Sampling Strategy for Detailed Urban Land Use Classification: A Systematic Analysis in Shenzhen

• Published in: Remote sensing (Basel, Switzerland) Vol. 12; no. 9; p. 1497
• Main Authors: Su, Mo, Guo, Renzhong, Chen, Bin, Hong, Wuyang, Wang, Jiaqi, Feng, Yimei, Xu, Bing
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2020
• Subjects: Accuracy; Cities; Classification; field survey; Land surveys; Land use; Land use classification; land use mapping; Location based services; machine learning; parcel segmentation; Purity; Remote sensing; Researchers; Roads & highways; samples; Sampling; Studies; Training; Urban areas; Workload; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12091497

A heavy workload is required for sample collection for urban land use classification, and researchers are in urgent need of sampling strategies as a guide to achieve more effective work. In this paper, we make use of an urban land use survey to obtain a complete sample set of a city, test the impact of different training and validation sample sizes on the accuracy, and summarize the sampling strategy. The following conclusions are drawn based on our systematic analysis in Shenzhen. (1) For the best classification accuracy, the number of training samples should be no less than 40% of the total number of parcels or no less than 5500 parcels. For the best labor cost performance, the number should be no less than 7% or no less than 900. (2) The accuracy evaluation is stable and reliable and requires validation sample numbers of no less than 10% of the total or no less than 1200. (3) Samples with a purity of 60–90% are preferred, and the classification effectiveness is better in samples with a purity greater than 90% under the same number. (4) If spatial equilibrium sampling cannot be carried out, sampling areas with complex land use patterns should be preferred.