The formulation of mixed urban land uses is not only intended to find the ideal scenario of land use but also regarded as a way toward sustainable urban development. We propose a geo-semantic mining approach Traj2Vec to quantify the trajectories of residents as high-dimensional semantic vectors. Then, a random forest (RF) method is used to model the relationship between the semantic vectors and mixed urban land uses. The proposed Traj2Vec approach can obtain the highest accuracy (OA = 0.7733, kappa = 0.7245) in urban land-use classification and a high average proportion accuracy (64.0%) in capturing the proportions of urban land-use types. Diversity analysis indicates that Shenzhen has a high degree of mixed urban land use at the scale of a street block. By analyzing the mixing index and the travel distance, we find a weak but significant negative correlation between them ( $r=-0.107,p<0.001$), which not only confirms the conclusion that an increase in the degree of mixing will reduce the travel distances of residents but also verifies the mixing index. This suggests that urban planning should focus on mixed urban land uses, which can reduce the travel distances of residents, reduce energy consumption, and make cities more compact.

Cellular automata (CA) model is a useful tool for simulating spatiotemporal changes of land-use evolution. However, previous CA models have usually ignored the inhomogeneous and mixed land-use situations and simplified the land grid cell as one single land-use type. This study developed a multi-label (ML) convolutional neural network CA (ML-CNN-CA) model to simulate the complex evolution of mixed land uses. The multi-label learning strategy in the proposed model can formulize mixed land uses by labeling multiple land-use types to each grid cell. The transition rules of CA were modified to accommodate the multi-label learning strategy and mined by coupling a CNN network and three interactive mechanisms, including the neighborhood effect, adaptive inertia coefficient, and random factor. The proposed ML-CNN-CA model was applied and examined in a fast-developing city (Huizhou) in the China’s Pearl River Delta region for the mixed land-use simulation during 2009–2013 and 2013–2020. The ML-CNN-CA model with different CNN architectures and a previous artificial neural network (ANN)-based multi-label CA model were also implemented for comparisons. Results show the capability of the ML-CNN-CA model to simulate fine-scale mixed land-use changes with satisfactory performances. Specifically, the simulated mixed land-use patterns agree well with the actual land uses from the morphological perspective. Furthermore, the quantitative assessments demonstrate the performance of the proposed model, showing an accuracy value of 0.912 for 2009–2013 and 0.896 for 2013–2020, and a hamming loss value of 0.048 for 2009–2013 and 0.055 for 2013–2020. The comparisons also show the best performance of the ML-CNN-CA model with the VGG-based architecture and significant outperformance of the proposed model against the previous ANN-based CA model. Multiple sensitivity analyses were also conducted to investigate the uncertainty of the proposed model. The ML-CNN-CA model proposed in this study can provide a new tool for better simulation of fine-scale mixed land-use changes and is expected to help formulate urban planning guidelines and achieve sustainable urban development.

The rapid development of information technology and location techniques not only leads to an increasing growth of massive geospatial big data but also raises the attention of using these data to complement with remote sensing images. Many efforts have been made to utilize geospatial big data to identify human activity patterns and carry out urban and environmental researches, integrating with remote sensing images. Nonetheless, there are still many issues, including the representativeness and locality of geospatial big data, as well as the fusion methods, remain to be further explored. In this article, we first reviewed the innovation and proceedings of data mining and analyzing techniques, as well as remote sensing applications driven by geospatial big data. Besides, two popular concepts, namely, “Social Sensing” and “Urban Computing,” were briefly introduced. Then, we highlighted the role of geospatial big data in mining human activity dynamics and socioeconomic characteristics, and the feasibility of combining with remote sensing data for various studies. Lastly, we presented some empirical case studies on the confluence of remote sensing and geospatial big data in land use extraction, environmental and disaster monitoring, as well as socioeconomic dynamics sensing. The provided examples and discussion demonstrated the high efficiency and complementarity of the integration of remote sensing and geospatial big data, which benefits decision making from multiple perspectives and scales.

