Coupling video vision transformer (ViVit) into land change simulation: a comparison with three-dimensional convolutional neural network (3DCNN)

IF 1 4区地球科学 Q4 GEOGRAPHY, PHYSICAL

Journal of Spatial Science Pub Date : 2024-02-26 DOI:10.1080/14498596.2024.2312506

Haiyang Li, Liang Fan, Yifan Gao, Zhao Liu, Peichao Gao

引用次数: 0

Abstract

To enhance land use/cover change (LUCC) simulation accuracy, we introduced ViViT-ANN-CA, blending video vision transformer’s spatio-temporal features extraction ability, artificial neural network‘s...

查看原文本刊更多论文

将视频视觉转换器（ViVit）与土地变化模拟相结合：与三维卷积神经网络（3DCNN）的比较

为了提高土地利用/覆盖变化（LUCC）模拟的准确性，我们引入了 ViViT-ANN-CA，它融合了视频视觉转换器的时空特征提取能力、人工神经网络的...

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of Spatial Science 地学-地质学

CiteScore

5.00

自引率

5.30%

发文量

审稿时长

>12 weeks

期刊介绍： The Journal of Spatial Science publishes papers broadly across the spatial sciences including such areas as cartography, geodesy, geographic information science, hydrography, digital image analysis and photogrammetry, remote sensing, surveying and related areas. Two types of papers are published by he journal: Research Papers and Professional Papers. Research Papers (including reviews) are peer-reviewed and must meet a minimum standard of making a contribution to the knowledge base of an area of the spatial sciences. This can be achieved through the empirical or theoretical contribution to knowledge that produces significant new outcomes. It is anticipated that Professional Papers will be written by industry practitioners. Professional Papers describe innovative aspects of professional practise and applications that advance the development of the spatial industry.