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The accurate extraction of buildings from remote sensing images is crucial in fields such as 3D urban planning, disaster detection, and military reconnaissance. In recent years, models based on Transformer have performed well in global information processing and contextual relationship modeling, but suffer from high computational costs and insufficient ability to capture local information. In contrast, convolutional neural networks (CNNs) are very effective in extracting local features, but have a limited ability to process global information. In this paper, an asymmetric network (CTANet), which combines the advantages of CNN and Transformer, is proposed to achieve efficient extraction of buildings. Specifically, CTANet employs ConvNeXt as an encoder to extract features and combines it with an efficient bilateral hybrid attention transformer (BHAFormer) which is designed as a decoder. The BHAFormer establishes global dependencies from both texture edge features and background information perspectives to extract buildings more accurately while maintaining a low computational cost. Additionally, the multiscale mixed attention mechanism module (MSM-AMM) is introduced to learn the multiscale semantic information and channel representations of the encoder features to reduce noise interference and compensate for the loss of information in the downsampling process. Experimental results show that the proposed model achieves the best F1-score (86.7%, 95.74%, and 90.52%) and IoU (76.52%, 91.84%, and 82.68%) compared to other state-of-the-art methods on the Massachusetts building dataset, the WHU building dataset, and the Inria aerial image labeling dataset. [ABSTRACT FROM AUTHOR]

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In the dynamic landscape of urban development, the demand for precise urban extraction methods is paramount. A multispectral light detection and ranging (LiDAR) system simultaneously captures spatial geometry and multi-wavelength intensity data, allowing for three-dimensional point cloud segmentation and feature extraction, accurate urban extraction using a Multispectral LiDAR (MS-LiDAR) point cloud is vital. Feature extraction from MS-LiDAR point cloud has evolved by dint of deep learning scientific progress. Automated building extraction from MS-LiDAR point clouds without converting point cloud data to a raster is the main idea in this paper. This investigation used Teledyne Optech Titan multi-spectral LAS point clouds. A statistical outlier removal (SOR) filter removed noise. LAS Teledyne Optech Titan multispectral ALS point clouds are used in the study. A representative example of a complex urban environment from the National Center for Airborne Laser Mapping (NCALM) of a residential area near the University of Houston. A statistical outlier removal (SOR) filter removed noise. Then point clouds from 532, 1024, and 1550 nm were merged. Via the cloth simulation filter (CSF) filtering algorithm, ground and above-ground points were separated. The Point CNN algorithm was used in this study to extract buildings from multispectral LiDAR point clouds directly. This was studied in nine different scenarios. A new index-based methodology, normalized difference feature Index (NDFI), is proposed to enhance the building extraction accuracy from multispectral LiDAR point cloud by using green and mid-infrared (MIR) bands. The deep learning building extraction was applied based on the proposed new urban index and point convolutional neural network (Point-CNN) algorithm. In addition to its conceptual ease and low processing cost, the suggested index is efficient at extracting buildings. In Deep Network, introducing a new urban index NDFI yielded better accurate findings (OA of 95.27%) than NDVI's 95.11%. Finally, this strategy was tested with real-world data, proving its viability. Overall results show that the point-CNN algorithm combined with the new urban index is both accurate and well-suited for building extraction. [ABSTRACT FROM AUTHOR]

The satellite images have been employed in building extraction to aid urban planning, tax assessment, disaster management, etc. The number of buildings and building types is huge in urban areas, which puts more burden on human experts to extract buildings in satellite images. Hence, building extraction from satellite images using deep learning (DL) has become an emerging research domain in recent decades. The performance of the DL model depends on training parameters, the depth of the model, and the memory required to preserve the model. In this work, a Memory-Efficient Residual Dilated Convolutional Network (MRDCN) has been proposed to extract buildings effectively with reduced number of training parameters and with lesser memory consumption. The model is trained using the Massachusetts buildings dataset and implemented using PyTorch in Kaggle platform. The trained model has been tested using both Massachusetts and AIRS Dataset. The simulation results prove that the proposed model uses 31.64% less memory than the existing dilated residual network. It is evident from the results that the MRDCN is able to extract the buildings with better accuracy and an Intersection of Union with minimal memory consumption than the existing standard UNet, SegNet, ResUNet, and Dilated ResUNet models. [ABSTRACT FROM AUTHOR]