Mixed land use has been widely used as a planning tool to improve the functionality of cities. However, depicting mixed land use is rather difficult due to its complexities. Previous studies have decomposed urban land areas using either remote sensing images or geospatial big data. Few studies have combined these two data sources because of the lack of methodologies. This article proposed an end-to-end two-stream convolutional neural network (CNN) for combining features (CF-CNN) to estimate the proportion of mixed land use by integrating high spatial resolution (HSR) images and geospatial big data of real-time Tencent user density (RTUD) data. Two deep learning networks, one for image information extraction and other for human activity-related information extraction, are used to construct two branches of CF-CNN. The mixed land use can be described by calculating the proportions of each land use type at the street-block level. Compared with methods for using single-source data, CF-CNN obtained the highest classification accuracy. We further applied the Shannon diversity index (SHDI) to quantify the agglomerated urban mixed land use. The Spearman correlation coefficients among the SHDI, community distance, and neighborhood vibrancy were calculated to verify the effectiveness of the mixed land use composition. Our framework provided an alternative way of identifying mixed land use structures by integrating multisource data.

Fine knowledge of the spatiotemporal distribution of the population is fundamental in a wide range of fields, including resource management, disaster response, public health, and urban planning. The United Nations’ Sustainable Development Goals also require the accurate and timely assessment of where people live to formulate, implement, and monitor sustainable development policies. However, due to the lack of appropriate auxiliary datasets and effective methodological frameworks, there are rarely continuous multi-temporal gridded population data over a long historical period to aid in our understanding of the spatiotemporal evolution of the population. In this study, we developed a framework integrating a ResNet-N deep learning architecture, considering neighborhood effects with a vast number of Landsat-5 images from Google Earth Engine for population mapping, to overcome both the data and methodology obstacles associated with rapid multi-temporal population mapping over a long historical period at a large scale. Using this proposed framework in China, we mapped fine-scale multi-temporal gridded population data (1 km × 1 km) of China for the 1985–2010 period with a 5-year interval. The produced multi-temporal population data were validated with available census data and achieved comparable performance. By analyzing the multi-temporal population grids, we revealed the spatiotemporal evolution of population distribution from 1985 to 2010 in China with the characteristic of concentration of the population in big cities and the contraction of small- and medium-sized cities. The framework proposed in this study demonstrates the feasibility of mapping multi-temporal gridded population distribution at a large scale over a long period in a timely and low-cost manner, which is particularly useful in low-income and data-poor areas.

The precise prediction of multi-scale traffic is a ubiquitous challenge in the urbanization process for car owners, road administrators, and governments. In the case of complex road networks, current and past traffic information from both upstream and downstream roads are crucial since various road networks have different semantic information about traffic. Rationalizing the utilization of semantic information can realize short-term, long-term, and unseen road traffic prediction. As the demands of multi-scale traffic analysis increase, on-demand interactions and visualizations are expected to be available for transportation participants. We have designed a multi-scale traffic generation system, namely DynaGraph, using a multi-agent framework to process multi-scale traffic data, conduct multi-scale traffic analysis, and present multi-scale visualization results. DynaGraph consists of three essential AI agents: 1) a text-to-demand agent with deep thinking ability to interact with users and extract prediction tasks through texts or voice; 2) a traffic prediction agent that leverages multi-scale traffic data to generate temporal features and similarity, and fuse them with limited spatial features and similarity, to achieve accurate prediction of three tasks; and 3) a suggestion and visualization agent that uses the prediction results to generate suggestions and visualizations, providing users with a comprehensive understanding of traffic conditions. Our DynaGraph as a generic system focuses on addressing concerns about traffic prediction from transportation participants, and conducted extensive experiments on five real-world road datasets to demonstrate its competitive prediction accuracy, scalability, and superior interactive performance.