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.

Monitoring Spatiotemporal Changes of Impervious Surfaces in Beijing City Using Random Forest Algorithm and Textural Features

“…This study adopted the VIS, NIR, and SWIR bands of Landsat 5, 7, and 8 images. Some recent studies are denoting that the thermal bands of Landsat 8 [46].and Luojia-1 nighttime light data [47] help classify urban land covers. Thus, these data can be considered as the input features to improve the classification of RF in further work.…”

Identifying Urban Functional Areas in China’s Changchun City from Sentinel-2 Images and Social Sensing Data

“…Social sensing data used in the study include POI and mobile phone signaling data. Baidu map, founded in 2005, has become the main online map system in China, providing intelligent positioning and POI retrieval services [37], with high POI coverage and a total volume of 150 million. Therefore, the Baidu Map application program interface (API) was used to obtain POI data of the research area in 2020 (Figure 2).…”

Urban land-use analysis using proximate sensing imagery: a survey

“…Their experiment achieved an accuracy of 49.54% on image-level land-use classification and over 29% recall at the parcel level classification on their 45classes data set, which provided a strong baseline for fine-grained land-use classification on noisy data set. In the most recent work, Chang et al (2020) leveraged the semantic segmentation result of GSV images to construct the representation for urban parcels include the features denoting the mean kernel density of the green visual ratio, openness, enclosure, etc. The features extracted from GSV images are integrated with the features extracted from Luojia-1, Sentinel-2A images, and Baidu POI to construct the urban parcel features, and the results are sent into a Random Tree Model to make the final prediction.…”