In recent years, limited surface space and restricted urban expansion have become increasingly prominent. The development of underground spaces has emerged as an effective solution, offering a new dimension for urban growth and sustainability. However, due to the non-renewable and costly nature of underground space (UGS), it should be properly planned before development to avoid waste of resources and economic loss. In this study, the potential of UGS development based on the influence of surface buildings was evaluated by using the high-resolution satellite images of Gaofen-2 (GF-2) in Qingdao, Shandong Province, China. Firstly, the DeepLabv3+ network was lightened to enhance the training efficiency and building extraction accuracy. The building shadows were then extracted by an improved shadow measurement model, and used to estimate the height of extracted buildings based on the imaging mechanism and information of the remote sensing image. Finally, the building height (BH) was converted into the depth of influence on the UGS, and the development potential of the UGS was then estimated. The experimental results show that the UGS within 0–10 m in Qingdao city has been extensively occupied. However, the geological layers between 10–30 m and 30–50 m contain large continuous areas that have not been utilized, indicating the greatest potential for resource development within the city. [ABSTRACT FROM AUTHOR]

Building extraction aims to extract building pixels from remote sensing imagery, which plays a significant role in urban planning, dynamic urban monitoring, and many other applications. UNet3+ is widely applied in building extraction from remote sensing images. However, it still faces issues such as low segmentation accuracy, imprecise boundary delineation, and the complexity of network models. Therefore, based on the UNet3+ model, this paper proposes a 3D Joint Attention (3DJA) module that effectively enhances the correlation between local and global features, obtaining more accurate object semantic information and enhancing feature representation. The 3DJA module models semantic interdependence in the vertical and horizontal dimensions to obtain feature map spatial encoding information, as well as in the channel dimensions to increase the correlation between dependent channel graphs. In addition, a bottleneck module is constructed to reduce the number of network parameters and improve model training efficiency. Many experiments are conducted on publicly accessible WHU,INRIA and Massachusetts building dataset, and the benchmarks, BOMSC-Net, CVNet, SCA-Net, SPCL-Net, ACMFNet, MFCF-Net models are selected for comparison with the 3DJA-UNet3+ model proposed in this paper. The experimental results show that 3DJA-UNet3+ achieves competitive results in three evaluation indicators: overall accuracy, mean intersection over union, and F1-score. The code will be available at https://github.com/EnjiLi/3DJA-UNet3Plus. [ABSTRACT FROM AUTHOR]

Data and reports indicate an increasing frequency and intensity of natural disasters worldwide. Buildings play a crucial role in disaster responses and damage assessments, aiding in planning rescue efforts and evaluating losses. Despite advances in applying deep learning to building extraction, challenges remain in handling complex natural disaster scenes and reducing reliance on labeled datasets. Recent advances in satellite video are opening a new avenue for efficient and accurate building extraction research. By thoroughly mining the characteristics of disaster video data, this work provides a new semantic segmentation model for accurate and efficient building extraction based on a limited number of training data, which consists of two parts: the prediction module and the automatic correction module. The prediction module, based on a base encoder–decoder structure, initially extracts buildings using a limited amount of training data that are obtained instantly. Then, the automatic correction module takes the output of the prediction module as input, constructs a criterion for identifying pixels with erroneous semantic information, and uses optical flow values to extract the accurate corresponding semantic information on the corrected frame. The experimental results demonstrate that the proposed method outperforms other methods in accuracy and computational complexity in complicated natural disaster scenes. [ABSTRACT FROM AUTHOR]

In high-resolution remote sensing images, there are areas with weak textures such as large building roofs, which occupy a large number of pixels in the image. These areas pose a challenge for traditional semantic segmentation networks to obtain ideal results. Common strategies like downsampling, patch cropping, and cascade models often sacrifice fine details or global context, resulting in limited accuracy. To address these issues, a novel semantic segmentation framework has been designed specifically for large-format high-resolution remote sensing images by aggregating global and local features in this paper. The framework consists of two branches: one branch deals with low-resolution downsampled images to capture global features, while the other branch focuses on cropped patches to extract high-resolution local details. Also, this paper introduces a feature aggregation module based on the Transformer structure, which effectively aggregates global and local information. Additionally, to save GPU memory usage, a novel three-step training method has been developed. Extensive experiments on two public datasets demonstrate the effectiveness of the proposed approach, with an IoU of 90.83% on the AIDS dataset and 90.30% on the WBDS dataset, surpassing state-of-the-art methods such as DANet, DeepLab v3+, U-Net, ViT, TransUNet, CMTFNet, and UANet. [ABSTRACT FROM AUTHOR]

