Paper Info
Title
Mapping the Essential Urban Land Use in Changchun by Applying Random Forest and Multi-Source Geospatial Data
Abstract
Understanding urban spatial pattern of land use is of great significance to urban land management and resource allocation. Urban space has strong heterogeneity, and thus there were many researches focusing on the identification of urban land use. The emergence of multiple new types of geospatial data provide an opportunity to investigate the methods of mapping essential urban land use. The popularization of street view images represented by Baidu Maps is benificial to the rapid acquisition of high-precision street view data, which has attracted the attention of scholars in the field of urban research. In this study, OpenStreetMap (OSM) was used to delineate parcels which were recognized as basic mapping units. A semantic segmentation of street view images was combined to enrich the multi-dimensional description of urban parcels, together with point of interest (POI), Sentinel-2A, and Luojia-1 nighttime light data. Furthermore, random forest (RF) was applied to determine the urban land use categories. The results show that street view elements are related to urban land use in the perspective of spatial distribution. It is reasonable and feasible to describe urban parcels according to the characteristics of street view elements. Due to the participation of street view, the overall accuracy reaches 79.13%. The contribution of street view features to the optimal classification model reached 20.6%, which is more stable than POI features.
Year
DOI
Venue
2020
10.3390/rs12152488
REMOTE SENSING
Keywords
DocType
Volume
urban land use,street view,random forest,volunteered geographic information,Changchun
Journal
12
Issue
Citations 
PageRank 
15
1
0.35
References 
Authors
0
6
Name
Order
Citations
PageRank
Shouzhi Chang110.69
Zongming Wang27219.71
Dehua Mao3266.83
Kehan Guan410.35
Mingming Jia536641.64
Chaoqun Chen610.35