A New Remote Sensing Images and Point-of-Interest Fused (RPF) Model for Sensing Urban Functional Regions

Abstract

For urban planning and environmental monitoring, it is essential to understand the diversity and complexity of cities to identify urban functional regions accurately and widely. However, the existing methods developed in the literature for identifying urban functional regions have mainly been focused on single remote sensing image data or social sensing data. The multi-dimensional information which was attained from various data source and could reflect the attribute or function about the urban functional regions that could be lost in some extent. To sense urban functional regions comprehensively and accurately, we developed a multi-mode framework through the integration of spatial geographic characteristics of remote sensing images and the functional distribution characteristics of social sensing data of Point-of-Interest (POI). In this proposed framework, a deep multi-scale neural network was developed first for the functional recognition of remote sensing images in urban areas, which explored the geographic feature information implicated in remote sensing. Second, the POI function distribution was analyzed in different functional areas of the city, then the potential relationship between POI data categories and urban region functions was explored based on the distance metric. A new RPF module is further deployed to fuse the two characteristics in different dimensions and improve the identification performance of urban region functions. The experimental results demonstrated that the proposed method can efficiently achieve the accuracy of 82.14% in the recognition of functional regions. It showed the great usability of the proposed framework in the identification of urban functional regions and the potential to be applied in a wide range of areas.

Decision-Level and Feature-Level Integration of Remote Sensing and Geospatial Big Data for Urban Land Use Mapping

“…It should be noted that the objective of this study is not to quantitatively compare the performance of the two methods but to illustrate their advantages and disadvantages by applying them to the mapping of the urban land use situation in Hangzhou city. The results in this study, which show that DI-based classification performs better than FI-based classification, may not apply to other circumstances [36,64]. Given that our study area is located in eastern China, where the diversity of urban land use types has a higher level of complexity than other regions in China, our mapping result is probably not representative of other regions.…”

“…Zhao et al [28] generated land cover types by training RS images with the semantic elements derived from OpenStreetMap (OSM) data, then identified each building through semantic classification by using POI. Xu et al [36] extracted geographic information from RS and functional distribution from GBD (Gaode POI), then combined them by assigning different weights for urban land use mapping in China. In the DI-based method, the RS and GBD features are calculated and processed separately, avoiding the feature conflicting issues.…”

“…As a prominent example of the GBD that attracts the most users, POI contain abundant information including land use category, geographic location, and other features (e.g., address, telephone, and postcode) [36]. A total number of 28,898 available records of POI within Hangzhou city in 2019 was collected via application programming interfaces (APIs) provided by Gaode Map Services.…”

“…The urban land use classification system used for aggregating initial POI types were shown in Table 1 (https://lbs.amap.com, accessed on 14 December 2019). Moreover, according to previous research, the urban area could be divided into six categories, including institution, residence, commercial land, industrial land, open space, and transportation [25,36]. Since we have excluded the road and river layers for generating urban parcels, there are five categories left.…”

An SOE-Based Learning Framework Using Multisource Big Data for Identifying Urban Functional Zones

“…2b. POIs have been proven to play an important role in functional zone recognition [33]- [35]. It is not generated by physical information on land surface, but attribute tags and geographic points by human economic activities.…”