Extracting buildings from remote sensing imagery (RSI) is an essential task in a wide range of applications, such as urban and monitoring. Deep learning has emerged as a powerful tool for this purpose, and in this research, we propose an advanced building extraction method based on SE-ResNet18 and SE-ResNet34 architectures. These models were selected through a rigorous comparative analysis of various deep learning models, including variations of residual networks (ResNet), squeeze-and-excitation residual networks (SE-ResNet), and visual geometry group (VGG), for their high performance in all metrics and their computational efficiency. Our proposed methodology outperformed all other models under consideration by a significant margin, demonstrating its robustness and efficiency. It achieved superior results with less computational effort and time, a testament to its potential as a powerful tool for semantic segmentation tasks in remote sensing applications. An extensive comparative evaluation involving a wide range of state-of-the-art works further validated our method's effectiveness. Our method achieved an unparalleled intersection over union (IoU) score of 88.51%, indicative of its exceptional accuracy in identifying and segmenting buildings within the Wuhan University (WHU) building dataset. The overall performance of our method, which offers an excellent balance between high performance and computational efficiency, makes it a compelling choice for researchers and practitioners in the field. [ABSTRACT FROM AUTHOR]

Due to the high similarity between background and buildings in high spatial resolution remote sensing images, which makes it difficult for the network to take into account buildings of different sizes, the pixels in the building boundary region are confused with the background, and the building boundaries are easily missed. In order to solve the above problems, the building extraction algorithm (UUNet) for remote sensing images fusing partial convolution and residual refinement is proposed. Using U-Net as the baseline network, firstly, this paper improves the encoder. It adds two Conv4×4 at the front end of the encoder to expand the sensing field at the beginning and capture more remote sensing image feature information. It utilizes the PC module constructed by partial convolution (PConv3×3) to enhance the ability of the encoder to extract multi-scale building features, and downsamples twice with Conv2×2 to reduce the loss of building feature information. Secondly, this paper reduces the number of parameters. It crops the three-layer structure of the U-Net network decoder to a UUNet network decoder. Lastly, it adds an improved residual refinement module. It constructs a U-shaped residual refinement module cropped to a three-layer structure at the output of the decoder, to further purify the rough building feature maps output from the decoder, so as to make the edge information of the buildings clearer. Decoder is jump-connected to the encoder of the U-shaped residual refinement module to preserve the initial features, and SimAM is embedded in the refinement module to improve the building focus, optimize the network to improve the boundary blurring, and enhance the quality of target boundary extraction. In the ablation experiment conducted on the Satellite dataset II (East Asia), UUNet shows improvements over U-Net, with IoUBuilding, IoUBackground, F1, OA and mIoU increased by 2.78 percentage points, 0.12 percentage points, 1.91 percentage points, 0.19 percentage points and 1.45 percentage points, respectively, indicating that UUNet outperforms the baseline network. Furthermore, comparative experiments on both Satellite dataset II (East Asia) and WHU dataset demonstrate that UUNet performs better than existing mainstream algorithms, significantly enhancing building extraction in high-resolution remote sensing images.

Abstract Building extraction aims to extract building pixels from remote sensing imagery, which plays a significant role in urban planning, dynamic urban monitoring, and many other applications. UNet3+ is widely applied in building extraction from remote sensing images. However, it still faces issues such as low segmentation accuracy, imprecise boundary delineation, and the complexity of network models. Therefore, based on the UNet3+ model, this paper proposes a 3D Joint Attention (3DJA) module that effectively enhances the correlation between local and global features, obtaining more accurate object semantic information and enhancing feature representation. The 3DJA module models semantic interdependence in the vertical and horizontal dimensions to obtain feature map spatial encoding information, as well as in the channel dimensions to increase the correlation between dependent channel graphs. In addition, a bottleneck module is constructed to reduce the number of network parameters and improve model training efficiency. Many experiments are conducted on publicly accessible WHU,INRIA and Massachusetts building dataset, and the benchmarks, BOMSC-Net, CVNet, SCA-Net, SPCL-Net, ACMFNet, MFCF-Net models are selected for comparison with the 3DJA-UNet3+ model proposed in this paper. The experimental results show that 3DJA-UNet3+ achieves competitive results in three evaluation indicators: overall accuracy, mean intersection over union, and F1-score. The code will be available at https://github.com/EnjiLi/3DJA-UNet3Plus .

