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Accurate and timely information on urban vegetation (UV) can be used as an important indicator to estimate the health of cities. Due to the low cost of RGB cameras, true color imagery (TCI) has been widely used for high spatial resolution UV mapping. However, the current index-based and classifier-based UV mapping approaches face problems of the poor ability to accurately distinguish UV and the high reliance on massive annotated samples, respectively. To address this issue, an index-guided semantic segmentation (IGSS) framework is proposed in this paper. Firstly, a novel cross-scale vegetation index (CSVI) is calculated by the combination of TCI and Sentinel-2 images, and the index value can be used to provide an initial UV map. Secondly, reliable UV and non-UV samples are automatically generated for training the semantic segmentation model, and then the refined UV map can be produced. The experimental results show that the proposed CSVI outperformed the existing five RGB vegetation indices in highlighting UV cover and suppressing complex backgrounds, and the proposed IGSS workflow achieved satisfactory results with an OA of 87.72% ∼ 88.16% and an F1 score of 87.73% ∼ 88.37%, which is comparable with the fully-supervised method.

Accurate and timely information on urban vegetation (UV) can be used as an important indicator to estimate the health of cities. Due to the low cost of RGB cameras, true color imagery (TCI) has been widely used for high spatial resolution UV mapping. However, the current index-based and classifier-based UV mapping approaches face problems of the poor ability to accurately distinguish UV and the high reliance on massive annotated samples, respectively. To address this issue, an index-guided semantic segmentation (IGSS) framework is proposed in this paper. Firstly, a novel cross-scale vegetation index (CSVI) is calculated by the combination of TCI and Sentinel-2 images, and the index value can be used to provide an initial UV map. Secondly, reliable UV and non-UV samples are automatically generated for training the semantic segmentation model, and then the refined UV map can be produced. The experimental results show that the proposed CSVI outperformed the existing five RGB vegetation indices in highlighting UV cover and suppressing complex backgrounds, and the proposed IGSS workflow achieved satisfactory results with an OA of 87.72% ∼ 88.16% and an F1 score of 87.73% ∼ 88.37%, which is comparable with the fully-supervised method. [ABSTRACT FROM AUTHOR]

Under increased urban settlement density, the central areas of cities are usually the main targets of the regeneration process. This action involves modern urban modelling methods to emphasize the buildings structures and vegetation assessment. One of the methods is represented by LiDAR (Light Detection And Ranging) technology. LiDAR offers highresolution imagery for very detailed information on object attributes, such as spectral signature, texture and shape, very accurate position and height information. Recently, remote sensing techniques are being increasingly used for inventory, monitoring and management of vegetation. Precise and up to date spatial information on the current status of green areas is a requirement for the sustainable conservation of urban vegetation. Correct information of urban vegetation is important to preserve the ecological environment within urban spaces. The aim of this study is to expose the utility of the LiDAR technique for assessment of urban vegetation in relation with built-up space in Bucharest Municipality. The results show that spectral details of data reflect the relationships between green areas and their surrounding environment. Urban evolution of Bucharest municipality was very rapid, especially starting with the second half of the 19th century, which led in time to a reduction of the green spaces. Although the capital city has a rich patchwork of green spaces (parks, public gardens, urban lawns, squares) and varied in terms of functionality (for recreation, for decorative purposes, with ecological function), we can observe they are diminishing in the recent years, due to the tendency for expansion of the residential areas.

Vegetation monitoring is becoming a major issue in the urban environment due to the services they procure and necessitates an accurate and up to date mapping. Very High Resolution satellite images enable a detailed mapping of the urban tree and herbaceous vegetation. Several supervised classifications with statistical learning techniques have provided good results for the detection of urban vegetation but necessitate a large amount of training data. In this context, this study proposes to investigate the performances of different sampling strategies in order to reduce the number of examples needed. Two windows based active learning algorithms from state-of-art are compared to a classical stratified random sampling and a third combining active learning and stratified strategies is proposed. The efficiency of these strategies is evaluated on two medium size French cities, Strasbourg and Rennes, associated to different datasets. Results demonstrate that classical stratified random sampling can in some cases be just as effective as active learning methods and that it should be used more frequently to evaluate new active learning methods. Moreover, the active learning strategies proposed in this work enables to reduce the computational runtime by selecting multiple windows at each iteration without increasing the number of windows needed. [ABSTRACT FROM AUTHOR]

HJ-1A/B NDVI (HJ NDVI) time-series data possess relatively high spatio-temporal resolution which is significant for the research on urban areas. However, its application is hindered by noise resulting from the restrictions of imaging quality and limits of the satellite platform. The NDVI noise reduction is necessary. Some noise-reduction techniques including the asymmetric Gaussian filter (AG), the double logistic filter (DL), the Savitzky-Golay (S-G) filter and the harmonic analysis (Hants) of NDVI time-series have been used to carry out the NDVI time series reconstruction, and based on the comparison results of different filter, S-G filter is the optimal in the application on urban areas. Finally,urban vegetation mapping is carried out based on the new HJ NDVI. [ABSTRACT FROM AUTHOR]