Titlebook: Advances and New Trends in Environmental Informatics 2023; Sustainable Digital Volker Wohlgemuth,Dieter Kranzlmüller,Maximilian H Conferen

HAND

书目名称Advances and New Trends in Environmental Informatics 2023影响因子(影响力)




书目名称Advances and New Trends in Environmental Informatics 2023影响因子(影响力)学科排名




书目名称Advances and New Trends in Environmental Informatics 2023网络公开度




书目名称Advances and New Trends in Environmental Informatics 2023网络公开度学科排名




书目名称Advances and New Trends in Environmental Informatics 2023被引频次




书目名称Advances and New Trends in Environmental Informatics 2023被引频次学科排名




书目名称Advances and New Trends in Environmental Informatics 2023年度引用




书目名称Advances and New Trends in Environmental Informatics 2023年度引用学科排名




书目名称Advances and New Trends in Environmental Informatics 2023读者反馈




书目名称Advances and New Trends in Environmental Informatics 2023读者反馈学科排名

SEED

2196-8705 unication technologies with environmental sustainability.Exp.This book is an outcome of the 37th International Conference EnviroInfo 2023, held at the Leibniz Supercomputing Centre (Munich, Germany), organized by the technical committee for Environmental Informatics of the German Informatics Society

recede

instulate

978-3-031-46904-6The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

贪心

Tractable

Die ,schen Inseln des Pankreas,built as a project-specific adaptation of the environmental information system (EIS) Guerilla Sensing. We describe the adaptation process step by step to provide a blueprint for easy use of Guerilla Sensing in similar future projects.

ethereal

Infect

A. Benninghoff,A. Hartmann,T. Hellmanith sensor calibration and data fusion competences and a ML-method research based on low-cost OpenSensorMap. The three ML-pipelines are described, commonalities and differences worked out and the advantages of every technique are led over in an effort of a combined ML-pipeline which could be realise

agenda

Cacophonous

R. Bachmann,E. Scharrer,B. Scharrer ecosystems in near future. Radar backscatter e.g. allows insights to crop conditions, soil properties and direct mapping of vegetation growth. Precise SAR pre-processing is a substantial prerequisite to perform machine learning on SAR data, e.g. for early prediction of optimal sowing, harvesting an