Artifi cial intelligence and data analysis are essential in smart agriculture for enhancing crop productivity and food security.However,progress in this field is often limited by the lack of specialized,error-free lab...Artifi cial intelligence and data analysis are essential in smart agriculture for enhancing crop productivity and food security.However,progress in this field is often limited by the lack of specialized,error-free labeled datasets.This paper introduces DAClA5,a multispectral image dataset for agricultural crop identification,complemented with Sentinel-1 radar data.The dataset consists of 172 Sentinel-2 multispectral images(800×450 pixels)and 159 Sentinel-1 radar images,collected over Braov,Romania,from 2020 to 2024,with precise,in-situ verified labels.Additionally,6,454 Sentinel-2 and 5,995 Sentinel-1 rectangular patches(32 x 32 pixels)were extracted,exceeding 6 million pixels in total.The cropland parcels considered in our dataset are used for research and are owned and cultivated by the National Institute of Research and Development for Potato and Sugar Beet,ensuring error-free labeling.The labels in our dataset provide detailed information about crop types,offering insights into crop distribution,growth stages,and phenological events.Furthermore,we present a comprehensive dataset analysis and two key use cases:crop identifi cation based on a"past vs.present"approach and early crop identification during the agricultural season.展开更多
基金Funded by the European UnionThe Al4AGRl project entitled"Romanian Excellence Center on Artificial Intelligence on Earth Observation Data for Agriculture"received funding from the European Union's Horizon Europe research and innovation program under grant agreement no.101079136Al4AGRl project received funding from the European Union's Horizon Europe research and innovation programme[101079136].
文摘Artifi cial intelligence and data analysis are essential in smart agriculture for enhancing crop productivity and food security.However,progress in this field is often limited by the lack of specialized,error-free labeled datasets.This paper introduces DAClA5,a multispectral image dataset for agricultural crop identification,complemented with Sentinel-1 radar data.The dataset consists of 172 Sentinel-2 multispectral images(800×450 pixels)and 159 Sentinel-1 radar images,collected over Braov,Romania,from 2020 to 2024,with precise,in-situ verified labels.Additionally,6,454 Sentinel-2 and 5,995 Sentinel-1 rectangular patches(32 x 32 pixels)were extracted,exceeding 6 million pixels in total.The cropland parcels considered in our dataset are used for research and are owned and cultivated by the National Institute of Research and Development for Potato and Sugar Beet,ensuring error-free labeling.The labels in our dataset provide detailed information about crop types,offering insights into crop distribution,growth stages,and phenological events.Furthermore,we present a comprehensive dataset analysis and two key use cases:crop identifi cation based on a"past vs.present"approach and early crop identification during the agricultural season.
