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

HAND

書目名稱Advances and New Trends in Environmental Informatics 2023影響因子(影響力)




書目名稱Advances and New Trends in Environmental Informatics 2023影響因子(影響力)學科排名




書目名稱Advances and New Trends in Environmental Informatics 2023網(wǎng)絡公開度




書目名稱Advances and New Trends in Environmental Informatics 2023網(wǎng)絡公開度學科排名




書目名稱Advances and New Trends in Environmental Informatics 2023被引頻次




書目名稱Advances and New Trends in Environmental Informatics 2023被引頻次學科排名




書目名稱Advances and New Trends in Environmental Informatics 2023年度引用




書目名稱Advances and New Trends in Environmental Informatics 2023年度引用學科排名




書目名稱Advances and New Trends in Environmental Informatics 2023讀者反饋




書目名稱Advances and New Trends in Environmental Informatics 2023讀者反饋學科排名

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

貪心

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