Land Use Classification with GIS and Remote Sensing Data Based on AI Technology

Dear Colleagues,

Remote sensing has been a very useful source of data to understand the changes on the earth’s surface. Land Cover classification based on the spectral signatures captured in remote sensing observations has been widely used to understand anthropogenic changes and their impact on the environment. At macro scale, such information has been invaluable for monitoring progress towards sustainable development goals and for informing the policymaking processes. 

However, the application of remote sensing to analyze the changes in Land Use that are linked with socio-economic characteristics rather than physical drivers has been comparatively less explored. 

Information on Land Use and its change can be extremely useful for humanitarian strategies and operations at regional and local scales. For example, the information on Land Use at a local scale can be used to support areas without specific types of essential services, for example, education and health care, etc. 

The applications of remote sensing to characterize Land Use has been limited partly due to the lack of the means to extract contextual information from remote sensing data and also due to the lack of the resources required for such complex analysis. We have seen recent progress and applications of deep learning to extract complex contextual information from remote sensing data to understand socio-economic characteristics such as poverty which demonstrates the feasibility of using AI and remote sensing to better characterize Land Use and its change. Recent developments in AI algorithms and resources such as very high-resolution imagery, cloud-based computing platforms also enabled the applications of AI-based algorithms on remote sensing data for Land Use classification.  

Nonetheless, there are still many obstacles for the successful applications of AI for land use characterization including lack of proper, curated training samples, scalability of AI algorithms and, applicability of deep learning algorithms to the different types of remote sensing data, etc. 

This special issue welcomes papers that explore the various topics on the applications of AI to characterize Land Use and its change.  

Dr. Do-Hyung Kim
Dr. Anupam Anand
Dr. Joseph Bullock
Dr. Miguel Luengo-Oroz
Guest Editors

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