scholarly journals Concatenated Residual Attention UNet for Semantic Segmentation of Urban Green Space

Forests ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1441
Author(s):  
Guoqiang Men ◽  
Guojin He ◽  
Guizhou Wang
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Green Space ◽  
Spatial Information ◽  
Urban Green Space ◽  
Remote Sensing Images ◽  
Large Area ◽  
Urban Green ◽  
Urban Green Spaces ◽  
Residual Structure

Urban green space is generally considered a significant component of the urban ecological environment system, which serves to improve the quality of the urban environment and provides various guarantees for the sustainable development of the city. Remote sensing provides an effective method for real-time mapping and monitoring of urban green space changes in a large area. However, with the continuous improvement of the spatial resolution of remote sensing images, traditional classification methods cannot accurately obtain the spectral and spatial information of urban green spaces. Due to complex urban background and numerous shadows, there are mixed classifications for the extraction of cultivated land, grassland and other ground features, implying that limitations exist in traditional methods. At present, deep learning methods have shown great potential to tackle this challenge. In this research, we proposed a novel model called Concatenated Residual Attention UNet (CRAUNet), which combines the residual structure and channel attention mechanism, and applied it to the data source composed of GaoFen-1 remote sensing images in the Shenzhen City. Firstly, the improved residual structure is used to make it retain more feature information of the original image during the feature extraction process, then the Convolutional Block Channel Attention (CBCA) module is applied to enhance the extraction of deep convolution features by strengthening the effective green space features and suppressing invalid features through the interdependence of modeling channels.-Finally, the high-resolution feature map is restored through upsampling operation by the decoder. The experimental results show that compared with other methods, CRAUNet achieves the best performance. Especially, our method is less susceptible to the noise and preserves more complete segmented edge details. The pixel accuracy (PA) and mean intersection over union (MIoU) of our approach have reached 97.34% and 94.77%, which shows great applicability in regional large-scale mapping.

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.

scholarly journals THE CITY OF ADDIS ABABA FROM ‘FOREST CITY’ TO ‘URBAN HEAT ISLAND’: AN ANALYSIS OF URBAN GREEN SPACE DYNAMICS USING REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEM

2017 ◽  
Vol 10 (2) ◽  
pp. 254-262
Author(s):  
Mathias Tesfaye Abebe ◽  
Tebarek Lika Megento
Keyword(s):  
Remote Sensing ◽  
Green Space ◽  
Addis Ababa ◽  
Urban Green Space ◽  
Green Spaces ◽  
Urban Green ◽  
Urban Green Spaces ◽  
Gis Tools ◽  
Remote Sensing Techniques ◽  
Space Dynamics

The unprecedented rate of urban growth in developing countries causes various problems such as deficiency in public infrastructure services, lack of green spaces and inadequate service provisions. This study applies GIS tools and remote sensing techniques to assess the effects of urban development on urban green space in Ethiopia’s capital. Spatial and non-spatial datasets were collected from different organizations and processed using GIS tools and remote sensing techniques for land use/ land cover classification and analysis. The analysis demonstrated shrinking of urban green spaces- plantations, forestland, grassland and cultivated land (at annual rates of 5.9%, 3.3%, 5.4% and 3.7 % respectively) by 82.1%, 62.1%, 78.8 and 65.8 % respectively during the past three decades (1986-2015) whereas built-up and transport areas increased at annual rate of 5.7% and 1.3% and consumed 419% and 47% of the city’s total area respectively.

scholarly journals THE CITY OF ADDIS ABABA FROM ‘FOREST CITY’ TO ‘URBAN HEAT ISLAND’: AN ANALYSIS OF URBAN GREEN SPACE DYNAMICS USING REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEM

