capable of reaching ultra-high speeds. We demonstrate the basic building block ONNs- A single neuron perceptron-by mapping synapses onto 49 wavelengths to achieve an operating speed of 11.9 x 109 operations per second, or Giga-OPS, at 8 bits per operation, which equates to 95.2 gigabits/s (Gbps). We test the perceptron on handwritten-digit recognition and cancer-cell detection- A chieving over 90% and 85% accuracy, respectively. By scaling the perceptron to a deep learning network using off-the-shelf telecom technology we can achieve high throughput operation for matrix multiplication for real-time massive data processing. © 2021 SPIE.

关键词： Data handling

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The Importance of Exploration and Exploitation Innovation in Emerging Economies

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Journal of Open Innovation: Technology, Market, and Complexity 2022年第3期8卷

作者： Moreira, António Carrizo Navaia, Eurico Colarinho Ribau, Cláudia Department of Economics Management Industrial Engineering and Tourism University of Aveiro Aveiro 3810-193 Portugal GOVCOPP—Research Unit on Governance Competitiveness and Public Policies University of Aveiro Aveiro 3810-193 Portugal INESCTEC—Institute for Systems and Computer Engineering Technology and Science Porto 4200-465 Portugal Faculdade de Ciências Sociais e Humanidades Universidade Zambeze Beira 369 Sofala Mozambique Instituto Superior de Contabilidade e Administração University of Aveiro Aveiro 3810-193 Portugal

Innovation capabilities are among the main driving sources of export performance;however, the literature on how exploration and exploitation innovation influence export performance in the context of emerging economies is scarce. As such, the purpose of this paper is to assess the relationship between innovation capabilities and export performance, mediated by exploration and exploitation innovation. For that, an online questionnaire was implemented surveying 250 Mozambican Small and Medium Enterprises (SMEs) in the agro-industry, agro-processing, and fishing sectors. Based on a Partial Least Squares Structural Equation Model (PLS-SEM) relating innovation capabilities, exploitation, and exploration innovation to export performance, it is possible to state that innovation capabilities influence the export performance of SMEs in this emerging country. Moreover, exploration and exploitation innovation exert important mediation effects, the latter being more prevalent than the former. The results constitute a major contribution as it is possible to claim that, in the context of emerging economies, Mozambican SMEs have managed to enhance their export performance, based on innovation capabilities, but, also, to master their ambidexterity with exploiting capabilities, normally linked to their current technological trajectories, being more prevalent that exploitation capabilities, which are focused on the pursuit of radical innovation. © 2022 by the authors.

关键词： exploitation innovation exploration innovation export performance innovation capabilities internationalization Mozambique PLS-SEM SMEs

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Combined effects of host genetics and diet on human gut microbiota and incident disease in a single population cohort (vol 54, pg 134, 2022)

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NATURE GENETICS 2024年第3期56卷 554-554页

作者： Qin, Youwen Havulinna, Aki S. Liu, Yang Jousilahti, Pekka Ritchie, Scott C. Tokolyi, Alex Sanders, Jon G. Valsta, Liisa Brozynska, Marta Zhu, Qiyun Tripathi, Anupriya Vazquez-Baeza, Yoshiki Loomba, Rohit Cheng, Susan Jain, Mohit Niiranen, Teemu Lahti, Leo Knight, Rob Salomaa, Veikko Inouye, Michael Meric, Guillaume Cambridge Baker Systems Genomics Initiative Baker Heart and Diabetes Institute Melbourne Victoria Australia School of BioSciences The University of Melbourne Melbourne Victoria Australia Department of Clinical Pathology Melbourne Medical School The University of Melbourne Melbourne Victoria Australia Cambridge Baker Systems Genomics Initiative Department of Public Health and Primary Care University of Cambridge Cambridge UK British Heart Foundation Centre of Research Excellence University of Cambridge Cambridge UK British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care University of Cambridge Cambridge UK Health Data Research UK Cambridge Wellcome Genome Campus & University of Cambridge Cambridge UK The Alan Turing Institute London UK Department of Infectious Diseases Central Clinical School Monash University Melbourne Victoria Australia Department of Public Health and Welfare Finnish Institute for Health and Welfare Helsinki Finland Institute for Molecular Medicine Finland FIMM-HiLIFE Helsinki Finland Department of Medicine Turku University Hospital and University of Turku Turku Finland Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Ecology and Evolutionary Biology Cornell University Ithaca NY USA Cornell Institute for Host-Microbe Interaction and Disease Cornell University Ithaca NY USA Department of Pediatrics School of Medicine University of California San Diego La Jolla CA USA Division of Biological Sciences University of California San Diego La Jolla CA USA Center for Microbiome Innovation University of California San Diego La Jolla CA USA Department of Computer Science & Engineering Jacobs School of Engineering University of California San Diego La Jolla CA USA NAFLD Research Center Department of Medicine University of California San Diego La Jolla CA USA Smidt Heart Institute Cedars-Sinai Medical Center Los Angeles CA USA Department of Computing Univer

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AI drives the assessment of lung cancer microenvironment composition

