Machine Learning Methods for Classification of the Green Infrastructure in City Areas

Kranjčić, Nikola; Medak, Damir; Župan, Robert; Rezo, Milan

doi:10.3390/ijgi8100463

Cited by 27 publications

(13 citation statements)

References 26 publications

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“…The main methods are supervised classification and unsupervised classification. The supervised classification includes decision tree, support vector machine method, and maximum likelihood method, while unsupervised classification includes ISODATA [15,16]. These methods are mainly applied to low and medium resolution remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

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

Zhou

Wang

et al. 2020

Remote Sensing

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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.

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Section: Introductionmentioning

confidence: 99%

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

Zhou

Wang

et al. 2020

Remote Sensing

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“…To generate an independent tree, samples and features are randomly selected, which can prevent overfitting. The classification problem is determined by the category with the most output times of individual trees [51].…”

Section: Prediction Modelmentioning

confidence: 99%

Identifying Urban Functional Areas in China’s Changchun City from Sentinel-2 Images and Social Sensing Data

et al. 2021

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The urban functional area is critical to an understanding of the complex urban system, resource allocation, and management. However, due to urban surveys’ focus on geographic objects and the mixture of urban space, it is difficult to obtain such information. The function of a place is determined by the activities that take place there. This study employed mobile phone signaling data to extract temporal features of human activities through discrete Fourier transform (DFT). Combined with the features extracted from the point of interest (POI) data and Sentinel images, the urban functional areas of Changchun City were identified using a random forest (RF) model. The results indicate that integrating features derived from remote sensing and social sensing data can effectively improve the identification accuracy and that features derived from dynamic mobile phone signaling have a higher identification accuracy than those derived from POI data. The human activity characteristics on weekends are more distinguishable for different functional areas than those on weekdays. The identified urban functional layout of Changchun is consistent with the actual situation. The residential functional area has the highest proportion, accounting for 33.51%, and is mainly distributed in the central area, while the industrial functional area and green-space are distributed around.

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“…Earth observation satellite, one of the most significant platforms, is widely applied for LCC due to their customized sensors which are able to provide extensive geographical coverage while with an affordable cost for spatial and temporal land use/cover mapping [3]. In particular, LCC using remote sensing images of high spatial/spectral resolutions is playing a paramount role in urban planning, land resource management, green infrastructure monitoring, disaster management, and agricultural applications [4][5][6][7]. In China, the largest developing country, rapid urbanization has been changing its geographic characteristics, particularly for urban areas where the balance of environment and urban infrastructures is gradually being impaired.…”

Section: Introductionmentioning

confidence: 99%

“…In China, the largest developing country, rapid urbanization has been changing its geographic characteristics, particularly for urban areas where the balance of environment and urban infrastructures is gradually being impaired. Therefore, land cover classification for urban areas is of great importance to assess its changes for its sustainable development [6].…”

Section: Introductionmentioning

confidence: 99%

Sentinel-2 Satellite Imagery for Urban Land Cover Classification by Optimized Random Forest Classifier

Zhang

et al. 2021

Applied Sciences

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Land cover classification is able to reflect the potential natural and social process in urban development, providing vital information to stakeholders. Recent solutions on land cover classification are generally addressed by remotely sensed imagery and supervised classification methods. However, a high-performance classifier is desirable but challenging due to the existence of model hyperparameters. Conventional approaches generally rely on manual tuning, which is time-consuming and far from satisfying. Therefore, this work aims to propose a systematic method to automatically tune the hyperparameters by Bayesian parameter optimization for the random forest classifier. The recently launched Sentinel-2A/B satellites are drawn to provide the remote sensing imageries for land cover classification case study in Beijing, China, which have the best spectral/spatial resolutions among the freely available satellites. The improved random forest with Bayesian parameter optimization is compared against the support vector machine (SVM) and random forest (RF) with default hyperparameters by discriminating five land cover classes including building, tree, road, water, and crop field. Comparative experimental results show that the optimized RF classifier outperforms the conventional SVM and the RF with default hyperparameters in terms of accuracy, precision, and recall. The effects of band/feature number and the band usefulness are also assessed. It is envisaged that the improved classifier for Sentinel-2 satellite image processing can find a wide range of applications where high-resolution satellite imagery classification is applicable.

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Machine Learning Methods for Classification of the Green Infrastructure in City Areas

Cited by 27 publications

References 26 publications

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

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

Identifying Urban Functional Areas in China’s Changchun City from Sentinel-2 Images and Social Sensing Data

Sentinel-2 Satellite Imagery for Urban Land Cover Classification by Optimized Random Forest Classifier

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