Fusing Earth Observation, Volunteered Geographic Information and Artificial Intelligence for improved Land Management

被引:0
作者
Antoniou, Vyron [1 ]
Lupia, Flavio [2 ]
机构
[1] Multinatl Geospatial Support Grp, Frauenberger Str 250, D-53879 Euskirchen, Germany
[2] CREA Council Agr Res & Econ, Via Po 14, I-00198 Rome, Italy
关键词
EARTH OBSERVATION; VGI; MACHINE LEARNING; DEEP LEARNING; DIGITAL AGRICULTURE; LAND MANAGEMENT;
D O I
暂无
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
The ever-growing availability of Earth Observation (EO) data is demonstrating a wide range of potential applications in the realm of land management. On the other hand, large volumes of data need to be handled and analysed to extract meaningful information and Geomatics coupled with new approaches such as Artificial Intelligence (AI) and Machine Learning (AI) will play a pivotal role in the years to come. Training datasets need to be developed to use these new models and Volunteered Geographic Information can be one of the promising sources for EO processing. Among the various applications, agriculture may benefit from the large dataset availability and AI processing. However, several issues remain unsolved and further steps should be taken in the near future by researchers and policy makers.
引用
收藏
页码:10 / 14
页数:5
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