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1、Using AI to Combine Databases for SOC MonitoringAlessandro Samuel-RosaPedometrics Laboratory LAC Soil Carbon 2025Museu do Amanh,Rio de Janeiro,Brazil2025-06-26 15:35-15:50BackgroundSoil Data Integration Using AI|Alessandro Samuel-Rosa|Pedometrics Laboratory SoilDataRepository of research data in soi

2、l scienceGeneral purpose repositoryResearch of any temporal/spatial dimension/extensionResearch carried out in any corner of the countryGoalsGive visibility to field,laboratory,and office workProvide opportunities for data reuse in other research and applicationsBring past research data to a product

3、ion environment https:/soildata.mapbiomas.org/Soil Data Integration Using AI|Alessandro Samuel-Rosa|Pedometrics Laboratory Free and open soil data since 2016Soil Data Integration Using AI|Alessandro Samuel-Rosa|Pedometrics Laboratory Dados de soloGeneral purpose repository(data lake)e.g.,pedology,fe

4、rtility,management,physics,biology+300 studies deposited/rescued(theses,dissertations,scientific papers,reports,etc.)+30 thousand field samplesData produced by independent professionals and public and private organizations(universities,institutes,and companies)Highlight:SoilDataRepository of researc

5、h data in soil science https:/soildata.mapbiomas.org/Soil Data Integration Using AI|Alessandro Samuel-Rosa|Pedometrics Laboratory Annual releases!v2024 is coming soon!Soil Data Integration Using AI|Alessandro Samuel-Rosa|Pedometrics Laboratory Soil Data Integration Using AI|Alessandro Samuel-Rosa|Pe

6、dometrics Laboratory Data flow in the repositorySoil Data Integration Using AI|Alessandro Samuel-Rosa|Pedometrics Laboratory RescueInfrastructureSamplingCurationProcessingXYT ModellingDeposit https:/soildata.mapbiomas.org/This is what we will be looking at today!AI for Soil Data RescueSoil Data Inte

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根据文章内容,以下是对全文主要内容的简明概括: - **土壤数据仓库**:一个用于土壤科学研究数据的通用仓库,自2016年以来免费开放,包含300多个研究项目,30万个样本数据。 - **数据救援**:利用AI从非结构化文档中提取数据,解决数据格式不一致、碎片化等问题。 - **公民数据贡献**:公民通过识别土地特征来贡献数据,如沙丘、侵蚀等,并利用高分辨率卫星图像进行数据增强。 - **土壤采样优化**:使用AI进行多目标优化,选择最佳采样点。 - **数据质量控制**:AI用于土壤数据质量控制,确保数据准确性和一致性。 - **数据缺口填补**:AI用于填补土壤数据中的缺失值,如土壤深度、有机碳含量等。 - **模型训练**:使用大量数据训练模型,包括土壤属性、环境条件、地理位置等,以提高预测精度。 - **开源贡献**:所有代码和资源均开源,鼓励贡献和合作。
"AI如何拯救土壤数据?" "公民参与,AI助力土壤监测?" "土壤数据,AI如何填补空白?"
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