Fusing High-Spatial-Resolution Remotely Sensed Imagery and OpenStreetMap Data for Land Cover Classification Over Urban Areas

Abstract

Land cover classification of urban areas is critical for understanding the urban environment. High-resolution remotely sensed imagery provides abundant, detailed spatial information for urban classification. In the meantime, OpenStreetMap (OSM) data, as typical crowd-sourced geographical information, have been an emerging data source for obtaining urban information. In this context, a land cover classification method that fuses high-resolution remotely sensed imagery and OSM data is proposed. Training samples were generated by integrating the OSM data and multiple information indexes. OSM data, which contain class attributes and location information of urban objects, served as the labels of initial training samples. Multiple information indexes that reflect spectral and spatial characteristics of different classes were utilized to improve the training set. Morphological attribute profiles were used because the structural and contextual information of images was effective in distinguishing the classes with similar spectral characteristics. Moreover, a road superimposition strategy that considers road hierarchy was developed because OSM data provide road information with high completeness in the urban area. Experiments were conducted on the data captured over Wuhan city, and three state-of-the-art approaches were adopted for comparison. Results show that the proposed approach obtains satisfactory results and outperforms the other comparative approaches.

Automatic Detection of Spatiotemporal Urban Expansion Patterns by Fusing OSM and Landsat Data in Kathmandu

“…These data have been widely used over the last decade as a supplementary source of information for land-use mapping, and have been especially valuable in classifying artificial surfaces [29]. The most common use is combining OSM and satellite images to extract information related to the urban environment [30][31][32][33]. For example, useful and good-quality land use/land cover (LULC) information was extracted using OSM and GlobeLand30 in Kathmandu and Dar es Salaam with good results Fonte et al [34].…”

Assessing OpenStreetMap Completeness for Management of Natural Disaster by Means of Remote Sensing: A Case Study of Three Small Island States

“…Our objective here is to rely on remotely sensed measurements to predict the coverage of OSM building footprints. Previous studies have utilized OSM data for different remote sensing applications, for example, for classification of urban areas [40] or for semantic labeling of aerial and satellite images [41]. Despite significant progress in the field of machine learning and the increasing availability of satellite imagery, there is still a scarcity of studies aiming to utilize remotely sensed observations to predict the completeness of OSM building footprints.…”

Building Extraction Using Orthophotos and Dense Point Cloud Derived from Visual Band Aerial Imagery Based on Machine Learning and Segmentation

“…The final product of aerial photography, the orthophotograph, which is free of distortions, has only a limited ability as input data in image classifications to discriminate the objects of the urban environment. Although cameras having near-infrared band (NIR) became common in recent years due to multispectral cameras, and the NIR band is one of the most important input data to separate the vegetation from the buildings, usually as a spectral index (normalized difference vegetation index, NDVI) [43,44], only the latest surveys are conducted with this opportunity. Photogrammetric image processing provides a possible improvement in the accuracy of image classification.…”