Research on Supply and Demand Matching Optimization for Large-scale E-commerce Platforms
Urban land use information reflects social and economic functions and activities. It is an important base for urban planning and regional management, for solving urban problems and for developing cities scientifically and rationally. Meanwhile, land use reflects regional industries, which can help relevant institutions to reasonably choose industrial upgrading and enhance regional economic competitiveness. However, types of urban land use are complex and diverse, and manual field investigation is time-consuming and laborious, and detailed urban land use data is usually undisclosured. To handle those problems, plenty of researches make full use of remote sensing images to obtain urban land use information, which is a significant field of remote sensing applications. Moreover, thanks to rapid development of communication technology, abundant spatial data of geographic significance is available, such as heat map data, social media data, and point of interest data, which could also make a contribution in land use analysis. Those multi-modal data provide a solid foundation and a convenient access to study urban spatial structure.
Recent advances in remote sensing and machine learning technologies have contributed to the mapping and monitoring of multi-scale urban land uses, yet there lacks a holistic mapping framework that is compatible with various demands. Moreover, land use mix has evolved to be a key component in modern urban settings, but few have explicitly measured the spatial complexity of land use or quantitively uncovered its driving forces.
- Geographic representation modeling for heterogeneous multimodal data based on remote sensing and multi-source geographic data
- Land Parcel Division and Land Use Classification
- Industries Classification and Recognition
- Integrated processing system of remote sensing images and multi-source geographic data
Related Research Topics
- Remote sensing image processing and Representation
- Spatiotemporal data representation
- Geographic text representation, especially delivery address text
- Multimodal representation based on image & text & spatiotemporal data
- Zero-shot learning and few-shot learning
- Graph embedding on urban computing