{"title":"利用弱监督学习进行规模效应感知的自下而上种群估计","authors":"Jing Xia, Rui Li, Xinrui Liu, Guangyu Liu, Mingjun Peng","doi":"10.1080/17538947.2024.2341788","DOIUrl":null,"url":null,"abstract":"Fine-scale population estimation (FPE) is crucial for urban management. After training, the bottom-up FPE models can be applied independently of census data. However, given the lack of real fine-sc...","PeriodicalId":54962,"journal":{"name":"International Journal of Digital Earth","volume":"68 1","pages":""},"PeriodicalIF":3.7000,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Scale effects-aware bottom-up population estimation using weakly supervised learning\",\"authors\":\"Jing Xia, Rui Li, Xinrui Liu, Guangyu Liu, Mingjun Peng\",\"doi\":\"10.1080/17538947.2024.2341788\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Fine-scale population estimation (FPE) is crucial for urban management. After training, the bottom-up FPE models can be applied independently of census data. However, given the lack of real fine-sc...\",\"PeriodicalId\":54962,\"journal\":{\"name\":\"International Journal of Digital Earth\",\"volume\":\"68 1\",\"pages\":\"\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-04-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Digital Earth\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1080/17538947.2024.2341788\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GEOGRAPHY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Digital Earth","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1080/17538947.2024.2341788","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOGRAPHY, PHYSICAL","Score":null,"Total":0}
Scale effects-aware bottom-up population estimation using weakly supervised learning
Fine-scale population estimation (FPE) is crucial for urban management. After training, the bottom-up FPE models can be applied independently of census data. However, given the lack of real fine-sc...
期刊介绍：
The International Journal of Digital Earth is a response to this initiative. This peer-reviewed academic journal (SCI-E) focuses on the theories, technologies, applications, and societal implications of Digital Earth and those visionary concepts that will enable a modeled virtual world. The journal encourages papers that:
Progress visions for Digital Earth frameworks, policies, and standards;
Explore geographically referenced 3D, 4D, or 5D models to represent the real planet, and geo-data-intensive science and discovery;
Develop methods that turn all forms of geo-referenced data, from scientific to social, into useful information that can be analyzed, visualized, and shared;
Present innovative, operational applications and pilots of Digital Earth technologies at a local, national, regional, and global level;
Expand the role of Digital Earth in the fields of Earth science, including climate change, adaptation and health related issues,natural disasters, new energy sources, agricultural and food security, and urban planning;
Foster the use of web-based public-domain platforms, social networks, and location-based services for the sharing of digital data, models, and information about the virtual Earth; and
Explore the role of social media and citizen-provided data in generating geo-referenced information in the spatial sciences and technologies.
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