Deep learning is currently the mainstream approach for building extraction tasks in remote-sensing imagery, capable of automatically learning features of buildings in imagery and yielding satisfactory extraction results. However, due to the diverse sizes, irregular layouts, and complex spatial relationships of buildings, extracted buildings often suffer from incompleteness and boundary issues. Gaofen-7 (GF-7), as a high-resolution stereo mapping satellite, provides well-rectified images from its rear-view imagery, which helps mitigate occlusions in highly varied terrain, thereby offering rich information for building extraction. To improve the integrity of the edges of the building extraction results, this paper proposes a dual-task network (Edge-UNet, EUnet) based on UNet, incorporating an edge extraction branch to emphasize edge information while predicting building targets. We evaluate this method using a self-made GF-7 Building Dataset, the Wuhan University (WHU) Building Dataset, and the Massachusetts Buildings Dataset. Comparative analysis with other mainstream semantic segmentation networks reveals significantly higher F1 scores for the extraction results of our method. Our method exhibits superior completeness and accuracy in building edge extraction compared to unmodified algorithms, demonstrating robust performance. [ABSTRACT FROM AUTHOR]

• A spatial cognitive learning method for vectorized building extraction is proposed. • Enriched topological embedding of graph nodes increases model generalization ability. • Pooling and up-sampling augmenting feature reuse in graph convolution is introduced. • Obtain-and-play extraction with accurate coordinates of shapely boundary is accessed. Traditional approach from source image to application vectors in building extraction needs additional complex regularization of converted intermediate raster results. While in conversion, the lost detailed artifacts, unnecessary nodes, and messy paths would be labor-intensive to repair errors and topological issues, even aside the inherent problems of blob-like objects and blurry, jagged edges in first-stage extraction. This research explores new graph convolution-driven solution, the spatial-cognitive shaping model (SCShaping), to directly access vectorization form of individual buildings through spatial cognitive approximation to coordinates that form building boundaries. To strengthen graph nodes expressivity, this method enriches topological feature embedding travelling along the model architecture along with features contributed from convolutional neural network (CNN) extractor. To stimulate the neighboring aggregation in graphs, Graph-Encoder-Decoder mechanism is introduced to augment feature reuse integrating complementary graph convolution layers. The strong embedding guarantees effective feature tapping and the robust structure guarantees the feature mining. Comparative studies have been conducted between the proposed approach with five other methods on three challenging datasets. The results demonstrate the proposed approach yields unanimous and significant improvements in mask-wise metrics, which evaluate object integrity and accuracy, as well as edge-wise metrics, which assess contour regularity and precision. The outperformance also indicates better multi-scale object adaptability of SCShaping. The obtain-and-play SCShaping commands a pleasurable implementation way to advance ideal man – machine collaboration. [ABSTRACT FROM AUTHOR]

Fast and automatic extraction of buildings from aerial imagery is essential for various applications. However, it remains a challenge, mainly because it requires the accurate recovery of buildings from high-resolution aerial imagery data. The task is particularly challenging due to the distinct nature of building shapes. The extraction of buildings from aerial imagery is often manually conducted by humans, which makes the process very time-consuming and costly. Furthermore, it is especially challenging to automatically extract the footprints of closely spaced buildings in dense urban areas through traditional segmentation techniques. This study proposes an instance segmentation-based building extraction technique for effective building detection and segmentation. The proposed method combines transfer learning with Mask Regional Convolutional Neural Network (Mask R-CNN) integrated with PointRend to locate and generate high-quality segmentation masks for building instances. In addition, several data augmentation strategies are also implemented to enlarge the training dataset. Results indicate that the proposed instance segmentation-based method for building extraction is able to execute distinctly efficient and effective automatic segmentation of distinctive and complex-shaped buildings in a dense urban area. [ABSTRACT FROM AUTHOR]

