Crop type mapping using LiDAR, Sentinel-2 and aerial imagery with machine learning algorithms

• Published in: Geo-spatial information science Vol. 24; no. 2; pp. 215 - 227
• Main Authors: Prins, Adriaan Jacobus, Van Niekerk, Adriaan
• Format: Journal Article
• Language: English
• Published: Wuhan Taylor & Francis 03.04.2021; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Accuracy; Aerial photography; Aerial surveys; Algorithms; Classification; crop type classification; Crops; Datasets; Digitization; Digitizing; Evaluation; Human error; Labels; LiDAR; Machine learning; Mapping; multispectral imagery; per-pixel classification; Precision agriculture; Remote sensing; Satellite imagery; sentinel-2; Water monitoring; Water use

LiDAR data are becoming increasingly available, which has opened up many new applications. One such application is crop type mapping. Accurate crop type maps are critical for monitoring water use, estimating harvests and in precision agriculture. The traditional approach to obtaining maps of cultiva...