Land Use and Land Cover Mapping in the Era of Big Data

We are currently living in the era of big data. The volume of collected or archived geospatial data for land use and land cover (LULC) mapping including remotely sensed satellite imagery and auxiliary geospatial datasets is increasing. Innovative machine learning, deep learning algorithms, and cutting-edge cloud computing have also recently been developed. While new opportunities are provided by these geospatial big data and advanced computer technologies for LULC mapping, challenges also emerge for LULC mapping from using these geospatial big data. This article summarizes the review studies and research progress in remote sensing, machine learning, deep learning, and geospatial big data for LULC mapping since 2015. We identified the opportunities, challenges, and future directions of using geospatial big data for LULC mapping. More research needs to be performed for improved LULC mapping at large scales.

Automated summary

This article summarizes the research progress and applications of remote sensing, machine learning, deep learning, and geospatial big data in LULC mapping. The article identifies the opportunities and challenges in using geospatial big data for LULC mapping and suggests that more research is needed to improve the accuracy of large-scale LULC mapping.