Construction waste is an inevitable byproduct of urban renewal, causing severe pressure on the environment, health, and ecology. Accurately estimating the production of construction waste is crucial for assessing the consumption of urban renewal. However, traditional manual estimation methods rely heavily on statistical data and historical experience, which lack flexibility in practical applications and are time-consuming and labor-intensive. In addition, their accuracy and timeliness need to be improved urgently. Fortunately, with the advantages of high-resolution remote sensing images (HRSIs) such as strong timeliness, large amounts of information, and macroscopic observations, they are suitable for the large-scale dynamic change detection of construction waste. However, the existing deep learning models have a relatively poor ability to extract and fuse features for small and multi-scale targets, and it is difficult to deal with irregularly shaped and fragmented detection areas. Therefore, this study proposes a Multi-scale Target Attention-Enhanced Network (MT-AENet), which is used to dynamically track and detect changes in buildings and construction waste disposal sites through HRSIs and accurately estimate the annual production of urban construction waste. The MT-AENet introduces a novel encoder–decoder architecture. In the encoder, ResNet-101 is utilized to extract high-level semantic features. A depthwise separable-atrous spatial pyramid pooling (DS-ASPP) module with different dilation rates is constructed to address insufficient receptive fields, resolving the issue of discontinuous holes when extracting large targets. A dual-attention mechanism module (DAMM) is employed to better preserve positional and channel details. In the decoder, multi-scale feature fusion (MS-FF) is utilized to capture contextual information, integrating shallow and intermediate features of the backbone network, thereby enhancing extraction capabilities in complex scenes. The MT-AENet is used to extract buildings and construction waste at different periods in the study area, and the actual production and landfill volume of construction waste are calculated based on area changes, indirectly measuring the rate of urban construction waste resource conversion. The experimental results in Changping District, Beijing demonstrate that the MT-AENet outperforms existing baseline networks in extracting buildings and construction waste. The results of this study are validated according to government statistical standards, providing a promising direction for efficiently analyzing the consumption of urban renewal. [ABSTRACT FROM AUTHOR]

Automatic building detection from remote sensing images holds significant applications in various domains, including cartography, land-use change detection, urban planning, and 3D intelligent city construction. Driven by the innovation of deep learning technology, convolutional neural networks (CNNs) have emerged as a powerful framework for automatic interpretation, exhibiting hierarchical feature generation and robust representation for building extraction. However, the inherent computational limitations of convolutional operators hinder the acquisition of an effective global receptive field. Recent advancements in deep learning have witnessed the adoption of the multi-head self-attention (MHSA) mechanism by Transformer, presenting the superiority of global context construction. Nonetheless, this network structure encounters difficulties in effectively modelling two-dimensional spatial structure features with scale variation in semantic segmentation and often has huge parameters with high algorithm complexity. To address the above problems, this paper proposed a hybrid deep neural network synergistically harnessing the superiority of CNN and Transformer to extract buildings from remote sensing images. Meanwhile, an improved MHSA mechanism is developed to facilitate the construction of multiscale global representations, concurrently reducing computational complexity. Furthermore, the cross-level MHSA modules the semantic correlation between the deep and shallow-level features to refine spatial fine-grained information. The proposed method was comprehensively evaluated on two building datasets via quantitative analysis and visualization. The experimental results confirmed that the developed network and modules outperform other state-of-the-art methods and can effectively improve the accuracy (93.4% F1-score on the WHU dataset, 89.38% F1-score on the CAMB dataset) with high efficiency for building extraction. [ABSTRACT FROM AUTHOR]

