Quantifying and Qualifying Urban Green by Integrating Remote Sensing, GIS, and Social Science Method

2007 ◽  
pp. 93-105 ◽  
Author(s):  
Stefan Lang ◽  
Elisabeth Schöpfer ◽  
Daniel Hölbling ◽  
Thomas Blaschke ◽  
Matthias Moeller ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Social Science ◽  
Science Method ◽  
Urban Green

Fire, People and Pixels: Linking Social Science and Remote Sensing to Understand Underlying Causes and Impacts of Fires in Indonesia

Human Ecology ◽  
2005 ◽  
Vol 33 (4) ◽  
pp. 465-504 ◽  
Author(s):  
Rona A. Dennis ◽  
Judith Mayer ◽  
Grahame Applegate ◽  
Unna Chokkalingam ◽  
Carol J. Pierce Colfer ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Social Science ◽  
Underlying Causes

scholarly journals Urban Green Plastic Cover Mapping Based on VHR Remote Sensing Images and a Deep Semi-Supervised Learning Framework

2020 ◽  
Vol 9 (9) ◽  
pp. 527
Author(s):  
Jiantao Liu ◽  
Quanlong Feng ◽  
Ying Wang ◽  
Bayartungalag Batsaikhan ◽  
Jianhua Gong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Supervised Learning ◽  
Local Governments ◽  
Learning Strategy ◽  
Future Research ◽  
Urban Landscapes ◽  
Plastic Mulch ◽  
Urban Green ◽  
Learning Framework ◽  
Multi Scale

With the rapid process of both urban sprawl and urban renewal, large numbers of old buildings have been demolished in China, leading to wide spread construction sites, which could cause severe dust contamination. To alleviate the accompanied dust pollution, green plastic mulch has been widely used by local governments of China. Therefore, timely and accurate mapping of urban green plastic covered regions is of great significance to both urban environmental management and the understanding of urban growth status. However, the complex spatial patterns of the urban landscape make it challenging to accurately identify these areas of green plastic cover. To tackle this issue, we propose a deep semi-supervised learning framework for green plastic cover mapping using very high resolution (VHR) remote sensing imagery. Specifically, a multi-scale deformable convolution neural network (CNN) was exploited to learn representative and discriminative features under complex urban landscapes. Afterwards, a semi-supervised learning strategy was proposed to integrate the limited labeled data and massive unlabeled data for model co-training. Experimental results indicate that the proposed method could accurately identify green plastic-covered regions in Jinan with an overall accuracy (OA) of 91.63%. An ablation study indicated that, compared with supervised learning, the semi-supervised learning strategy in this study could increase the OA by 6.38%. Moreover, the multi-scale deformable CNN outperforms several classic CNN models in the computer vision field. The proposed method is the first attempt to map urban green plastic-covered regions based on deep learning, which could serve as a baseline and useful reference for future research.

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.

Biophysical factors of remote sensing approach in urban green analysis

2014 ◽  
Vol 29 (7) ◽  
pp. 807-818 ◽  
Author(s):  
Illyani Ibrahim ◽  
Azizan Abu Samah ◽  
Rosmadi Fauzi
Keyword(s):  
Remote Sensing ◽  
Urban Green ◽  
Biophysical Factors

scholarly journals EFFICIENCY OF AERIAL LASER SCANNING OF THE TERRITORY WHEN MONITORING URBAN GREEN SPACES

2021 ◽  
Vol 4 (2) ◽  
pp. 31-34
Author(s):  
Ekaterina A. Vasil'eva
Keyword(s):  
Remote Sensing ◽  
Laser Scanning ◽  
Green Spaces ◽  
Environmental Legislation ◽  
Urban Green ◽  
Urban Green Spaces ◽  
Aerial Laser Scanning ◽  
State Register ◽  
Inventory And Monitoring

The article describes the role of remote sensing technologies in monitoring urban green spaces. The positive aspects of the use of air laser scanning in the inventory and monitoring of urban green spaces are listed. The role of urban green spaces in the formation of an environmentally friendly urban environment is briefly described. Insufficient elaboration of the regulatory and legal documentation in the field of registration of urban green spaces in the Unified State Register of Real Estate Objects was noted. It is emphasized that the lack of approaches to the consideration of urban green spaces as independent cadastral objects entails numerous violations in the field of environmental and environmental legislation of settlements. A solution to this problem is proposed, which consists in the mandatory accounting of cadastral data on the land plot under the urban green spaces when maintaining the urban green spaces monitoring database.

Sign in / Sign up

Export Citation Format

Share Document