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Journal of Pathology Informatics 2024年 15卷 100400-100400页

作者： Gallo, Enzo Guardiani, Davide Betti, Martina Arteni, Brindusa Ana Maria Di Martino, Simona Baldinelli, Sara Daralioti, Theodora Merenda, Elisabetta Ascione, Andrea Visca, Paolo Pescarmona, Edoardo Lavitrano, Marialuisa Nisticò, Paola Ciliberto, Gennaro Pallocca, Matteo Department of Pathology IRCCS Regina Elena National Cancer Institute Rome Italy Biostatistics Bioinformatics and Clinical Trial Center IRCCS Regina Elena National Cancer Institute Rome Italy Department of Computer Control and Management Engineering La Sapienza University of Rome Rome Italy UOC Anatomy Pathology Biobank IRCCS Regina Elena National Cancer Institute Istituti Fisioterapici Ospitalieri IFO Rome Italy Department of Radiological Oncological and Pathological Sciences Sapienza University of Rome Policlinico Umberto I Rome Italy Department of Experimental Medicine Sapienza University of Rome Policlinico Umberto I Rome Italy School of Medicine and Surgery University of Milano-Bicocca Monza 20900 Italy Tumor Immunology and Immunotherapy Unit IRCCS Regina Elena National Cancer Institute Rome Italy Scientific Direction IRCCS Regina Elena National Cancer Institute Rome Italy Institute of Experimental Endocrinology and Oncology National Research Council Naples Italy

Purpose: The abundance and distribution of tumor-infiltrating lymphocytes (TILs) as well as that of other components of the tumor microenvironment is of particular importance for predicting response to immunotherapy in lung cancer (LC). We describe here a pilot study employing artificial intelligence (AI) in the assessment of TILs and other cell populations, intending to reduce the inter- or intra-observer variability that commonly characterizes this evaluation. Design: We developed a machine learning-based classifier to detect tumor, immune, and stromal cells on hematoxylin and eosin-stained sections, using the open-source framework QuPath. We evaluated the quantity of the aforementioned three cell populations among 37 LC whole slide images regions of interest, comparing the assessments made by five pathologists, both before and after using graphical predictions made by AI, for a total of 1110 quantitative measurements. Results: Our findings indicate noteworthy variations in score distribution among pathologists and between individual pathologists and AI. The AI-guided pathologist's evaluations resulted in reduction of significant discrepancies across pathologists: three comparisons showed a loss of significance (p > 0.05), whereas other four showed a reduction in significance (p > 0.01). Conclusions: We show that employing a machine learning approach in cell population quantification reduces inter- and intra-observer variability, improving reproducibility and facilitating its use in further validation studies. © 2024 The Authors

关键词： computer-aided tool Digital pathology Lung cancer Machine learning NSCLC Pathology image QuPath Tumor-infiltrating lymphocytes Whole slide images

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Applying the FAIR Principles to computational workflows

arXiv

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arXiv 2024年

作者： Wilkinson, Sean R. Aloqalaa, Meznah Belhajjame, Khalid Crusoe, Michael R. de Paula Kinoshita, Bruno Gadelha, Luiz Garijo, Daniel Ragnar Gustafsson, Ove Johan Juty, Nick Kanwal, Sehrish Khan, Farah Zaib Köster, Johannes Gehlen, Karsten Petersvon Pouchard, Line Rannow, Randy K. Soiland-Reyes, Stian Soranzo, Nicola Sufi, Shoaib Sun, Ziheng Vilne, Baiba Wouters, Merridee A. Yuen, Denis Goble, Carole Oak Ridge Leadership Computing Facility Oak Ridge National Laboratory Oak RidgeTN United States Department of Computer Science University of Manchester Manchester United Kingdom LAMSADE PSL Paris Dauphine University Paris France Berlin Germany Earth Sciences Barcelona Supercomputing Center Barcelona Spain Heidelberg Germany Ontology Engineering Group Universidad Politécnica de Madrid Madrid Spain Australian BioCommons University of Melbourne MelbourneVIC Australia ParkvilleVIC Australia University Medicine Essen University of Duisburg-Essen Essen Germany Data Management Department Deutsches Klimarechenzentrum GmbH Hamburg Germany Center for Computing Research Sandia National Laboratories AlbuquerqueNM United States Silverdraft Supercomputing BoiseID United States Informatics Institute University of Amsterdam Amsterdam Netherlands Earlham Institute Norwich United Kingdom Center for Spatial Information Science and Systems Department of Geography and Geoinformation Science George Mason University FairfaxVA United States Bioinformatics Group Riga Stradins University Riga Latvia School of Clinical Medicine University of New South Wales KensingtonNSW Australia Ontario Institute for Cancer Research TorontoON Canada

Recent trends within computational and data sciences show an increasing recognition and adoption of computational workflows as tools for productivity and reproducibility that also democratize access to platforms and processing know-how. As digital objects to be shared, discovered, and reused, computational workflows benefit from the FAIR principles, which stand for Findable, Accessible, Interoperable, and Reusable. The Workflows Community Initiative’s FAIR Workflows Working Group (WCI-FW), a global and open community of researchers and developers working with computational workflows across disciplines and domains, has systematically addressed the application of both FAIR data and software principles to computational workflows. We present recommendations with commentary that reflects our discussions and justifies our choices and adaptations. These are offered to workflow users and authors, workflow management system developers, and providers of workflow services as guidelines for adoption and fodder for discussion. The FAIR recommendations for workflows that we propose in this paper will maximize their value as research assets and facilitate their adoption by the wider community. © 2024, CC BY.

