Cross-level and multiscale CNN-Transformer network for automatic building extraction from remote sensing imagery

IF 3 3区地球科学 Q2 IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY

International Journal of Remote Sensing Pub Date : 2024-04-17 DOI:10.1080/01431161.2024.2339199

Qinglie Yuan, Bin Xia

引用次数: 0

Abstract

Automatic building detection from remote sensing images holds significant applications in various domains, including cartography, land-use change detection, urban planning, and 3D intelligent city ...

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用于从遥感图像中自动提取建筑物的跨级别和多尺度 CNN-Transformer 网络

从遥感图像中自动检测建筑物在制图、土地利用变化检测、城市规划和三维智能城市等多个领域都有重要应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Remote Sensing 工程技术-成像科学与照相技术

CiteScore

7.00

自引率

5.90%

发文量

219

审稿时长

4.8 months

期刊介绍： The International Journal of Remote Sensing ( IJRS) is concerned with the theory, science and technology of remote sensing and novel applications of remotely sensed data. The journal’s focus includes remote sensing of the atmosphere, biosphere, cryosphere and the terrestrial earth, as well as human modifications to the earth system. Principal topics include: • Remotely sensed data collection, analysis, interpretation and display. • Surveying from space, air, water and ground platforms. • Imaging and related sensors. • Image processing. • Use of remotely sensed data. • Economic surveys and cost-benefit analyses. • Drones Section: Remote sensing with unmanned aerial systems (UASs, also known as unmanned aerial vehicles (UAVs), or drones).