Automatic building extraction from very high-resolution remote sensing images is of great significance in several application domains, such as emergency information analysis and intelligent city construction. In recent years, with the development of deep learning technology, convolutional neural networks (CNNs) have made considerable progress in improving the accuracy of building extraction from remote sensing imagery. However, most existing methods require numerous parameters and large amounts of computing and storage resources. This affects their efficiency and limits their practical application. In this study, to balance the accuracy and amount of computation required for building extraction, a novel efficient lightweight residual network (ELRNet) with an encoder-decoder structure is proposed for building extraction. ELRNet consists of a series of downsampling blocks and lightweight feature extraction modules (LFEMs) for the encoder and an appropriate combination of LFEMs and upsampling blocks for the decoder. The key to the proposed ELRNet is the LFEM which has depthwise-factorised convolution incorporated in its design. In addition, the effective channel attention (ECA) added to LFEM, performs local cross-channel interactions, thereby fully extracting the relevant information between channels. The performance of ELRNet was evaluated on the public WHU Building dataset, achieving 88.24% IoU with 2.92 GFLOPs and 0.23 million parameters. The proposed ELRNet was compared with six state-of-the-art baseline networks (SegNet, U-Net, ENet, EDANet, ESFNet, and ERFNet). The results show that ELRNet offers a better tradeoff between accuracy and efficiency in the automatic extraction of buildings in very highresolution remote sensing images. This code is publicly available on GitHub (https://github.com/GaoAi/ELRNet). [ABSTRACT FROM AUTHOR]

The deep learning-based building extraction methods produce different feature maps at different stages of the network, which contain different information features. The detailed information of the feature maps decreases along the depth of the network, and insufficiently detailed information results in limited accuracy. However, existing methods are incapable of making full use of low-level feature maps with rich details. To overcome these shortcomings, we proposed a Cross-stage Features Fusion Network (CFF-Net) for building extraction from remote sensing images. In the CFF-Net, we innovatively proposed a Cross-stage Features Fusion (CFF) module that fuses different features generated at different stages. And we used the attention mechanism to make the network more focused on important information at different scales. To further improve the accuracy of building extraction, we designed the Prediction Enhancement (PE) module, where the last convolutional layer and the feature map generated in the intermediate stage are used for prediction at the same time to enhance the final result. To evaluate the effectiveness of the proposed network, we conduct quantitative and qualitative experiments on the two publicly available datasets, i.e. the Inria dataset and the WHU datasets. CFF-Net outperformed other state-of-the-art algorithms on the two datasets in IoU and F1 metrics. The efficiency analysis reveals that the proposed CFF-Net achieves a great balance between building extraction performance and complexity/efficiency, with faster convergence and higher robustness.

Extraction of color and texture features of buildings from high-resolution remote sensing images often encounters the problems of interference of background information and varying target scales. In addition, most of the current attention mechanisms focus on building key feature selection for building extraction optimization, but ignore the influence of the complex background. Hence, we propose incorporating a novel reverse attention module into the network. The innovative module enables the model to selectively extract crucial building features while suppressing the impact of intricate background noise. It mitigates the influence of uniform spectral and structurally similar heterogeneous background targets on building segmentation and extraction. As a result, the overall generalizability of the model is improved. The reverse attention can also emphasize and amplify the specific details pertaining to the boundaries of the target. Furthermore, we couple a new multi-branch convolution block into the encoder, integrating dilated convolutions with multiple dilation rates. Compared to other methods that use only one multi-scale module to extract multi-scale information from high-level features, we use different receptive field convolutions to simultaneously capture multi-scale targets from multi-level features, effectively improving the ability of the model to extract multi-scale building features. The experimental findings demonstrate that our proposed multi-branch reverse attention semantic segmentation network achieves IoU of 90.59% and 81.79% on the well-known WHU building and Inria aerial image datasets, respectively.

Although the emergence of deep learning has improved the performance of automatic building extraction, there is still a long way to go before it can completely replace the labour-intensive manual delineation of building contours. To narrow the gap between building extraction results of deep learning methods and manual-level annotations, this paper introduces an interactive semantic segmentation framework that uses manual clicks as interactive information to guide the process of semantic segmentation towards the manual annotation level. In our framework, we first use an interactive semantic segmentation network for coarse building extraction from high resolution remote sensing images. Then, we use an optimization network to further refine the extraction results. We comprehensively compare the automatic deep learning methods, the proposed interactive building extraction framework, and the full manual delineation in practical experimental settings. First, our interactive method can significantly improve the performance of automatic building extraction. Second, by comparing the efficiency of manual annotation using the ArcGIS software and our interactive method, it is found that the proposed method can save more than half of the time. The above results show the potential of the interactive method in improving the efficiency of contour annotation and reducing the cost of manual annotation. [ABSTRACT FROM AUTHOR]

