With a large population and rapid urbanization, there are still many challenges to optimize the ecological-agricultural-urban space. Here, taking Yulin City, situated on the Loess Plateau of China as a case in point, we explored the spatial suitability evaluation of ecological-agricultural-urban space. Building upon the Chinese government’s concept of “resource and environmental carrying capacity and territorial development suitability evaluation” (hereinafter referred to as “double evaluation”), this study applies machine learning to the planning of ecological-agricultural-urban space. It explores an intelligent evaluation method for land space patterns using multi-source data. Based on the random forest (RF) algorithm and geographic information system (GIS), resulting in evaluated spatial patterns for ecological-agricultural-urban in the Yulin area. The results showed the constructed random forest models achieved an accuracy of 93% for ecology, 90% for agriculture, and 92% for urban space in Yulin City on the test dataset. By means of suitability analysis, the results indicated that the extremely important ecological space were predominantly located in the southwestern and eastern regions of the study area, while suitable space for agricultural production were primarily scattered throughout the southeast. In contrast, suitable space for urban construction were concentrated mainly in the central part of the study area. The use of machine learning has proven to be effective in addressing multicollinearity among spatial evaluation factors across three different areas. By eliminating human subjectivity in weight assignment during evaluation, it introduces fresh perspectives for land space planning and status assessment. These findings may offer support for the scientific delineation of ecological-agricultural-urban space (three districts and three lines) in China.

With the increasing global population and escalating ecological and farmland degradation, challenges to the environment and livelihoods have become prominent. Coordinating urban development, food security, and ecological conservation is crucial for fostering sustainable development. This study focuses on assessing the "Ecology-Agriculture-Urban" (E-A-U) space in Yulin City, China, as a representative case. Following the framework proposed by Chinese named "environmental capacity and national space development suitability evaluation" (hereinafter referred to as "Double Evaluation"), we developed a Self-Attention Residual Neural Network (SARes-NET) model to assess the E-U-A space. Spatially, the northwest region is dominated by agriculture, while the southeast is characterized by urban and ecological areas, aligning with regional development patterns. Comparative validations with five other models, including Logistic Regression (LR), Naive Bayes (NB), Gradient Boosting Decision Trees (GBDT), Random Forest (RF) and Artificial Neural Network (ANN), reveal that the SARes-NET model exhibits superior simulation performance, highlighting it’s ability to capture intricate non-linear relationships and reduce human errors in data processing. This study establishes deep learning-guided E-A-U spatial evaluation as an innovative approach for national spatial planning, holding broader implications for national-level territorial assessments.

The Xinghongpu Formation is very important for understanding the Devonian tectonic evolution of the South Qinling orogenic belt. Geochemical, detrital zircon U-Pb-Hf isotopic studies were carried out on the Late Devonian metasedimentary rocks of the Xinghongpu Formation to constrain the depositional age, the provenance, and the tectonic setting. The detrital zircon U-Pb dating results revealed that the depositional age of the Xinghongpu Formation of the Late Devonian was not earlier than 363.2 Ma. The whole-rock geochemistry suggested that (1) this suite of metasedimentary rocks was mainly derived from quartzose sediments of mature continental provenance, with a small contribution from mafic and intermediate igneous provenance, and (2) the metasedimentary sandstone of the Xinghongpu Formation from the Late Devonian was deposited in an active continental margin to continental arc setting. The detailed detrital zircon U-Pb dating showed that the age spectra of detrital zircon could be divided into four groups: (1) 416–480 Ma, accounting for about 23%; (2) 740–850 Ma, accounting for about 19%; (3) 889–1017 Ma, accounting for about 19%; and (4) 1072–1146 Ma, accounting for about 12%. It also contained a group of Early Proterozoic zircons. The age and Hf isotope of the detrital zircons suggested that the clastic sediment deposited in the Xinghongpu Formation mainly came from the South Qinling Orogenic Belt and the North Qinling Orogenic Belt. The detrital zircon Lu-Hf isotopes indicated that most zircons were the products of the ancient crustal remelting, and the mantle-derived magmatic sources contributed to the provenance. The Xinghongpu Fm. formed in an oceanic basin in a continental margin environment with arc systems.