Nengcheng Chen’s research while affiliated with China University of Geosciences and other places

Due to urbanization, accurately modeling intercity population migration is vital for regional policymaking. While the radiation model provides an interpretable framework by focusing on opportunity distribution rather than physical distance, it struggles with nonlinear migration patterns and relies solely on population size as its input. To address these limitations, we propose the Deep Radiation model, which integrates the theoretical foundation of the radiation model with the nonlinear capabilities of neural networks. By replacing linear equations with a feedforward neural network, our model predicts migration flows based on an expanded set of features across four dimensions: economic development, urbanization, infrastructure, and environmental factors. Testing on 295 Chinese cities demonstrates that the Deep Radiation model significantly outperforms the original in capturing human mobility patterns, particularly the push–pull effects of cities and their surrounding regions. This study advances migration modeling and provides actionable insights for regional planning.

Analyzing and mining spatiotemporal processes refers to the extraction of geographic phenomena from spatiotemporal data and the analysis of available geographic knowledge and patterns. It finds applications in various fields such as natural disaster evolution, environmental pollution, and human behavior prediction. However, training spatiotemporal models based on big data is time-consuming, and the traditional physical models and static objects used in existing geographic data analysis software have limitations in mining efficiency and simulation accuracy for dynamic spatiotemporal processes. In this paper, we develop an intelligent spatiotemporal process analysis and mining software tool, called STPam, which integrates a plug-and-play artificial intelligence model by a service-oriented method, distributed deep learning framework, and multi-source big data adaptation. The floods in the middle reaches of the Yangtze River have perennially affected safety and property in surrounding cities and communities. Therefore, this article applies the software to simulate the flooding process in the basin in 2022. The experimental results correspond to the rare drought phenomenon in the basin, demonstrating the practicality of the STPam software. In summary, STPam aids researchers in visualizing and analyzing geospatial processes and also holds potential application value in assisting regional management authorities in making disaster prevention and mitigation decisions.

In estimating the global carbon cycle, the net ecosystem exchange (NEE) is crucial. The understanding of the mechanism of interaction between NEE and various environmental factors of ecosystems has been very limited, and the interactions between the factors are intricate and complex, which leads to difficulties in accurately estimating NEE. In this study, we propose the A-DMLP (attention-deep multilayer perceptron)-deep learning model for NEE simulation as well as an interpretability study using the SHapley Additive exPlanations (SHAP) model. The attention mechanism was introduced into the deep multilayer perceptual machine, and the important information in the original input data was extracted using the attention mechanism. Good results were obtained on nine eddy covariance sites in China. The model was also compared with the random forest, long short-term memory, deep neural network, and convolutional neural networks (1D) models to distinguish it from previous shallow machine learning models to estimate NEE, and the results show that deep learning models have great potential in NEE modeling. The SHAP method was used to investigate the relationship between the input features of the A-DMLP model and the simulated NEE, and to enhance the interpretability of the model. The results show that the normalized difference vegetation index, the enhanced vegetation index, and the leaf area index play a dominant role at most sites. This study provides new ideas and methods for analyzing the intricate relationship between NEE and environmental factors by introducing the SHAP interpretable model. These advancements are crucial in achieving carbon reduction targets.

This paper proposes a diversity identification method based on information fusion for quantitatively identifying mixed urban functional zones (UFZ), addressing the critical need for better city planning and management. This method integrates both social and physical sensing data, considering the frequency of urban functional occurrences and the intensity of human activity. Specifically, we extract “dynamic” human activity features from crowdsourced smart device data and “static” visual features from street view images. Based on the fused multi‐modal data, our method infers the large‐scale distribution of UFZs more accurately. We also create a standardized mixed UFZ dataset for model training and testing, which includes residential, commercial, public services, industrial, and ecological categories. In general, the method transforms the functional label recognition task into a probability distribution recognition task. It addresses complex land use distributions rather than simply assigning a single label to each zone. The result shows that our method could achieve a Cosine similarity of (0.542 ± 0.143), the lowest Chebyshev of (0.785 ± 0.043), and L1 distances of (0.264 ± 0.080), indicating more accurate and consistent predictions and closer match to true distributions.

In recent years, the evolution of digital twin technology has paved the way for the construction of intelligent holographic intersections. This can be facilitated by utilizing precise point clouds from roadside lidar. With its capability of real-time monitoring, lidar plays a crucial role in enhancing intersection perception, enabling precise detection and tracking of road objects, as well as providing accurate speed estimates. Despite the introduction of few roadside lidar datasets aimed at enhancing supervised learning algorithms, their applicability to intelligent intersection monitoring remains limited. To address this, this paper presents an Intelligent Intersections (Int2sec) dataset, which exhibits several salient features: 1) it encompasses a broad array of urban intersection scenarios accompanied by a substantial quantity of object annotations; 2) the deployment of dual lidar stations facilitates a thorough scanning of scenes, thereby ensuring expansive scene coverage and mitigating the mutual occlusion phenomenon amongst objects; and 3) the dataset not only catalogues the coordinates, dimensions, and orientations of objects but also encompasses additional attributes such as tracking IDs and real-time motion statuses. Furthermore, the paper evaluates the efficacy of various prominent benchmarking networks, providing a critical analysis and prospective for future research.