Deep Learning-Based Semantic Segmentation of Urban Features in Satellite Images: A Review and Meta-Analysis

Neupane, Bipul; Horanont, Teerayut; Aryal, Jagannath

doi:10.3390/rs13040808

Cited by 123 publications

(87 citation statements)

References 148 publications

(365 reference statements)

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“…[2] reviewed the DL models for road extraction during the time period from 2010 to 2019; ref. [3] discussed data sources, data preparation, training details and performance comparison for DL semantic segmentation models for satellite images in urban environments; refs. [4,5] reviewed DL applications in hyperspectral and multispectral images; [6,7] reviewed DL approaches to process 3D point cloud or RS data; ref.…”

Section: Introductionmentioning

confidence: 99%

A Review of Landcover Classification with Very-High Resolution Remotely Sensed Optical Images—Analysis Unit, Model Scalability and Transferability

Qin

Liu

2022

Remote Sensing

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As an important application in remote sensing, landcover classification remains one of the most challenging tasks in very-high-resolution (VHR) image analysis. As the rapidly increasing number of Deep Learning (DL) based landcover methods and training strategies are claimed to be the state-of-the-art, the already fragmented technical landscape of landcover mapping methods has been further complicated. Although there exists a plethora of literature review work attempting to guide researchers in making an informed choice of landcover mapping methods, the articles either focus on the review of applications in a specific area or revolve around general deep learning models, which lack a systematic view of the ever advancing landcover mapping methods. In addition, issues related to training samples and model transferability have become more critical than ever in an era dominated by data-driven approaches, but these issues were addressed to a lesser extent in previous review articles regarding remote sensing classification. Therefore, in this paper, we present a systematic overview of existing methods by starting from learning methods and varying basic analysis units for landcover mapping tasks, to challenges and solutions on three aspects of scalability and transferability with a remote sensing classification focus including (1) sparsity and imbalance of data; (2) domain gaps across different geographical regions; and (3) multi-source and multi-view fusion. We discuss in detail each of these categorical methods and draw concluding remarks in these developments and recommend potential directions for the continued endeavor.

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Section: Introductionmentioning

confidence: 99%

A Review of Landcover Classification with Very-High Resolution Remotely Sensed Optical Images—Analysis Unit, Model Scalability and Transferability

Qin

Liu

2022

Remote Sensing

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“…Consequently, designing a technique that can obtain high precision on feature segmentation results, especially from high spatial resolution remote sensing data, is quite challenging. Over the last years, convolutional neural network (CNN) frameworks [5][6][7] have been applied for semantic segmentation not only in computer vision applications, such as coined CNN with conditional random fields (CRFs) [8], patch network [9], deconvolutional networks [10], deep parsing network [11], SegNet [12], decoupled network [13], and fully connected network [14], but also in the remote sensing field [15][16][17]. Seeing that the CNN framework has the capability to utilize input data and efficiently encode spatial and spectral features without any pre-processing stage, it is becoming extremely popular in the remote sensing field as well [18].…”

Section: Introductionmentioning

confidence: 99%

Multi-Object Segmentation in Complex Urban Scenes from High-Resolution Remote Sensing Data

et al. 2021

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Terrestrial features extraction, such as roads and buildings from aerial images using an automatic system, has many usages in an extensive range of fields, including disaster management, change detection, land cover assessment, and urban planning. This task is commonly tough because of complex scenes, such as urban scenes, where buildings and road objects are surrounded by shadows, vehicles, trees, etc., which appear in heterogeneous forms with lower inter-class and higher intra-class contrasts. Moreover, such extraction is time-consuming and expensive to perform by human specialists manually. Deep convolutional models have displayed considerable performance for feature segmentation from remote sensing data in the recent years. However, for the large and continuous area of obstructions, most of these techniques still cannot detect road and building well. Hence, this work’s principal goal is to introduce two novel deep convolutional models based on UNet family for multi-object segmentation, such as roads and buildings from aerial imagery. We focused on buildings and road networks because these objects constitute a huge part of the urban areas. The presented models are called multi-level context gating UNet (MCG-UNet) and bi-directional ConvLSTM UNet model (BCL-UNet). The proposed methods have the same advantages as the UNet model, the mechanism of densely connected convolutions, bi-directional ConvLSTM, and squeeze and excitation module to produce the segmentation maps with a high resolution and maintain the boundary information even under complicated backgrounds. Additionally, we implemented a basic efficient loss function called boundary-aware loss (BAL) that allowed a network to concentrate on hard semantic segmentation regions, such as overlapping areas, small objects, sophisticated objects, and boundaries of objects, and produce high-quality segmentation maps. The presented networks were tested on the Massachusetts building and road datasets. The MCG-UNet improved the average F1 accuracy by 1.85%, and 1.19% and 6.67% and 5.11% compared with UNet and BCL-UNet for road and building extraction, respectively. Additionally, the presented MCG-UNet and BCL-UNet networks were compared with other state-of-the-art deep learning-based networks, and the results proved the superiority of the networks in multi-object segmentation tasks.

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“…In addition, the development of artificial intelligence (AI) provides effective solutions and opportunities for SID [18,19]. More importantly, AI is helpful for the implementations of relevant technologies in SID, such as smart cities, internet of things, big data, cloud computing, BIM-GIS (building information modeling and geographical information science) integration, machine learning and deep learning [20][21][22][23][24][25][26]. Studies have also demonstrated that AI may inhibit the achievement of 35% of SDG targets [18].…”

Section: Introductionmentioning

confidence: 99%

Earth Observation for Sustainable Infrastructure: A Review

Song

2021

Remote Sensing

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Infrastructure is a fundamental sector for sustainable development and Earth observation has great potentials for sustainable infrastructure development (SID). However, implementations of the timely, large–scale and multi–source Earth observation are still limited in satisfying the huge global requirements of SID. This study presents a systematical literature review to identify trends of Earth observation for sustainable infrastructure (EOSI), investigate the relationship between EOSI and Sustainable Development Goals (SDGs), and explore challenges and future directions of EOSI. Results reveal the close associations of infrastructure, urban development, ecosystems, climate, Earth observation and GIS in EOSI, and indicate their relationships. In addition, from the perspective of EOSI–SDGs relationship, the huge potentials of EOSI are demonstrated from the 70% of the infrastructure influenced targets that can be directly or indirectly derived from Earth observation data, but have not been included in current SDG indicators. Finally, typical EOSI cases are presented to indicate challenges and future research directions. This review emphasizes the contributions and potentials of Earth observation to SID and EOSI is a powerful pathway to deliver on SDGs.

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Deep Learning-Based Semantic Segmentation of Urban Features in Satellite Images: A Review and Meta-Analysis

Cited by 123 publications

References 148 publications

A Review of Landcover Classification with Very-High Resolution Remotely Sensed Optical Images—Analysis Unit, Model Scalability and Transferability

A Review of Landcover Classification with Very-High Resolution Remotely Sensed Optical Images—Analysis Unit, Model Scalability and Transferability

Multi-Object Segmentation in Complex Urban Scenes from High-Resolution Remote Sensing Data

Earth Observation for Sustainable Infrastructure: A Review

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