Abstract:
The advancements of the 1980s led to the creation of various important technologies, including GPS and satellite imagery, which allowed for the sustainable management of land resources. This must be done while preserving sustainable landuse systems and confronting concerns such as climate change, water scarcity, and the risk of increasing erosion and productivity due to extreme weather events. In several areas, geospatial Big Data analytics is transforming the way firms’ function. Although there are many research workson geographic data analytics and real-time data processing of massive spatial data streams in the literature, only a few have covered the entire geospatial big data analytics and geospatial data science project lifecycle. Because of the volume, pace, and variety of the data being analysed, big data analysis differs from typical data analysis. In comparison to conventional data analysis projects, geospatial data science initiatives are likely to be more difficult and require advanced technologies. The current study introduces a novel geographic big data mining and machine learning framework for geospatial data gathering, fusion, storage, management, processing, analysis, visualisation, and land resource modelling and evaluation. Any data science project that has a robust procedure for land resource data analysis and clear instructions for comprehensive analysis is always a positive. It also aids in estimating the amount of time and resources required early in the process to gain a good picture of the land resource challenges that must be overcome. Automation and the use of artificial intelligence (AI), the internet of things (IoT), drones, satellite imagers, and Big Data lay the foundation for a global β€_x009c_Digital Twin,β€_x009d_ which will aid in the development of site-specific conservation and management practices that will boost incomes and ensure the long-term sustainability of land use/land cover systems. Β© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
