众包模式在大规模遥感影像信息提取领域的探索

赵江华; 王学志; 林青慧; 黎建辉; 周园春

doi:10.11959/j.issn.2096-0271.2016066

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众包模式在大规模遥感影像信息提取领域的探索

专题：科学数据与创新应用 | 更新时间：2024-06-03

- 众包模式在大规模遥感影像信息提取领域的探索
- Exploration of crowdsourcing in information extraction from remote sensing images
- 大数据 2016年2卷第6期页码：2016066
- 作者机构：
  
  1. 中国科学院计算机网络信息中心，北京 100190
  2. 中国科学院大学，北京 100049
- 作者简介：
  
  [ "赵江华(1989-)，女，中国科学院计算机网络信息中心研究实习员，主要研究方向为大数据分析与处理、数据挖掘。" ]
  [ "王学志（1979-），男，中国科学院计算机网络信息中心副研究员，主要研究方向为海量时空数据处理与分析。" ]
  [ "林青慧（1979-），女，中国科学院计算机网络信息中心副研究员，主要研究方向为大数据资源汇聚、分析、共享及服务。" ]
  [ "黎建辉（1973-），男，中国科学院计算机网络信息中心研究员、博士生导师，主要研究方向为大数据管理、大数据分析与处理。" ]
  [ "周园春（1975-），男，中国科学院计算机网络信息中心研究员、博士生导师，主要研究方向为数据挖掘、大数据分析与处理。" ]
- 基金信息：
  
  国家重点研发计划基金资助项目“科学大数据管理系统”;The National Key Research Program of China “Scientific Big Data Management System”(2016YFB1000600);国家重点研发计划基金资助项目“协同精密定位技术”;The National Key Research Program of China “Collaborative Precision Positioning Project”(2016YFB0501900)
- DOI：10.11959/j.issn.2096-0271.2016066
  中图分类号： TP79
- 网络首发：2016-11，
  
  纸质出版：2016-11-20
- 稿件说明：
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赵江华, 王学志, 林青慧, 等. 众包模式在大规模遥感影像信息提取领域的探索[J]. 大数据, 2016,2(6):2016066.

Jianghua ZHAO, Xuezhi WANG, Qinghui LIN, et al. Exploration of crowdsourcing in information extraction from remote sensing images[J]. Big data research, 2016, 2(6): 2016066.
赵江华, 王学志, 林青慧, 等. 众包模式在大规模遥感影像信息提取领域的探索[J]. 大数据, 2016,2(6):2016066. DOI： 10.11959/j.issn.2096-0271.2016066.

Jianghua ZHAO, Xuezhi WANG, Qinghui LIN, et al. Exploration of crowdsourcing in information extraction from remote sensing images[J]. Big data research, 2016, 2(6): 2016066. DOI： 10.11959/j.issn.2096-0271.2016066.

摘要

基于地理空间数据云平台，对基于众包的大规模遥感影像信息提取模式进行了研究，提出了一套完整的流程体系，并通过多时期的青藏高原湖泊提取任务对模式的报酬发放机制、任务分配方式、任务划分方法、人才激励等领域进行了探索和完善。实验结果表明提前支付部分报酬并采用小组的方式，对提高数据质量和控制并没有很大影响，而积累人才对获取高质量的数据结果很重要。由于该模式集成了众包和机器计算能力，且对遥感影像处理是通用的，因此可用于更多的需要人工参与的海量遥感影像处理工作中。

Abstract

Based on geospatial data cloud(GSCloud)

the application of crowdsourcing in large scale information extraction from satellite images was studied

and a systematic architecture of this paradigm was proposed.By performing an experiment of extracting lakes on Qinghai-Tibetan plateau from landsat images

various aspects of the paradigm like the incentive mechanism

task assignment method

task division and many others were explored.Results show that paying part of the reward in advance and assigning a task to a team instead of individuals do not help attracting more applicants and improving the quality of results.And the accumulation of talents is of critical importance to obtain high-quality task results.Since this paradigm integrates crowdsourcing and machine computing power

and it is generic

it can be applied in more massive remote sensing image processing work which requires much human intervention.

关键词

Keywords

references

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