2017 ◽  
Vol 10 (2) ◽  
pp. 254-262 ◽  
Author(s):  
Mathias Tesfaye Abebe ◽  
Tebarek Lika Megento
Keyword(s):  
Remote Sensing ◽  
Green Space ◽  
Addis Ababa ◽  
Urban Green Space ◽  
Green Spaces ◽  
Urban Green ◽  
Urban Green Spaces ◽  
Gis Tools ◽  
Remote Sensing Techniques ◽  
Space Dynamics

The unprecedented rate of urban growth in developing countries causes various problems such as deficiency in public infrastructure services, lack of green spaces and inadequate service provisions. This study applies GIS tools and remote sensing techniques to assess the effects of urban development on urban green space in Ethiopia’s capital. Spatial and non-spatial datasets were collected from different organizations and processed using GIS tools and remote sensing techniques for land use/ land cover classification and analysis. The analysis demonstrated shrinking of urban green spaces- plantations, forestland, grassland and cultivated land (at annual rates of 5.9%, 3.3%, 5.4% and 3.7 % respectively) by 82.1%, 62.1%, 78.8 and 65.8 % respectively during the past three decades (1986-2015) whereas built-up and transport areas increased at annual rate of 5.7% and 1.3% and consumed 419% and 47% of the city’s total area respectively.

Noise perception assessment in urban green spaces using soundwalk approach

2021 ◽  
Vol 263 (1) ◽  
pp. 5780-5791
Author(s):  
Omid Samani ◽  
Verena Zapf ◽  
M. Ercan Altinsoy
Keyword(s):  
Green Space ◽  
Traffic Noise ◽  
Urban Green Space ◽  
Green Spaces ◽  
Noise Emission ◽  
Visual Effect ◽  
Technical Noise ◽  
Urban Green ◽  
Urban Green Spaces ◽  
The City

Urban green spaces are intended to provide citizens with calm environments free of annoying city noises. This requires a thorough understanding of noise emission and related exposure to sounds in green spaces. This research investigates noise perception in various spots in an urban green space. For this purpose, the study has been conducted in the grand garden of the city of Dresden. The garden covers 1.8 square kilometers of various landscapes, including water streams, park railways, fountains, bridges, roads for bicycles and pedestrians etc. Noise perception was investigated at eleven spots with emphasis on four noise types: nature noise, human noise, traffic noise, and technical noise. In parallel, audio-visual recordings were conducted for each spot to identify the connection between the perceptual measures and the psychoacoustic parameters. These spots are categorized based on the resulting perception and psychoacoustic parameters. In addition, the visual effect of each spot on final perception is investigated. Eventually, annoyance for each spot is identified based on the corresponding participants' perception and is associated with the relevant psychoacoustic parameters.

HUBUNGAN KARAKTERISTIK LANSKAP DENGAN PREFERENSI SITUS OVIPOSISI KUPU-KUPU DI RUANG TERBUKA HIJAU KAWASAN JAKARTA TIMUR

Jurnal BIOMA ◽  
2015 ◽  
Vol 11 (1) ◽  
pp. 22
Author(s):  
PUTRI DIANA ◽  
REFIRMAN DJAMAHAR ◽  
HANUM ISFAENI
Keyword(s):  
Green Space ◽  
Correlation Coefficients ◽  
Urban Green Space ◽  
Oviposition Site ◽  
Urban Green ◽  
Urban Green Spaces ◽  
Landscape Characteristics ◽  
Site Preferences ◽  
Descriptive Survey ◽  
The Relationship

