A super-ensemble approach to map land cover types with high resolution over data-sparse African savanna landscapes

•An ensemble mapping method is proposed for data sparse landscapes.•The consensus of low-quality land cover maps offers a starting point to map a better one.•Machine learning-aided labeling minimizes labeling effort and speeds up mapping.•Analyzing labeling errors can increase models’ ability to learn from low-quality labels.