Building Extraction from RGB Satellite Images using Deep Learning: A U-Net Approach

Τέμενος, Αναστάσιος; Protopapadakis, Eftychios; Doulamis, Anastasios; Temenos, Nikos

doi:10.1145/3453892.3461320

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…3) Non-maximum suppression of the edges calculated in step (2). Usually, the calculated gradient edge is composed of multiple pixels, which seems "blurred", but in practical applications, more precise and clear edges are needed, so the non-maximum suppression algorithm compares the gradient size of the current edge point with the gradient sizes on both sides of its gradient direction, and if the gradient value there is larger than the gradient values on both sides of its gradient direction, the gradient value is retained, otherwise it is set to 0.…”

Section: Edge Extractionmentioning

confidence: 99%

“…As one of the main types of urban features, buildings are the main elements of urban areas, the main places for human learning and living, and the thematic elements to be highlighted in large-scale topographic maps of cities. Remote sensing image building extraction technology is the target identification technology of extracting buildings from aerial or satellite images to obtain their location, outline and other information [1][2]. It is easy to identify buildings manually visually from remote sensing images; however, it is very difficult to achieve automatic computer extraction of buildings from remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

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Multi-featured multi-scale combination of high-resolution remote sensing images for building extraction

Niu

2023

Applied Mathematics and Nonlinear Sciences

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As one of the important feature categories in urban geographic data, buildings are the key thematic elements to be represented in large-scale urban mapping with the high speed of urban digital construction. The identification and extraction of buildings are of great significance for feature extraction, feature matching, image interpretation and mapping. However, the great variability of building size, shape, color, orientation, etc., in remote sensing images poses a great challenge to building detection. To this end, this paper proposes an algorithm based on multi-feature multi-scale fusion for the automatic extraction of buildings in remote sensing images are represented in the form of roofs. It is difficult to represent all buildings with a single feature because of the different colors, textures and shapes of building roofs. Effective features to describe buildings are proposed, including edge density and edge distribution, brightness contrast, color contrast and other features to describe building edge brightness. We propose effective features to describe buildings, including edge density and edge distribution, luminance contrast, color contrast and other underlying features to describe the edges, luminance and color of buildings, and adding special structural features such as main direction orthogonality and target integrity and symmetry to describe buildings by multiple features together. Moreover, the K-value nearest neighbor classification algorithm is used to train a series of samples, and the weights of each feature in the multi-feature model are obtained through iterative learning to obtain the multi-feature linear model and calculate the visual saliency of buildings in the sliding window; finally, the proposed algorithm has experimented with several groups of high-resolution remote sensing images respectively, and the multi-scale multi-feature fusion model algorithm is used as the Erkoff random field model to compare the algorithm. The results of this paper show that the proposed multiscale multi-feature fusion model algorithm improves by 10.82% for building classification accuracy extraction and 13.96% for feature selection extraction accuracy, and finally, the comparison from the shape optimization effect figure concludes that the multiscale multi-feature fusion model can achieve better extraction accuracy and practical effect for buildings in remote sensing images, which has certain practicality and It has certain practicality and superiority. It promotes the in-depth application of multi-feature multi-scale combined high-resolution remote sensing image-building extraction in geographic states, road traffic and other industries.t

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Section: Edge Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-featured multi-scale combination of high-resolution remote sensing images for building extraction

Niu

2023

Applied Mathematics and Nonlinear Sciences

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A Novel Deep Learning Approach for High-Resolution Satellite-Based DEM Filtering

Singla,

Patel,

Patel

2024

J Indian Soc Remote Sens

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Building segmentation from UAV orthomosaics using unet-resnet-34 optimised with grey wolf optimisation algorithm

Nsiah,

Mantey,

Ziggah

2023

Smart Constr. Sustain. Cities

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Given the importance and interest of buildings in the urban environment, numerous studies have focused on automatically extracting building outlines by exploiting different datasets and techniques. Recent advancements in unmanned aerial vehicles (UAVs) and their associated sensors have made it possible to obtain high-resolution data to update building information. These detailed, up-to-date geographic data on the built environment are essential and present a practical approach to comprehending how assets and people are exposed to hazards. This paper presents an effective method for extracting building outlines from UAV-derived orthomosaics using a semantic segmentation approach based on a U-Net architecture with a ResNet-34 backbone (UResNet-34). The novelty of this work lies in integrating a grey wolf optimiser (GWO) to fine-tune the hyperparameters of the UResNet-34 model, significantly enhancing building extraction accuracy across various localities. The experimental results, based on testing data from four different localities, demonstrate the robustness and generalisability of the approach. In this study, Locality-1 is well-laid buildings with roads, Locality-2 is dominated by slum buildings in proximity, Locality-3 has few buildings with background vegetation and Locality-4 is a conglomeration of Locality-1 and Locality-2. The proposed GWO-UResNet-34 model produced superior performance, surpassing the U-Net and UResNet-34. Thus, for Locality-1, the GWO-UResNet-34 achieved 94.74% accuracy, 98.11% precision, 84.85% recall, 91.00% F1-score, and 88.16% MIoU. For Locality-2, 90.88% accuracy, 73.23% precision, 75.65% recall, 74.42% F1-score, and 74.06% MioU was obtained.The GWO-UResNet-34 had 99.37% accuracy, 90.97% precision, 88.42% recall, 89.68% F1-score, and 90.21% MIoU for Locality-3, and 95.30% accuracy, 93.03% precision, 89.75% recall, 91.36% F1-score, and 88.92% MIoU for Locality-4.

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Building Extraction from RGB Satellite Images using Deep Learning: A U-Net Approach

Cited by 4 publications

References 13 publications

Multi-featured multi-scale combination of high-resolution remote sensing images for building extraction

Multi-featured multi-scale combination of high-resolution remote sensing images for building extraction

A Novel Deep Learning Approach for High-Resolution Satellite-Based DEM Filtering

Building segmentation from UAV orthomosaics using unet-resnet-34 optimised with grey wolf optimisation algorithm

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