Identifying urban buildings in high-resolution RGB images presents challenges, mainly due to the absence of near-infrared bands in UAVs and Google Earth imagery and the diversity in building attributes. Deep learning (DL) methods, especially Convolutional Neural Networks (CNNs), are widely used for building extraction but are primarily pixel-based. Geographic Object-Based Image Analysis (GEOBIA) has emerged as an essential approach for high-resolution imagery. However, integrating GEOBIA with DL models presents challenges, including adapting DL models for irregular-shaped segments and effectively merging DL outputs with object-based features. Recent developments include tabular DL models that align well with GEOBIA. GEOBIA stores various features for image segments in a tabular format, yet the effectiveness of these tabular DL models for building extraction still needs to be explored. It also needs to clarify which features are crucial for distinguishing buildings from other land-cover types. Typically, GEOBIA employs shallow learning (SL) classifiers. Thus, this study evaluates SL and tabular DL classifiers for their ability to differentiate buildings from non-building features. Furthermore, these classifiers are assessed for their capacity to handle roof heterogeneity caused by sun exposure and roof materials. This study concludes that some SL classifiers perform similarly to their DL counterparts, and it identifies critical features for building extraction. [ABSTRACT FROM AUTHOR]

High-spatial-resolution urban buildings play a crucial role in urban planning, emergency response, and disaster management. However, challenges such as missing building contours due to occlusion problems (occlusion between buildings of different heights and buildings obscured by trees), uneven contour extraction due to mixing of building edges with other feature elements (roads, vehicles, and trees), and slow training speed in high-resolution image data hinder efficient and accurate building extraction. To address these issues, we propose a semantic segmentation model composed of a lightweight backbone, coordinate attention module, and pooling fusion module, which achieves lightweight building extraction and adaptive recovery of spatial contours. Comparative experiments were conducted on datasets featuring typical urban building instances in China and the Mapchallenge dataset, comparing our method with several classical and mainstream semantic segmentation algorithms. The results demonstrate the effectiveness of our approach, achieving excellent mean intersection over union (mIoU) and frames per second (FPS) scores on both datasets (China dataset: 85.11% and 110.67 FPS; Mapchallenge dataset: 90.27% and 117.68 FPS). Quantitative evaluations indicate that our model not only significantly improves computational speed but also ensures high accuracy in the extraction of urban buildings from high-resolution imagery. Specifically, on a typical urban building dataset from China, our model shows an accuracy improvement of 0.64% and a speed increase of 70.03 FPS compared to the baseline model. On the Mapchallenge dataset, our model achieves an accuracy improvement of 0.54% and a speed increase of 42.39 FPS compared to the baseline model. Our research indicates that lightweight networks show significant potential in urban building extraction tasks. In the future, the segmentation accuracy and prediction speed can be further balanced on the basis of adjusting the deep learning model or introducing remote sensing indices, which can be applied to research scenarios such as greenfield extraction or multi-class target extraction. [ABSTRACT FROM AUTHOR]

Objectives: The distribution of buildings is an important indicator to measure regional development. Automatic extraction of building information from remote sensing images is of great significance for urban and rural planning. Most existing methods underestimate building details such as boundaries and tiny buildings. Methods: In order to increase attention to building details, a dual-stream detail-concerned network (DSDCNet) is proposed in an encoder-decoder manner. First, a dual-stream feature extraction module is used to extract semantic features and detail-concerned features. They are fed into the decoder consisting of a series of detail refinement modules where detail-concerned features make up for the missing details of semantic features and the semantic features enhance semantic continuity of detail-concerned features. Then, a semantic-detail fusion module is used to fuse and squeeze two refined features. Furthermore, deep supervision is conducted and the multi-level outputs are used in detail-concerned loss function so as to strengthen the supervision of building details. Results: Five mainstream networks are selected for comparison in WHU dataset, ISPRS Vaihingen dataset and a domestic high-resolution dataset. The evaluation results show that DSDCNet has better performance than other networks, especially in F1-score and intersection over union without introducing too much network complexity. Conclusions: DSDCNet not only manages to improve the overall performance of building extraction results, but also effectively maintains the integrity of building boundaries and reduces the missed detection of small buildings. It has better extraction effect on the buildings with small size and complex context. [ABSTRACT FROM AUTHOR]

