用于排污口勘测深度学习的无人机系统基准物体检测数据集

IF 3.7 1区地球科学 Q1 GEOGRAPHY, PHYSICAL

International Journal of Digital Earth Pub Date : 2024-08-13 DOI:10.1080/17538947.2024.2390443

Chengbin Wu, Yaohuan Huang, Haijun Yang, Ling Yao, Yesen Liu, Zhuo Chen

引用次数: 0

摘要

基于深度学习的无人机系统图像目标检测对于排污口调查和流域环境保护至关重要。全面的无人机系统图像数据集为创建基于深度学习的无人机系统图像目标检测奠定了基础。

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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An unmanned aerial system benchmark object detection dataset for deep learning in outfall surveys

Deep-learning-based object detection in UAS imagery is crucial for outfall surveys and basin environmental protection. Comprehensive UAS image datasets serve as a foundation for creating deep-learn...

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来源期刊

International Journal of Digital Earth 地学-遥感

CiteScore

6.50

自引率

3.90%

发文量

审稿时长

3 months

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.