Building Footprint Extraction from High Resolution Aerial Images Using Generative Adversarial Network (GAN) Architecture

Abdollahi, Abolfazl; Pradhan, Biswajeet; Gite, Shilpa; Alamri, Abdullah

doi:10.1109/access.2020.3038225

Cited by 64 publications

(30 citation statements)

References 50 publications

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“…Therefore, still, there exists a need for performance improvements, particularly for decision-support frameworks that assist in converting the huge amount of data into valuable recommendations. Now, we witnessed the efficacy of deep learning (DL)based approaches such as CNN [14], Recurrent neural networks (RNNs) [15], and deep belief networks [16,17] in several application areas including image segmentation [18], classification [19], change detection [20] and agriculture. DL-based techniques for example CNN are empowered to automatically perceive the optimal key points from the input samples without the need of human experts.…”

Section: Introductionmentioning

confidence: 99%

A novel deep learning method for detection and classification of plant diseases

Albattah

Nawaz

Javed

et al. 2021

Complex Intell. Syst.

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The agricultural production rate plays a pivotal role in the economic development of a country. However, plant diseases are the most significant impediment to the production and quality of food. The identification of plant diseases at an early stage is crucial for global health and wellbeing. The traditional diagnosis process involves visual assessment of an individual plant by a pathologist through on-site visits. However, manual examination for crop diseases is restricted because of less accuracy and the small accessibility of human resources. To tackle such issues, there is a demand to design automated approaches capable of efficiently detecting and categorizing numerous plant diseases. Precise identification and classification of plant diseases is a tedious job due because of the occurrence of low-intensity information in the image background and foreground, the huge color resemblance in the healthy and diseased plant areas, the occurrence of noise in the samples, and changes in the position, chrominance, structure, and size of plant leaves. To tackle the above-mentioned problems, we have introduced a robust plant disease classification system by introducing a Custom CenterNet framework with DenseNet-77 as a base network. The presented method follows three steps. In the first step, annotations are developed to get the region of interest. Secondly, an improved CenterNet is introduced in which DenseNet-77 is proposed for deep keypoints extraction. Finally, the one-stage detector CenterNet is used to detect and categorize several plant diseases. To conduct the performance analysis, we have used the PlantVillage Kaggle database, which is the standard dataset for plant diseases and challenges in terms of intensity variations, color changes, and differences found in the shapes and sizes of leaves. Both the qualitative and quantitative analysis confirms that the presented method is more proficient and reliable to identify and classify plant diseases than other latest approaches.

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Section: Introductionmentioning

confidence: 99%

A novel deep learning method for detection and classification of plant diseases

Albattah

Nawaz

Javed

et al. 2021

Complex Intell. Syst.

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“…In satellite-based assessments, CNNs have been used to classify hurricane-damaged roofs [18] and to estimate tree failure due to high winds [19,20]. Recently, unsupervised and semisupervised learning approaches leveraged a large number of unlabeled data to extract structural features in satellite images [21,22]. With UAS imagery, Cheng et al [23] used Mask R-CNN [13], a more advanced neural network based on CNN, to detect and segment tornado-damaged buildings.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Approaches for Tornado Damage Estimation with Unpiloted Aerial Systems

et al. 2021

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Tornado damage estimation is important for providing insights into tornado studies and assisting rapid disaster response. However, it is challenging to precisely estimate tornado damage because of the large volumes of perishable data. This study presents data-driven approaches to tornado damage estimation using imagery collected from Unpiloted Aerial Systems (UASs) following the 26 June 2018 Eureka Kansas tornado. High-resolution orthomosaics were generated from Structure from Motion (SfM). We applied deep neural networks (DNNs) on the orthomosaics to estimate tornado damage and assessed their performance in four scenarios: (1) object detection with binary categories, (2) object detection with multiple categories, (3) image classification with binary categories, and (4) image classification with multiple categories. Additionally, two types of tornado damage heatmaps were generated. By directly stitching the resulting image tiles from the DNN inference, we produced the first type of tornado damage heatmaps where damage estimates are accurately georeferenced. We also presented a Gaussian process (GP) regression model to build the second type of tornado damage heatmap (a spatially continuous tornado damage heatmap) by merging the first type of object detection and image classification heatmaps. The GP regression results were assessed with ground-truth annotations and National Weather Service (NWS) ground surveys. This detailed information can help NWS Weather Forecast Offices and emergency managers with their damage assessments and better inform disaster response and recovery.

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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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Building Footprint Extraction from High Resolution Aerial Images Using Generative Adversarial Network (GAN) Architecture

Cited by 64 publications

References 50 publications

A novel deep learning method for detection and classification of plant diseases

A novel deep learning method for detection and classification of plant diseases

Data-Driven Approaches for Tornado Damage Estimation with Unpiloted Aerial Systems

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

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