Recent studies have increasingly applied deep learning techniques to analyze green spaces in urban areas, predominantly utilizing Google Street View (GSV) imagery to determine the amount of green space in each image. Researchers such as Li et al. [19], Lu et al. [20], Seiferling et al. [21], Wang et al. [22], and Yin and Wang [3] have demonstrated the potential of GSV for evaluating the tree cover of streets in Manhattan by computing Green View Indexing (GVI) in each image and extracting the pixels occupied by plants. However, most studies using GSV have only measured GVI without considering trees, shrubs, and lawns.

Some recent studies have attempted to overcome these limitations by classifying different tree species. Zarrin developed a new strategy for detecting various tree species based on their leaves [23]. Furthermore, Sun et al. proposed a method for classifying the type of vegetation (i.e., tree, low-lying vegetation, grass) in street view images [24]. More recently, Choi et al. utilized a semantic segmentation algorithm and graphical analysis to estimate tree profile parameters by determining the relative location of the interface of trees and the ground surface [12]. However, these studies still face limitations as they fail to properly consider the morphological and phenological characteristics of each tree species.

To compute the amount of green vegetation in a given area, researchers have employed two primary methods: (1) color band methods, which extract information based on pixel color, and (2) semantic segmentation techniques, which distinguish between natural greenery and non-vegetated surfaces, such as buildings and roads. The resulting GVI is typically expressed as a percentage or numerical score that reflects the proportion of green vegetation in a specific area.

Long et al. [25] proposed a method for semantic segmentation using fully convolutional networks, which replaced the fully connected layer used in general CNN models for image classification with a convolutional layer for pixel-level classification. They also introduced a skip layer to improve accuracy during the up-sampling process. Similarly, Badrinarayanan et al. [16,17] developed SegNet, which combines the advantages of DeconvNet [26] and U-Net [27]. SegNet uses pooling indices instead of copying and cropping the entire feature to improve memory efficiency, and removes the fully connected layer used in DeconvNet to reduce parameterization.

Another notable method is DeepLab [28], proposed by Chen et al., which uses atrous convolution-based semantic segmentation architecture and atrous spatial pyramid pooling (ASPP) [18,29,30] to improve the architecture. The authors proposed Panoptic DeepLab [31], which transforms the ASPP and decoder of DeepLabv3+ [18] into a dual form, with each decoder producing semantic and instance information as outputs for panoptic segmentation.

Cheng et al. proposed Mask2Former [32] for universal image segmentation, which modifies the architecture of vision transformer [33] by applying transformer [34] and BERT [35] to computer vision. Mask2Former extracts localized features by constraining cross-attention within predicted mask regions. Kabilan et al. [36] improved segmentation accuracy and reduced complexity by using a three-step segmentation process involving the analysis of key components, mapping of similar objects in a faster way, and segmenting similar areas through color mapping.

Semi-supervised learning is often used to solve the problem of insufficient labeled data, but it has a lazy mimicking problem. To address this issue, Huo et al. [37] proposed ATSO, a model that partitions unlabeled training data into two subsets and alternately uses one subset to fine-tune the model, updating labels on the other subset.

There have been numerous studies on applying hybrid and fusion methods for the segmentation of urban and satellite images. Li et al. proposed a hybrid convolutional network (HCN) comprising U-Net and VGG sub-networks, which was applied for road segmentation [38]. Wang et al. developed a remote sensing image segmentation method using a hybrid method (division and merge) [39]. Khoshboresh et al. proposed a novel hybrid method that combines deep convolutional neural networks and a restricted Boltzmann machine (RBM) to take advantage of the semantic segmentation of high-resolution airborne imagery for automatic building detection [40]. Sun et al. proposed a novel RGB and thermal data fusion network called FuseSeg, which achieved superior performance in the semantic segmentation of urban scenes [41]. Khan et al. developed a hybrid deep learning model that combines the benefits of two deep models, i.e., DenseNet and U-Net [42]. Niu et al. proposed a novel attention-based framework named hybrid multiple attention network (HMANet) that adaptively captures global correlations from the perspective of space, channel, and category in a more effective and efficient manner [43]. Abdollahi et al. introduced two new deep convolutional models, the multilevel context gate UNet (MCg-UNet) and the bidirectional ConvLSTM UNet model (BCL-UNet), based on the UNet family for multi-object segmentation such as roads and buildings in aerial images [44]. Chen et al. presented a pipeline of hybrid supervision that designs auxiliary segmentation models using boundary box attention modules and boundary box filter modules [45].

Various deep learning models have been proposed to address the problem of semantic image segmentation, leveraging multiple information sources to achieve improved performance. For instance, Zhang et al. presented a hybrid deep neural network that combines a transformer and CNN for the semantic segmentation of very high-resolution remote sensing imagery [46]. Another study by Luo et al. introduced a hybrid convolutional neural network (H-ConvNet) to improve urban land cover mapping with MSR Sentinel-2 images [47]. Li et al. proposed a novel hybrid contrastive regularization (HybridCR) framework in a weakly supervised setting, which obtained competitive performance compared to its fully supervised counterpart [48]. Hossain et al. proposed a hybrid segmentation method with modifications such as using the reference polygon to identify optimal parameters and a donut-filling technique to reduce over-segmentation caused by roof elements and illumination differences [49].

Other models have leveraged multimodal fusion to achieve optimal joint predictions. For example, Valdez-Rodríguez et al. proposed a hybrid 2D-3D CNN architecture capable of obtaining semantic segmentation and depth estimation simultaneously [50]. Wang et al. presented a Bilateral Awareness Network that fully captures long-range relationships and fine-grained details in Very Fine Resolution (VFR) images using a dependency path and a texture path [51]. Men et al. proposed a novel model called Concatenated Residual Attention UNet (CRAUNet), which combines the residual structure and channel attention mechanism [52]. Another study by Wang et al. introduced a Transformer-based decoder and constructed a UNet-like Transformer (UNetFormer) for real-time urban scene segmentation [53]. Finally, to take advantage of both CNN and Transformer, a novel Adaptive Enhanced Swin Transformer with U-Net (AESwin-UNet) was proposed for remote sensing segmentation [53,54].