2019 IEEE International Geoscience and Remote Sensing Symposium(IGARSS 2019)

来源:IGARSS 2019

[基本信息]

会议名称:2019 IEEE International Geoscience and Remote Sensing Symposium(IGARSS 2019)

开始日期:2019-07-28

结束日期:2019-08-02

所在国家:Japan

所在城市:Yokohama

具体地点:Pacifico Yokohama

[会务组联系方式]

会议网站:https://www.igarss2019.org/Default.asp

[会议背景介绍]

On behalf of the IEEE Geoscience and Remote Sensing Society and the IGARSS 2019 Organizing Committee, we are pleased to invite you to Yokohama, Japan for IGARSS 2019that will be held from Sunday July 28th through Friday August 2nd, 2019 at Convention Center “PACIFICO Yokohama”.

This will be the 39nd annual IGARSS symposium and will continue the excellent tradition of gathering world-class scientists, engineers and educators engaged in the fields of geoscience and remote sensing. We believe that the additional scientific themes of this event, focusing on ‘Disasters and Environment’ will allow the formation of an inspiring technical program.

IGARSS is recognized today as a premier event in remote sensing and provides an ideal forum for obtaining up-to-date information about the latest developments, exchanging ideas, identifying future trends in your research area and making contacts with the international remote sensing community. With intensive and careful planning underway we anticipate a technically outstanding and most pleasant symposium.

[征文范围及要求]

IGARSS is a premier event in remote sensing and provides an ideal forum for obtaining up-to-date information about the latest developments, exchanging ideas, identifying future trends and networking with the international geoscience and remote sensing community.

The IGARSS 2019 technical program will include the following general themes:

Data Analysis Methods

Atmosphere

Cryosphere

Oceans

Land

Missions, Sensors and Calibration

Data Management and Education

In addition, special scientific themes will be addressed, including:

Monitoring and damage assessment of natural disasters and hazards

NewSpace in remote sensing

Big data and machine learning

Observing key variables in climate action