The use of cloud based imagery services for understanding landscape change in support of opium monitoring in Afghanistan

使用基于云的图像服务了解景观变化以支持阿富汗的鸦片监测

• 批准号: 1948488
• 金额: --
• 依托单位: CRANFIELD UNIVERSITY
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1948488
• 关键词: use; cloud; based; imagery; services

Information on illicit poppy cultivation in Afghanistan is of critical importance to the opium monitoring programme of the United Nations Office on Drugs and Crime (UNODC). The pattern of cultivation is constantly evolving because of environmental pressures, such as water availability, and social and economic drivers related to counter narcotics activity. Remote sensing already plays a key role in gathering information on the area of opium cultivation and its spatial distribution. The shift to cloud computing opens up exciting possibilities for extracting new information from the huge amounts of satellite data from long-term earth observation programmes. You will test the hypothesis that inter-annual and intra-seasonal changes in vegetation growth cycles are predictors of poppy cultivation risk. This will involve using emerging cloud based technologies for processing image data into accurate and timely information on vegetation dynamics relating to opium cultivation.

关于阿富汗非法罂粟种植的信息对于联合国毒品和犯罪问题办事处（毒品和犯罪问题办事处）的鸦片监测方案至关重要。由于水供应等环境压力以及与禁毒活动有关的社会和经济驱动因素，种植模式不断演变。遥感在收集关于鸦片种植面积及其空间分布的信息方面已经发挥了关键作用。向云计算的转变为从长期地球观测计划的大量卫星数据中提取新信息提供了令人兴奋的可能性。你将检验假设，即植被生长周期的年际和季节内变化是罂粟种植风险的预测因子。这将涉及利用新兴的云技术将图像数据处理成与鸦片种植有关的植被动态的准确和及时的信息。

项目成果

Replacing human interpretation of agricultural land in Afghanistan with a deep convolutional neural network

• DOI: 10.1080/01431161.2020.1864059
• 发表时间: 2021-04-18
• 期刊: INTERNATIONAL JOURNAL OF REMOTE SENSING
• 影响因子: 3.4
• 作者: Hamer, A. M.;Simms, D. M.;Waine, T. W.
• 通讯作者: Waine, T. W.

Using deep learning to transfer knowledge between satellite datasets for automated agricultural land discrimination in Afghanistan

利用深度学习在卫星数据集之间传输知识，以实现阿富汗自动农业土地歧视

• DOI: 10.5194/egusphere-egu2020-9243
• 发表时间: 2020
• 作者: Hamer A