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Editorial for Special Issue: “new Insights into Ecosystem Monitoring Using Geospatial Techniques”

Remote sensing(2022)SCI 2区SCI 3区

Italian Natl Inst Environm Protect & Res ISPRA | Univ Cantabria | Simon Fraser Univ | UBO | Plant Sci & Biodivers Ctr SAS | US Geol Survey

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Abstract
Recent global-scale environmental issues from climate change to biodiversity loss are generating an intense social pressure on the scientific community [...]
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  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
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要点】:本文专辑聚焦于利用地理空间技术对生态系统监测的新洞察,旨在应对从气候变化到生物多样性丧失等全球环境问题。

方法】:专辑中的文章采用了多种地理空间技术,如遥感、地理信息系统(GIS)等,对生态系统进行监测和分析。

实验】:各篇文章通过具体案例研究,使用不同数据集(未明确提及具体数据集名称),展示了地理空间技术在生态系统监测中的有效性,并得出了相应的监测结果。