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Preface

机译:前言

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摘要

It is our great pleasure to welcome you to the proceedings of the First International Workshop on Internet and Social media for Environmental Monitoring (ISEM) 2016 held in conjunction with the Third international conference on Internet Science (INSCI 2016), Florence, Italy. The aim of ISEM 2016 was to present the most recent works in the area of environmental monitoring based on Web resources and user-generated content from social media (e.g., photos of the sky). The advancements in digital technologies and the high penetration of the Internet have facilitated the sharing of environmental information, such as meteorological measurements and observations of natural surroundings. Since the analysis of environmental information is critical both for human activities (e.g., agriculture, deforestation) and for the sustainability of the planet (e.g., nature conservation, green living, eco-driving, etc.), it is of great importance to develop techniques for the retrieval and aggregation of environmental information that is available over the Internet. Of particular interest is the exploitation of user-generated content, which, despite being of inconsistent quality in many cases, could contribute important information regarding areas that are not monitored by existing stations. In this context, ISEM 2016 focused on analysis, retrieval, and aggregation of environmental data from the Internet and user-generated content posted on social media, as well as on personalized services and decision support environmental applications (e.g., to suggest outdoor activities based on the current environmental conditions).
机译:我们非常高兴地欢迎您参加在意大利佛罗伦萨举行的2016年第一届国际互联网和社交媒体环境监测国际研讨会(ISEM)以及第三届国际互联网科学会议(INSCI 2016)的会议记录。 ISEM 2016的目的是根据网络资源和用户从社交媒体生成的内容(例如,天空照片)展示环境监测领域的最新作品。数字技术的进步和互联网的高度普及促进了环境信息的共享,例如气象测量和对自然环境的观测。由于对环境信息的分析对于人类活动(例如农业,森林砍伐)和地球的可持续性(例如自然保护,绿色生活,生态驾驶等)都是至关重要的,因此发展环境至关重要用于检索和汇总Internet上可用的环境信息的技术。特别引起关注的是对用户生成内容的利用,尽管在许多情况下质量不一致,但仍可提供有关现有站未监控的区域的重要信息。在此背景下,ISEM 2016专注于分析,检索和汇总来自互联网的环境数据以及在社交媒体上发布的用户生成的内容,以及个性化服务和决策支持环境应用程序(例如,建议基于当前的环境条件)。

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