リモートセンシングデータを用いたセマンティックセグメンテーションによる圃場内異常検出

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  • Detection of Abnormal Regions in a Field by Semantic Segmentation Using Remote Sensing Data

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<p>In recent years, “smart agriculture,” which introduces ICT into agriculture to improve the efficiency, automation, and productivity of agricultural work, is attracting attention. For advanced smart agriculture, this paper proposes a semantic segmentation model to detect and classify abnormal regions in a field using satellite or aerial images. The performance evaluation of the proposed model is conducted for the Agriculture-Vision Challenge Dataset, a dataset of aerial images of fields and abnormal regions. The results show that the proposed model can detect abnormal regions with higher performance than conventional deep learning models.</p>

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