# Applications of Machine Learning in National Territory Spatial Planning
## 2 Materials and Methods  
Big-Data-Driven Urban Spatial Environmental Monitoring and Infrastructure Service Capability Measurement  
With the continuous advancement of machine learning, positioning, navigation, and remote sensing technology, the construction of a \"Smart City\" provides data acquisition methods for space-time sensing big data, such as satellite and drone remote sensing, mobile sensing, social sensing, and crowdsourced sensing data, thus providing a new way to analyze urban dynamics such as urban space, human behavior, and the interaction between them [9] and explore the three-dimensional, comprehensive, and multi-dimensional nature of urban space [10]. Of the papers included in our Special Issue, two research teams attempted to use new types of big data combined with machine learning technologies to innovate research methodologies for different urban spatial issues, such as urban spatial environmental monitoring and infrastructure service capability measurement. [PERSON] et al. (contribution 1) provide a novel approach for analyzing nocturnal urban noise patterns and identifying distinct zones using mobile phone data and audio recordings from city sensors, which utilize machine learning techniques to extract noise features indicative of different sound sources and intensities and create comprehensive city noise maps during nighttime hours. In order to evaluate the service capacity of public health facilities more accurately, [PERSON] et al. (contribution 7) take the integrated population and spatial elements into consideration and compensate for the lack of a relevant evaluation model and data collection method based on the perspective of geographic system analysis by using point of interest (POI) big data. These methodologies have a profound impact on government decision-making intention, the processes involved, and the efficiency level at which it operates. However, there are also a number of research fields not involved in this area of interest that are worthy of further study, such as national rural revitalization and urban-rural integrated development, new infrastructure construction, and so on. Food security and agricultural development are major issues related to the national economy and people's livelihoods, which are crucial for maintaining social stability and economic development, building ecological barriers, and safeguarding national security [11]. Research on crop classification, food security monitoring and early warning systems, and other relevant fields based on machine learning methods is of great practical significance for ensuring regional food security and ecological security [12]. Recently, a number of scholars have presented an interdisciplinary perspective between food security, agricultural development spatial planning, and machine learning and have achieved notable results in the research fields of food security monitoring, food security early warning, and the simulation application of machine learning technology. However, existing research still lacks a comprehensive integrated system model, data mining, and visualization technology in food security through the use of machine learning technologies to establish and perfect them [13]. As a part of our Special Issue, two research teams have made efforts to tackle this issue. [PERSON] et al. (contribution 2) introduce the concept of resilience and explore the impact of NGCL on agricultural development resilience (ADR) in the main grain-producing area of northeast China based on the threshold effect model and the spatial lag model. [PERSON] et al. (contribution 4) summarize and show that new technologies in Industry 4.0 (drones and sensors, big data, the Internet of Things, and machine learning) and their application through climate-smart agriculture approaches play a key role in sustainable businesses (economically, socially, and environmentally) and food supply.