关键词： Application programs

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An Open-Source Knowledge Graph Ecosystem for the Life Sciences

arXiv

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arXiv 2023年

作者： Callahan, Tiffany J. Tripodi, Ignacio J. Stefanski, Adrianne L. Cappelletti, Luca Taneja, Sanya B. Wyrwa, Jordan M. Casiraghi, Elena Matentzoglu, Nicolas A. Reese, Justin Silverstein, Jonathan C. Hoyt, Charles Tapley Boyce, Richard D. Malec, Scott A. Unni, Deepak R. Joachimiak, Marcin P. Robinson, Peter N. Mungall, Christopher J. Cavalleri, Emanuele Fontana, Tommaso Valentini, Giorgio Mesiti, Marco Gillenwater, Lucas A. Santangelo, Brook Vasilevsky, Nicole A. Hoehndorf, Robert Bennett, Tellen D. Ryan, Patrick B. Hripcsak, George Kahn, Michael G. Bada, Michael Baumgartner, William A. Hunter, Lawrence E. Computational Bioscience Program University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY10032 United States Computer Science Department Interdisciplinary Quantitative Biology University of Colorado Boulder BoulderCO80301 United States AnacletoLab Computer Science Department University of Milan 20122 Italy Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Department of Physical Medicine and Rehabilitation School of Medicine University of Colorado Anschutz Medical Campus AuroraCO80045 United States Division of Environmental Genomics and Systems Biology Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Semanticly Ltd Athens Greece Department of Biomedical Informatics University of Pittsburgh School of Medicine PittsburghPA15206 United States Laboratory of Systems Pharmacology Harvard Medical School BostonMA02115 United States Division of Translational Informatics University of New Mexico School of Medicine AlbuquerqueNM87131 United States SIB Swiss Institute of Bioinformatics Basel Switzerland Berlin Institute of Health at Charité-Universitatsmedizin Berlin10117 Germany ELLIS European Laboratory for Learning and Intelligent Systems Germany Department of Biomedical Informatics University of Colorado School of Medicine AuroraCO80045 United States Data Collaboration Center Critical Path Institute 1840 E River Rd. Suite 100 TucsonAZ85718 United States Computer Electrical and Mathematical Sciences & Engineering Division Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Pediatrics University of Colorado School of Medicine AuroraCO80045 United States Janssen Research and Development RaritanNJ08869 United States Division of General Internal Medicine University of Colorado School of Medicine A

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoints and abstraction algorithms), and benchmarks (e.g., prebuilt KGs and embeddings). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability. © 2023, CC BY.

关键词： Ecosystems

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Toward building next-generation Geocoding systems: a systematic review

arXiv

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arXiv 2025年

作者： Yin, Zhengcong Goldberg, Daniel W. Lin, Binbin Zhou, Bing Li, Diya Ma, Andong Ming, Ziqian Cai, Heng Zhang, Zhe Wang, Shaohua Gao, Shanzhen Lee, Joey Ying Li, Xiao Huo, Da Department of Geography Texas A&M University 797 Lamar St. College StationTX77840 United States Department of Geography Pennsylvania State University 201 Old Main University ParkPA16802 United States Department of Earth and Atmospheric Sciences Metropolitan State University of Denver Science Building 2014 DenverCO80217 United States Esri Inc 380 New York St. RedlandsCA92373 United States State Key Laboratory of Remote Sensing and Digital Earth Aerospace Information Research Institute Chinese Academy of Sciences No.9 Dengzhuang South Rd. Beijing China Department of Computer Information Systems Virginia State University 1 Hayden St. PetersburgVA23806 United States LABI Education 143 Keelung Rd. Taipei Taiwan Transport Studies Unit University of Oxford South Parks Rd. OxfordOX1 3QY United Kingdom Department of Civil & Mineral Engineering University of Toronto 35 St George St. TorontoON Canada

Geocoding systems are widely used in both scientific research for spatial analysis and everyday life through location-based services. The quality of geocoded data significantly impacts subsequent processes and applications, underscoring the need for next-generation systems. In response to this demand, this review first examines the evolving requirements for geocoding inputs and outputs across various scenarios these systems must address. It then provides a detailed analysis of how to construct such systems by breaking them down into key functional components and reviewing a broad spectrum of existing approaches, from traditional rule-based methods to advanced techniques in information retrieval, natural language processing, and large language models. Finally, we identify opportunities to improve next-generation geocoding systems in light of recent technological advances. © 2025, CC BY.

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