The Review of Land Use/Land Cover Mapping AI Methodology and Application in the Era of Remote Sensing Big Data

Abstract

With the increasing number of remote sensing satellites, the diversification of observation modals, and the continuous advancement of artificial intelligence algorithms, historically opportunities have been brought to the applications of earth observation and information retrieval, including climate change monitoring, natural resource investigation, ecological environment protection, and territorial space planning. Over the past decade, artificial intelligence technology represented by deep learning has made significant contributions to the field of Earth observation. Therefore, this review will focus on the bottlenecks and development process of using deep learning methods for land use/land cover mapping of the Earth's surface. Firstly, it introduces the basic framework of semantic segmentation network models for land use/land cover mapping. Then, we summarize the development of semantic segmentation models in geographical field, focusing on spatial and semantic feature extraction, context relationship perception, multi-scale effects modelling, and the transferability of models under geographical differences. Then, the application of semantic segmentation models in agricultural management, building boundary extraction, single tree segmentation and inter-species classification are reviewed. Finally, we discuss the future development prospects of deep learning technology in the context of remote sensing big data.

Keywords

• |remote sensing big data|deep learning|semantic segmentation|land use/land cover mapping