Rural settlements extraction based on deep learning from high spatial resolution remote sensing imagery

2020 ◽  
Author(s):  
Qi Li ◽  
Liang Hong ◽  
Huiling Sun
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Rural Settlements ◽  
Remote Sensing Imagery

Multiscale U-Shaped CNN Building Instance Extraction Framework With Edge Constraint for High-Spatial-Resolution Remote Sensing Imagery

2020 ◽  
pp. 1-15
Author(s):  
Yuanyuan Liu ◽  
Dingyuan Chen ◽  
Ailong Ma ◽  
Yanfei Zhong ◽  
Fang Fang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remote Sensing Imagery

scholarly journals Mapping Urban Functional Zones by Integrating Very High Spatial Resolution Remote Sensing Imagery and Points of Interest: A Case Study of Xiamen, China

2018 ◽  
Vol 10 (11) ◽  
pp. 1737 ◽  
Author(s):  
Jinchao Song ◽  
Tao Lin ◽  
Xinhu Li ◽  
Alexander V. Prishchepov
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remote Sensing Images ◽  
Remote Sensing Imagery ◽  
Points Of Interest ◽  
Very High Spatial Resolution ◽  
Functional Zones ◽  
Very High

Fine-scale, accurate intra-urban functional zones (urban land use) are important for applications that rely on exploring urban dynamic and complexity. However, current methods of mapping functional zones in built-up areas with high spatial resolution remote sensing images are incomplete due to a lack of social attributes. To address this issue, this paper explores a novel approach to mapping urban functional zones by integrating points of interest (POIs) with social properties and very high spatial resolution remote sensing imagery with natural attributes, and classifying urban function as residence zones, transportation zones, convenience shops, shopping centers, factory zones, companies, and public service zones. First, non-built and built-up areas were classified using high spatial resolution remote sensing images. Second, the built-up areas were segmented using an object-based approach by utilizing building rooftop characteristics (reflectance and shapes). At the same time, the functional POIs of the segments were identified to determine the functional attributes of the segmented polygon. Third, the functional values—the mean priority of the functions in a road-based parcel—were calculated by functional segments and segmental weight coefficients. This method was demonstrated on Xiamen Island, China with an overall accuracy of 78.47% and with a kappa coefficient of 74.52%. The proposed approach could be easily applied in other parts of the world where social data and high spatial resolution imagery are available and improve accuracy when automatically mapping urban functional zones using remote sensing imagery. It will also potentially provide large-scale land-use information.

High Spatial Resolution Remote Sensing Image Classification Based on Deep Learning

2016 ◽  
Vol 36 (4) ◽  
pp. 0428001 ◽  
Author(s):  
刘大伟 Liu Dawei ◽  
韩玲 Han Ling ◽  
韩晓勇 Han Xiaoyong
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remote Sensing Image ◽  
Remote Sensing Image Classification
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