ABSTRACT Urban area is dominated by land that  functioned  of  the  interest of  economy and  settlement,  but only a few land allocated for wildlife. The butterflies was one of the wildlife that could be found         in urban areas.The remaining habitat that can be used by butterflies assumed confined to the urban green space. Based on its life cycle, the butterflies having an initial phase (egg to larvae) is a phase which is generally require specific habitat. This research aims to determine the relationship between landscape characteristic and oviposition site preferences of butterfly. This research was conducted on April to June, 2014 at fifteen urban green spaces in East Jakarta by using descriptive survey technique. Landscape characteristics measured include area, perimeter, lawn area, closed vegetation area, open vegetation area, non vegetation area. Landscape characteristic not only measured from urban green space, but also measured from the area around urban green space within the scope of 100 meters  buffer. Results show that there is a relationship between landscape characteristic and oviposition site preferences. Significant positive correlation between the abundance and area correlation coefficients   rs (0,546), open vegetation area rs (0,758) and non vegetation buffer area rs (0,688). There was no significant correlation between the abundance with perimeter area,  lawn  area,  closed  vegetation  area,  non vegetation  area,  lawn  buffer,  open  vegetation  buffer  and  closed  vegetation  buffer.   Keywords: caterpillar,landscape characteristic, oviposition site, preferences, urban green space

scholarly journals Remote Sensing Landslide Recognition Based on Convolutional Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yu Wang ◽  
Xiaofei Wang ◽  
Junfan Jian
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Vegetation Index ◽  
Near Infrared ◽  
Spatial Information ◽  
Spectral Characteristics ◽  
Remote Sensing Images ◽  
Landslide Event

Landslides are a type of frequent and widespread natural disaster. It is of great significance to extract location information from the landslide in time. At present, most articles still select single band or RGB bands as the feature for landslide recognition. To improve the efficiency of landslide recognition, this study proposed a remote sensing recognition method based on the convolutional neural network of the mixed spectral characteristics. Firstly, this paper tried to add NDVI (normalized difference vegetation index) and NIRS (near-infrared spectroscopy) to enhance the features. Then, remote sensing images (predisaster and postdisaster images) with same spatial information but different time series information regarding landslide are taken directly from GF-1 satellite as input images. By combining the 4 bands (red + green + blue + near-infrared) of the prelandslide remote sensing images with the 4 bands of the postlandslide images and NDVI images, images with 9 bands were obtained, and the band values reflecting the changing characteristics of the landslide were determined. Finally, a deep learning convolutional neural network (CNN) was introduced to solve the problem. The proposed method was tested and verified with remote sensing data from the 2015 large-scale landslide event in Shanxi, China, and 2016 large-scale landslide event in Fujian, China. The results showed that the accuracy of the method was high. Compared with the traditional methods, the recognition efficiency was improved, proving the effectiveness and feasibility of the method.

scholarly journals Arti Penting Ruang Hijau Kota bagi Masyarakat dan Pemerintah Kota (Ruang Hijau Kota di Lyon, Prancis)

2015 ◽  
Vol 11 (1) ◽  
pp. 14 ◽  
Author(s):  
Bima Fitriandana ◽  
Laurette Wittner ◽  
Joesron Alie Syahbana
Keyword(s):  
Green Space ◽  
Research Question ◽  
Essential Element ◽  
Developed Countries ◽  
Urban Green Space ◽  
City Development ◽  
River Bank ◽  
Urban Green ◽  
Urban Green Spaces ◽  
Big City

The appearance of different urban green space phenomena occuring both in developing and developed countries appeals to be found out in research. Urban green space hasn’t been an essential element in developing countries, such as in Indonesia and its big city. In another part, precisely in Lyon, urban green space is considered as an intergral and important part of city development, particularly in last 24 years (begun in 1990’s). Moreover, their people actively participate in some urban green space projects and go frequently in urban green spaces or urban parks. By indentifying those two phenomena, it’s vividly seen a problem both in societal and municipal level. Based on those facts, this research tried to find out a research question which is about the importance of urban green space for society and minicipality. This research that has been conducted in Tête d’Or park, gerland park, Sergent Blandan park, the river bank of Saône and Rhône as well as Mazagran park, used qualitative methode with some interviews reserved to some key actors, including society. The result shows that society regards urban green space as a source of benefit for social, education and health. Whereas, municipality considers it as an integral element of city providing environmental and economic benefit for city.

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