Urban change detection (CD) is crucial for informed decision-making but faces various challenges, including complex features, rapid changes, and extensive human interventions. These challenges underscore the urgent need for innovative multiclass CD (MCD) techniques that extensively incorporate deep learning (DL). Despite several successes achieved with the DL-based MCD methods, still certain shortcomings persist, including the disregard for spatial principles, which significantly hinders the seamless integration of geoscience-knowledge and artificial-intelligence. In this article, a novel DL model known as the position-aware graph-convolutional neural network (CNN) fusion network (PGCFN) is introduced, integrating spatial position encoding to effectively detect urban changes. The model’s first part encodes geospatial positions following Tobler’s first law (TFL) of geography. It then integrates encoded positions into an MCD model, combining a graph attention network (GAT) with a CNN to enhance performance. The model was tested on 0.5-m resolution remote sensing (RS) images, achieving an impressive minimum mean intersection over union (MIoU) score of 91.20%. Additionally, the model’s position-aware graph attention module exhibited a strong emphasis on geographic proximity when evaluating connections between superpixels. Overall, these findings affirm that our model could effectively addresses urban CD challenges and significantly enhances the integration of geoscience knowledge and artificial intelligence (AI).

The challenge of spatial resource allocation is pervasive across various domains such as transportation, industry, and daily life. As the scale of real-world issues continues to expand and demands for real-time solutions increase, traditional algorithms face significant computational pressures, struggling to achieve optimal efficiency and real-time capabilities. In recent years, with the escalating computational power of computers, the remarkable achievements of reinforcement learning in domains like Go and robotics have demonstrated its robust learning and sequential decision-making capabilities. Given these advancements, there has been a surge in novel methods employing reinforcement learning to tackle spatial resource allocation problems. These methods exhibit advantages such as rapid solution convergence and strong model generalization abilities, offering a new perspective on resolving spatial resource allocation problems. Despite the progress, reinforcement learning still faces hurdles when it comes to spatial resource allocation. There remains a gap in its ability to fully grasp the diversity and intricacy of real-world resources. The environmental models used in reinforcement learning may not always capture the spatial dynamics accurately. Moreover, in situations laden with strict and numerous constraints, reinforcement learning can sometimes fall short in offering feasible strategies. Consequently, this paper is dedicated to summarizing and reviewing current theoretical approaches and practical research that utilize reinforcement learning to address issues pertaining to spatial resource allocation. In addition, the paper accentuates several unresolved challenges that urgently necessitate future focus and exploration within this realm and proposes viable approaches for these challenges. This research furnishes valuable insights that may assist scholars in gaining a more nuanced understanding of the problems, opportunities, and potential directions concerning the application of reinforcement learning in spatial resource allocation.

Studying population prediction under micro-spatiotemporal granularity is of great significance for modern and refined urban traffic management and emergency response to disasters. Existing population studies are mostly based on census and statistical yearbook data due to the limitation of data collecting methods. However, with the advent of techniques in this information age, new emerging data sources with fine granularity and large sample sizes have provided rich materials and unique venues for population research. This article presents a new population prediction model with micro-spatiotemporal granularity based on the long short-term memory (LSTM) and cellular automata (CA) models. We aim at designing a hybrid data-driven model with good adaptability and scalability, which can be used in more refined population prediction. We not only try to integrate these two models, aiming to fully mine the spatiotemporal characteristics, but also propose a method that fuses multi-source geographic data. We tested its functionality using the data from Chongming District, Shanghai, China. The results demonstrated that, among all scenarios, the model trained by three consecutive days (ordinary dates), with the granularity of one hour, incorporated with road networks, achieves the best performance (0.905 as the mean absolute error) and generalization capability.

Spatial resource allocation is a multi-objective spatial optimization problem with multiple constraints. The division of school districts is a classic problem of spatial resource allocation. This paper proposes a new dynamically districting optimization method based on deep reinforcement learning to optimize the global effect of school districting. In the proposed method, the school district’s constantly adjusted allocation process is regarded as a multi-step Markov decision-making process. The method combines the advantages of a deep convolutional neural network with reinforcement learning for real-time response and flexibility, and directly learns behavioural policies based on the input of changing school district states. According to various constraints, this algorithm optimizes the distance of students to school and the utilization rate of schools, and it proposes a better allocation plan. To demonstrate its validity, the proposed method was evaluated using real datasets of two school districts in the United States. The experimental results studied in six different scenarios show that, compared with traditional algorithms, the new proposed method requires less prior knowledge and is globally optimal, and can provide a better allocation plan for school districting, which reduces the distance between students and schools and balances the utilization rate of schools.

Advances in positioning and communicating technologies make it possible to collect large volumes of taxi trajectory data, quickly providing a complete picture of the ground traffic systems and thus being applied to different fields. However, there are still challenges for data users to handle such big data. In view of this, we have developed a software system named XStar to deal with trajectory big data. Its core is a scalable index and storage structure. Based on it, raw data can be saved in a more compact scheme and accessed more efficiently. A real taxi trajectory dataset is employed to demonstrate its performance. In general, XStar facilitates processing and analyzing trajectory data affordably and straightforwardly. Since its release in Jan. 2019, it has received downloads of over 4000 by May 2021. More analytical functions are being developed.