Yang Zhao

Rapid urbanization and mega-events have reshaped land use in Beijing’s Yanqing District, making post-event balance between ecological protection and urban development a critical challenge for territorial spatial planning. This study analyzes land use evolution from 2015 to 2025, simulates 2035 patterns under natural development (ND), ecological protection (EP), and urban development (UD) using the PLUS model (Kappa = 0.91, OA = 0.95), and constructs an Ecological Conversion Direction Index (ECDI) to characterize the net direction of ecological quality change. Results show forest and construction land increased (+40.17 km²; +14.83 km²), grassland declined sharply (−38.23 km², −36.47%), cropland decreased (−25.36 km², −5.17%), and water expanded (+8.59 km², +62.38%). The ECDI remained positive (0.313 to 0.178) but weakened post-event. The driving mechanism showed a trend of transition from “event-response” toward “ecological constraint”—the contribution of distance to landmarks/venues declined substantially for most land types, while NDVI emerged as a prominent predictor negatively associated with construction land (18.24%) and positively associated with forest quality improvement (17.55%). The EP scenario was identified as optimal, reducing construction land by 8.05 km² to 98.67 km² while increasing forest to 1388.57 km², effectively safeguarding both the ecological baseline and cropland redline. We recommend adopting EP, strengthening the construction land reclamation rate, and coordinating the three control lines. These findings provide evidence-based policy support for reconciling urban development with ecological conservation in rapidly urbanizing ecological protection areas.

Mineral resources are of great significance in the development of the national economy. Prospecting and forecasting are the key to ensure the security of mineral resources supply, promote economic development, and maintain social stability. The methods for prospecting prediction have evolved from qualitative to quantitative prediction, from empirical research to mathematical analysis. In recent years, deep learning algorithms have gradually entered the attention of geologists due to their robust learning and simulation ability in the application of prospecting prediction. Deep learning algorithms can effectively analyze and predict data, which have great significance in improving the efficiency and accuracy of mineral exploration. However, there are not many specific examples of their application in mineral exploration prediction, and researchers have not yet conducted a comprehensive discussion on the advantages, disadvantages, and accuracy of deep learning algorithms in mineral prospectivity mapping applications. This paper reviews and discusses the application of deep learning in prospecting prediction, highlighting the challenges faced by deep learning in the application of prospecting prediction in data preprocessing, data enhancement, system parameter adjustment, and accuracy evaluation, and puts forward specific suggestions for research in these aspects. The purpose of this paper is to provide a reference for the application of deep learning to researchers and practitioners in the field of prospecting prediction.