Great strides have been made in Geographic Information Systems (GIS) research over the past half-century. However, this progress has created both opportunities and challenges. From a geographic perspective, certain challenges remain, including the modelling of geographic-featured environments with GIS data model, the enhancement of GIS’s analysis functions for comprehensive geographic analysis and achieving human-oriented geographic information presentation. Several basic theoretical and technical ideas that follow the workflow and processes of geographic information induction, geographic scenario modelling, geographic process analysis and geographic environment representation are proposed to fill the gaps between GIS and geography. We also call for designing methods for big geographic data-oriented analysis, making best use of videos and developing virtual geographic scenario-based GIS for further evolution.

Rooftop photovoltaics (RPVs) are crucial in achieving energy transition and climate goals, especially in cities with high building density and substantial energy consumption. Estimating RPV carbon mitigation potential at the city level of an entire large country is challenging given difficulties in assessing rooftop area. Here, using multi-source heterogeneous geospatial data and machine learning regression, we identify a total of 65,962 km² rooftop area in 2020 for 354 Chinese cities, which represents 4 billion tons of carbon mitigation under ideal assumptions. Considering urban land expansion and power mix transformation, the potential remains at 3-4 billion tons in 2030, when China plans to reach its carbon peak. However, most cities have exploited less than 1% of their potential. We provide analysis of geographical endowment to better support future practice. Our study provides critical insights for targeted RPV development in China and can serve as a foundation for similar work in other countries.

Virtual Geographic Environments (VGEs) are proposed as a new generation of geographic analysis tool to contribute to human understanding of the geographic world and assist in solving geographic problems at a deeper level. The development of VGEs is focused on meeting the three scientific requirements of Geographic Information Science (GIScience) — multi-dimensional visualization, dynamic phenomenon simulation, and public participation. To provide a clearer image that improves user understanding of VGEs and to contribute to future scientific development, this article reviews several aspects of VGEs. First, the evolutionary process from maps to previous GISystems and then to VGEs is illustrated, with a particular focus on the reasons VGEs were created. Then, extended from the conceptual framework and the components of a complete VGE, three use cases are identified that together encompass the current state of VGEs at different application levels: 1) a tool for geo-object-based multi-dimensional spatial analysis and multi-channel interaction, 2) a platform for geo-process-based simulation of dynamic geographic phenomena, and 3) a workspace for multi-participant-based collaborative geographic experiments. Based on the above analysis, the differences between VGEs and other similar platforms are discussed to draw their clear boundaries. Finally, a short summary of the limitations of current VGEs is given, and future directions are proposed to facilitate ongoing progress toward forming a comprehensive version of VGEs

Reliable information on building rooftops is crucial for utilizing limited urban space effectively. In recent decades, the demand for accurate and up-to-date data on the areas of rooftops on a large-scale is increasing. However, obtaining these data is challenging due to the limited capability of conventional computer vision methods and the high cost of 3D modeling involving aerial photogrammetry. In this study, a geospatial artificial intelligence framework is presented to obtain data for rooftops using high-resolution open-access remote sensing imagery. This framework is used to generate vectorized data for rooftops in 90 cities in China. The data was validated on test samples of 180 km²across different regions with spatial resolution, overall accuracy, and F1 score of 1 m, 97.95%, and 83.11%, respectively. In addition, the generated rooftop area conforms to the urban morphological characteristics and reflects urbanization level. These results demonstrate that the generated dataset can be used for data support and decision-making that can facilitate sustainable urban development effectively.

A virtual geographic environment (VGE) is a type of workspace for computer-aided geographic experiments (CAGEs) and geographic analyses. By supporting geo-visualization, geo-simulation, geo-collaboration, and human participation, it provides open virtual environments that correspond to the real world to assist computer-aided geographic experiments involving both the physical and human dimensions. Based on a discussion of how VGEs can contribute to CAGEs and geographic analyses, this article proposes a clear, systematic framework for VGEs. Four subenvironments are discussed according to their different functions, pertinent issues, and corresponding solutions: (1) the data environment, (2) the modeling and simulation environment, (3) the interactive environment, and (4) the collaborative environment. Furthermore, a case on the simulation of air pollution and its analysis at different geographic scales is used to demonstrate VGEs’ ability to facilitate computer-aided geographic experiments.