在极端范围差异背景下估算社会经济普查指标的多瞥深度学习架构

IF 4.3 1区地球科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

International Journal of Geographical Information Science Pub Date : 2024-02-01 DOI:10.1080/13658816.2024.2305636

Dan Runfola, Anthony Stefanidis, Zhonghui Lv, Joseph O’Brien, Heather Baier

引用次数: 0

摘要

卷积神经网络（CNN）被广泛用于卫星图像信息提取任务。然而，对于那些需要估算高度复杂的卫星图像中的综合信息的任务，卷积神经网络（CNNs）却显得力不从心。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

A multi-glimpse deep learning architecture to estimate socioeconomic census metrics in the context of extreme scope variance

Convolutional Neural Networks (CNNs) are leveraged for a wide range of satellite imagery information extraction tasks. However, for tasks which seek to estimate aggregated information across highly...

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

International Journal of Geographical Information Science 地学-计算机：信息系统

CiteScore

11.00

自引率

7.00%

发文量

审稿时长

9 months

期刊介绍： International Journal of Geographical Information Science provides a forum for the exchange of original ideas, approaches, methods and experiences in the rapidly growing field of geographical information science (GIScience). It is intended to interest those who research fundamental and computational issues of geographic information, as well as issues related to the design, implementation and use of geographical information for monitoring, prediction and decision making. Published research covers innovations in GIScience and novel applications of GIScience in natural resources, social systems and the built environment, as well as relevant developments in computer science, cartography, surveying, geography and engineering in both developed and developing countries.