MLNT 2023 : Applications of Machine Learning in National Territory Spatial Planning
Call For Papers
National territory spatial planning guides national spatial development and the spatial blueprint of sustainable development. It is the basis for all kinds of development, protection, and construction activities. It is of great significance to solve problems such as the prominent human–land conflict, the lack of spatial resources, and the imbalance of regional development in the process of rapid urbanization, industrialization, and modernization. National territory spatial planning integrates multiple spatial plans, including main functional area planning as well as land use planning and urban and rural planning, and opens a new era of integrated territorial governance. Integrated territorial governance is the theoretical interpretation of the direction of national territory spatial governance. Eradicating the conflicts among multiple spatial plans is the basic premise of the reconstruction of a national territory spatial planning system. Supporting the construction of ecological civilization is an important mission of national territory spatial governance for the current age.
Machine learning technology is multi-disciplinary field, involving probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and other disciplines. It specializes in determining how computers simulate or realize human learning behavior in order to acquire new knowledge or skills, and reorganizing existing knowledge structures to continuously improve its own performance.
This Special Issue solicits the latest application achievements and advanced technologies of machine learning in the theory and practice of national territory spatial planning. We expect these selected academic papers to systematically summarize and sort out the methods of national territory spatial planning, determine the technical problems existing in national territory spatial planning as it currently exists, and to provide reference technical guidance for theoretical research and practice of national territory spatial planning in the future. We sincerely request the technological applications of machine learning in the following topics, but works on other related aspects are welcome.
Assessment of the current situation of national territory spatial development and the protection and judgment of future risks.
Optimization of national territory spatial pattern in response to global climate change.
National territory spatial development patterns under the background of new globalization.
Strategy and system of main functional areas in the era of ecological civilization.
Safety and sustainable guarantee of water, soil, energy, and mineral resources.
National ecological security pattern.
National food security and agricultural spatial pattern.
National rural revitalization and urban–rural integrated development.
Population and urbanization trends and national urban development distribution.
Spatiotemporal patterns of population migration and mobility trends.
Optimization of national industrial spatial distribution.
Improving the quality of human settlements and community life cycles.
Supply of national territory spatial development serving high quality of life.
Development, protection and utilization of marine space and steadfastly promoting land and marine development in a coordinated way.
Coordinated development of comprehensive transportation systems and national territory spatial development patterns.
New infrastructure construction.
Coordinated protection, development and mechanism innovation of important economic zones and watersheds.
Knowledge graph mapping of national territory spatial planning.
Zoning guidance and transmission mechanisms of national territory spatial development.
Guarantee mechanism and system innovation for the implementation of national territory spatial development.
Big data applications and platforms supporting the preparation of national territory spatial development.
Practical cases of local territory spatial development innovation.
Prof. Dr. Jun Yang
Prof. Dr. Bing Xue
Prof. Dr. Xiangming Xiao