GSoD'21 Week 1 meeting

这是Google Season of Docs 2021第一周的会议总结。会议中,参与者们商定了未来会议的时间,并分享了各自的工作进展和计划。讨论了是否延长项目时间线,确定了文档结构和风格。此外,解决了时间线冲突,决定通过PR进行文档审查,并明确了技术作者与志愿者的沟通方式。

Google Season of Docs’21 Week 1 meeting

Meeting Summary

It was the first meeting of GSoD’21 participants after the selection announcement, I was excited to meet them all, I am glad that I got a chance to host this wonderful meeting.

In this meeting, firstly we had a discussion to set up a day and a time for future meetings, after that every participant gave his week summary and told us about their plans for the upcoming week. After that, we discussed whether we should stretch the main project’s timeline or not. Next Simin leads a discussion about documentation structure and style. Later we had a suggestions/Q&A session, and at last, we took a lovely group photo.

Attendees

Total 13 attendees attended the meeting. They are:

  1. Rohitesh Jain, Volunteer (UTC + 5:30)
  2. Sajen Sarvajith, Reconstruct Landing page writer (UTC + 5:30)
  3. Abhishek Jaiswal, How-to-guide section writer (UTC + 05:30)
  4. Simin Liao, Volunteer (+8:00)
  5. Mukosa Joseph mawa, Introductions and Explanations section writer (UTC +03:00)
  6. Rajiv Ranjan Singh, Improve the gRPC and OpenAPI ecosystem writer, rajivperfect007@gmail.com, (UTC+05:30)
  7. Souvik Biswas, Create easy to learn tutorials for beginner users writer, sbis1999@gmail.com (UTC +05:30)
  8. Soumi Bardhan, Improve References section writer (UTC + 5:30)
  9. Shraddha Vasant Prasad,Improve References sectio writer (UTC + 5:30)
  10. Shwetal Soni, Create easy to learn tutorials for beginners users writer (UTC +05:30)
  11. Chris Estepa, Introduction and Explanations sections writer (UTC +08:00)
  12. Vasvi Sood, How to guides, contactvasvisood@gmail.com writer (UTC + 5:30)
  13. Arnab Saha, Reconstruction of Landing page with value propositions writer (UTC + 5:30)

Google Season of Docs 2021: Wechaty GSoD'21 Group Photo

Agendas

  • 0:00:14 1. Requesting participants to join the Wechaty mailing list
  • 0:00:50 2. Discussion for fixing the day and the time for future meetings.
  • 0:03:40 3. Week Summaries & plans of the participants
  • 0:13:35 4. Discussion for resolving timeline conflicts
  • 0:15:20 5. Discussion about documentation structure and style
  • 0:41:00 6. Questions & Comments

You can learn more from our meeting notes.

Meeting Outcomes

  • Everyone joined the Wechaty mailing list
  • Sunday has been voted as the day for the weekly meetings
  • Future meetings will be scheduled on 19:00 UTC + 8
  • The main project’s timeline got stretched from 12 to 17 weeks
  • It’s decided that the project overlaps will be resolved by the next Friday (21 May)
  • We will be using PR’s for documentation review
  • It’s decided that there will be no mid review week
  • Tech writers got to know how they are supposed to contact volunteer(s) based on their preferences which are written in the communication section of the volunteer proposal
基于TROPOMI高光谱遥感仪器获取的大气成分观测资料,本研究聚焦于大气污染物一氧化氮(NO₂)的空间分布与浓度定量反演问题。NO₂作为影响空气质量的关键指标,其精确监测对环境保护与大气科学研究具有显著价值。当前,利用卫星遥感数据结合先进算法实现NO₂浓度的高精度反演已成为该领域的重要研究方向。 本研究构建了一套以深度学习为核心的技术框架,整合了来自TROPOMI仪器的光谱辐射信息、观测几何参数以及辅助气象数据,形成多维度特征数据集。该数据集充分融合了不同来源的观测信息,为深入解析大气中NO₂的时空变化规律提供了数据基础,有助于提升反演模型的准确性与环境预测的可靠性。 在模型架构方面,项目设计了一种多分支神经网络,用于分别处理光谱特征与气象特征等多模态数据。各分支通过独立学习提取代表性特征,并在深层网络中进行特征融合,从而综合利用不同数据的互补信息,显著提高了NO₂浓度反演的整体精度。这种多源信息融合策略有效增强了模型对复杂大气环境的表征能力。 研究过程涵盖了系统的数据处理流程。前期预处理包括辐射定标、噪声抑制及数据标准化等步骤,以保障输入特征的质量与一致性;后期处理则涉及模型输出的物理量转换与结果验证,确保反演结果符合实际大气浓度范围,提升数据的实用价值。 此外,本研究进一步对不同功能区域(如城市建成区、工业带、郊区及自然背景区)的NO₂浓度分布进行了对比分析,揭示了人类活动与污染物空间格局的关联性。相关结论可为区域环境规划、污染管控政策的制定提供科学依据,助力大气环境治理与公共健康保护。 综上所述,本研究通过融合TROPOMI高光谱数据与多模态特征深度学习技术,发展了一套高效、准确的大气NO₂浓度遥感反演方法,不仅提升了卫星大气监测的技术水平,也为环境管理与决策支持提供了重要的技术工具。 资源来源于网络分享,仅用于学习交流使用,请勿用于商业,如有侵权请联系我删除!
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值