Extraction of the urban green space based on the high resolution remote sensing image

2009 ◽  
Author(s):  
Miaomiao Cheng ◽  
Hong Jiang ◽  
Jian Chen ◽  
Zheng Guo ◽  
Zishan Jiang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Green Space ◽  
Remote Sensing Image ◽  
Urban Green Space ◽  
Urban Green

scholarly journals A Novel Intelligent Classification Method for Urban Green Space Based on High-Resolution Remote Sensing Images

2020 ◽  
Vol 12 (22) ◽  
pp. 3845
Author(s):  
Zhiyu Xu ◽  
Yi Zhou ◽  
Shixin Wang ◽  
Litao Wang ◽  
Feng Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Classification Accuracy ◽  
Green Space ◽  
Urban Green Space ◽  
Classification Method ◽  
Deciduous Trees ◽  
Remote Sensing Images ◽  
Urban Green

The real-time, accurate, and refined monitoring of urban green space status information is of great significance in the construction of urban ecological environment and the improvement of urban ecological benefits. The high-resolution technology can provide abundant information of ground objects, which makes the information of urban green surface more complicated. The existing classification methods are challenging to meet the classification accuracy and automation requirements of high-resolution images. This paper proposed a deep learning classification method for urban green space based on phenological features constraints in order to make full use of the spectral and spatial information of green space provided by high-resolution remote sensing images (GaoFen-2) in different periods. The vegetation phenological features were added as auxiliary bands to the deep learning network for training and classification. We used the HRNet (High-Resolution Network) as our model and introduced the Focal Tversky Loss function to solve the sample imbalance problem. The experimental results show that the introduction of phenological features into HRNet model training can effectively improve urban green space classification accuracy by solving the problem of misclassification of evergreen and deciduous trees. The improvement rate of F1-Score of deciduous trees, evergreen trees, and grassland were 0.48%, 4.77%, and 3.93%, respectively, which proved that the combination of vegetation phenology and high-resolution remote sensing image can improve the results of deep learning urban green space classification.

Using high-resolution LiDAR data to quantify the three-dimensional structure of vegetation in urban green space

2016 ◽  
Vol 19 (4) ◽  
pp. 1749-1765 ◽  
Author(s):  
Rhiannon J. C. Caynes ◽  
Matthew G. E. Mitchell ◽  
Dan Sabrina Wu ◽  
Kasper Johansen ◽  
Jonathan R. Rhodes
Keyword(s):  
High Resolution ◽  
Green Space ◽  
Three Dimensional ◽  
Urban Green Space ◽  
Dimensional Structure ◽  
Lidar Data ◽  
Urban Green ◽  
Three Dimensional Structure

scholarly journals Remote Sensing–Based Urban Green Space Detection Using Marine Predators Algorithm Optimized Machine Learning Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Nhat-Duc Hoang ◽  
Xuan-Linh Tran
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Land Use ◽  
Green Space ◽  
Urban Land ◽  
Urban Green Space ◽  
Urban Land Use ◽  
Urban Green ◽  
Marine Predators ◽  
Sentinel 2

Information regarding the current status of urban green space is crucial for urban land-use planning and management. This study proposes a remote sensing and data-driven solution for urban green space detection at regional scale via employment of state-of-the-art metaheuristic and machine learning approaches. Remotely sensed data obtained from Sentinel 2 satellite in the study area of Da Nang city (Vietnam) are used to construct and verify an intelligent model that hybridizes Marine Predators Algorithm (MPA) and support vector machines (SVM). SVM are employed to generalize a decision boundary that separates features characterizing statistical measurements of remote sensing data into two categories of “green space” and “nongreen space”. The MPA metaheuristic is used to optimize the SVM training phase by identifying an appropriate set of the SVM’s hyperparameters including the penalty coefficient and the kernel function parameter. Experimental results show that the proposed model which processes information provided by all of the Sentinel 2 satellite’s spectral bands can deliver a better performance than those obtained from the model based on vegetation indices. With a good classification accuracy rate of roughly 93%, an F1 score = 0.93, and an area under the receiver operating characteristic = 0.98, the newly developed model is a promising tool to assist local authority to obtain up-to-date information on urban green space and develop plans of sustainable urban land use.

scholarly journals Social functional mapping of urban green space using remote sensing and social sensing data

2018 ◽  
Vol 146 ◽  
pp. 436-452 ◽  
Author(s):  
Wei Chen ◽  
Huiping Huang ◽  
Jinwei Dong ◽  
Yuan Zhang ◽  
Yichen Tian ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Green Space ◽  
Urban Green Space ◽  
Functional Mapping ◽  
Urban Green ◽  
Social Sensing ◽  
Sensing Data
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