Journal
REMOTE SENSING
Volume 10, Issue 10, Pages -Publisher
MDPI
DOI: 10.3390/rs10101553
Keywords
land use classification; semantic segmentation; aerial images; street view images; convolutional neural network (CNN); deep learning; data fusion
Categories
Funding
- International Doctoral Innovation Centre
- University of Nottingham
- UK Engineering and Physical Sciences Research Council [EP/L015463/1]
- National Natural Science Foundation of China [91546106]
- Shenzhen Future Industry Development Funding Program [201607281039561400]
- Shenzhen Scientific Research and Development Funding Program [JCYJ20170818092931604]
- China Scholarship Council [201708440434]
- Ningbo Science and Technology Bureau
- Ningbo Education Bureau
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Urban land use is key to rational urban planning and management. Traditional land use classification methods rely heavily on domain experts, which is both expensive and inefficient. In this paper, deep neural network-based approaches are presented to label urban land use at pixel level using high-resolution aerial images and ground-level street view images. We use a deep neural network to extract semantic features from sparsely distributed street view images and interpolate them in the spatial domain to match the spatial resolution of the aerial images, which are then fused together through a deep neural network for classifying land use categories. Our methods are tested on a large publicly available aerial and street view images dataset of New York City, and the results show that using aerial images alone can achieve relatively high classification accuracy, the ground-level street view images contain useful information for urban land use classification, and fusing street image features with aerial images can improve classification accuracy. Moreover, we present experimental studies to show that street view images add more values when the resolutions of the aerial images are lower, and we also present case studies to illustrate how street view images provide useful auxiliary information to aerial images to boost performances.
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