SBIR Phase I: Building a Global Community to Crowdsource-Clean the Planet

SBIR 第一阶段：建立一个众包清洁地球的全球社区

• 批准号: 1746758
• 负责人: Jeff Kirschner
• 金额: $ 22.5万
• 依托单位: Litterati Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-01-01 至 2018-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1746758&HistoricalAwards=false
• 关键词: SBIR; Phase; Building; Global; Community

The broader impact/commercial potential of this SBIR Phase I project stems from developing a crowdsourced solution to litter - one of the world's most pervasive and toxic problems. To many, it's dirty, disgusting, and someone else's problem to solve. Unfortunately, we all suffer the consequences, as litter impacts our economy, degrades the environment, demoralizes community pride, kills wildlife, and poisons the food system. This project aims to develop a mobile technology that empowers anyone to identify, map, and collect the world?s litter, while simultaneously connecting to a broader community of associated brands, cities, schools. By crowdsourcing the data and cleanup, there is great potential in collecting massive amounts of information which can be used for everything from infrastructure improvement, to resource allocation, brand packaging redesign, and even individual responsibility and behavioral change.The intellectual merit of this project is derived from building a global database of litter. And one critical need to achieving such a monumental task is the ability to quickly (and accurately) identify any piece of litter, anytime, anywhere even if the litter is in a deep state of decay and decomposition. This project further aims to integrate that information with other data sets including location, time, proximity to schools, and the watershed. By leveraging technologies such as image recognition and machine learning, the project will further empower the people who are crowdsourcing the data and cleanup to collect a vast amount of identifiable information.

该SBIR I期项目的更广泛的影响/商业潜力源于为世界上最普遍和有毒的问题开发众包解决方案之一。对许多人来说，这是肮脏，令人作呕的，还有别人的问题要解决。不幸的是，我们所有人都遭受了后果，因为垃圾影响了我们的经济，使环境降低，使社区自豪感，杀死野生动植物并毒害食品体系。该项目旨在开发一种移动技术，使任何人都能识别，映射和收集世界的垃圾，同时连接到一个更广泛的相关品牌，城市，学校。通过众包数据和清理，收集大量信息具有很大的潜力，这些信息可用于从基础架构改进到资源分配，品牌包装重新设计，甚至个人责任和行为改变。完成这样一项艰巨的任务的一种关键需求是，即使垃圾处于腐烂和分解的深度状态，也可以随时随地快速（并准确地）识别任何垃圾的能力。该项目进一步旨在将这些信息与其他数据集集成在一起，包括位置，时间，与学校的邻近性以及分水岭。通过利用图像识别和机器学习等技术，该项目将进一步授权众包数据和清理的人收集大量可识别信息。