Crop type mapping using LiDAR, Sentinel-2 and aerial imagery with machine learning algorithms
Author:
Affiliation:
1. Department of Geography & Environmental Studies, Stellenbosch University, Stellenbosch, South Africa
Funder
Salt Accumulation and Waterlogging Monitoring System
Publisher
Informa UK Limited
Subject
Computers in Earth Sciences,Geography, Planning and Development
Link
https://www.tandfonline.com/doi/pdf/10.1080/10095020.2020.1782776
Reference52 articles.
1. Object-based land cover classification using airborne LiDAR
2. Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis
3. Object-oriented land cover classification of lidar-derived surfaces
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