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

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.

Continuous land cover change monitoring in the remote sensing big data era

Since the late 20th century,global change issues have attracted lots of attention.As a key component of global changes,land cover and land use information has been increasingly important for improved understanding of global environmental changes and feedbacks between social and environmental systems（Verburg et al.,2015）.

利用Google Earth Engine和机器学习测量过去20年天山冰川的变化

Glaciers in the Tianshan Mountains are an essential water resource in Central Asia,and it is necessary to identify their variations at large spatial scales with high resolution.We combined optical and SAR images,based on several machine learning algorithms and ERA-5 land data provided by Google Earth Engine,to map and explore the glacier distribution and changes in the Tianshan in 2001,2011,and 2021.Random forest was the best performing classifier,and the overall glacier area retreat rate showed acceleration from 0.87%/a to 1.49%/a,while among the sub-regions,Dzhungarsky Alatau,Central and Northern/Western Tianshan,and Eastern Tianshan showed a slower,stable,and sharp increase rates after 2011,respectively.Glacier retreat was more severe in the mountain periphery,low plains and valleys,with more area lost near the glacier equilibrium line.The sustained increase in summer temperatures was the primary driver of accelerated glacier retreat.Our work demonstrates the advantage and reliability of fusing multisource images to map glacier distributions with high spatial and temporal resolutions using Google Earth Engine.Its high recognition accuracy helped to conduct more accurate and time-continuous glacier change studies for the study area.