A New Era of Land Cover Land Use Categorization Using Remote Sensing and GIS of Big Data

Big data is the present era in which we find ourselves. Using spatial data, including auxiliary geospatial datasets and remotely sensed satellite images, has become prevalent in land cover and land use mapping (LCLU). Moreover, deep learning and machine learning algorithms have recently been innovated, offering new prospects for LCLU mapping. But problems often arise when it comes to using big geospatial data. This article summarizes research advancements in remote sensing (RS), machine learning (ML), deep learning (DL), and geographic information big data for LCLU classification. We analyzed the advantages, disadvantages, and potential future directions of LCLU mapping utilizing big geospatial data. Further research is needed to improve the LCLU process at larger scales.

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