Objectives: Accurately and automatically extracting buildings from high-resolution remote sensing images is of great significance in many aspects, such as urban planning, map data updating, emergency response, etc. The problems of missing and wrong detection of buildings and missing boundaries caused by spectrum confusion still exist in the existing full convolution neural networks. Methods: In order to overcome the limitations, a multi-feature fusion and object-boundary joint constraint network is proposed based on an encoding and decoding structure. In the encoding stage, the continuous-atrous spatial pyramid module is positioned at the end to extract and combine multi-scale features without sacrificing too much useful information. In the decoding stage, more accurate building features are integrated into the network and the boundary is refined by implementing the multi-output fusion constraint structure based on object and boundary. In the skip connection between the encoding and decoding stages, the convolutional block attention module is introduced to enhance the effective features. Furthermore, the multi-level output results from the decoding stage are used to build a piecewise multi-scale weighted loss function for fine network parameter updating. Results: Comparative experimental analysis is performed on the WHU and Massachusetts building datasets. The results show that the building extraction results of the proposed method are close to the ground truth. The quantitative evaluation result is higher than the other five state-of-the-art approaches. Specifically, intersection over union and F1-score on WHU and Massachusetts building datasets reach 90.44%, 94.98%, and 72.57%, 84.10%, respectively. The proposed model outperforms MFCNN and BRRNet in both complexity and efficiency. Conclusions: The proposed method not only improves the accuracy and integrity of extraction results in spectral obfuscation buildings, but also maintains a good boundary with strong robustness in scale. [ABSTRACT FROM AUTHOR]

Unmanned aircraft vehicles (UAVs) capture oblique point clouds in outdoor scenes that contain considerable building information. Building features extracted from images are affected by the viewing point, illumination, occlusion, noise and image conditions, which make building features difficult to extract. Currently, ground elevation changes can provide powerful aids for the extraction, and point cloud data can precisely reflect this information. Thus, oblique photogrammetry point clouds have significant research implications. Traditional building extraction methods involve the filtering and sorting of raw data to separate buildings, which cause the point clouds to lose spatial information and reduce the building extraction accuracy. Therefore, we develop an intelligent building extraction method based on deep learning that incorporates an attention mechanism module into the Samling and PointNet operations within the set abstraction layer of the PointNet++ network. To assess the efficacy of our approach, we train and extract buildings from a dataset created using UAV oblique point clouds from five regions in the city of Bengbu, China. Impressive performance metrics are achieved, including 95.7% intersection over union, 96.5% accuracy, 96.5% precision, 98.7% recall and 97.8% F1 score. And with the addition of attention mechanism, the overall training accuracy of the model is improved by about 3%. This method showcases potential for advancing the accuracy and efficiency of digital urbanization construction projects. [ABSTRACT FROM AUTHOR]

Automated extraction of polygonal building contours from high-resolution remote sensing images is important for various applications. However, it remains a difficult task to achieve automated extraction of polygonal buildings at the level of human delineation due to diverse building structures and imperfect image conditions. In this paper, we propose Line2Poly, an end-to-end approach that uses feature lines as geometric primitives to achieve polygonal building extraction by recovering topological relationships among these lines within an individual building. To extract building feature lines with precision, we adopt a two-stage strategy that combines Convolutional Neural Network (CNN) and transformer architectures. A CNN-based module extracts preliminary feature lines, which serve as positional priors for initializing positional queries in the subsequent transformer-based module. For polygonal building contour reconstruction, we devise a learnable polygon topology reconstruction module that predicts adjacency relationships among discrete lines, and integrates lines into building polygons. The resultant building polygons, based on feature lines, exhibit inherent regularity that aligns with manual labeling standards. Extensive experiments on the Vectorizing World Buildings dataset, the WHU aerial building dataset and the WHU-Mix (vector) dataset validate Line2Poly's impressive performance in building feature line extraction and instance-level building detection. Moreover, Line2Poly's predictions exhibit the highest level of concurrence with manual delineations, with over 83% agreement on the WHU aerial building test set and 68.7/59.7% on the WHU-Mix (vector) test set I and II, respectively. [ABSTRACT FROM AUTHOR]

In recent years, semantic segmentation algorithms based on deep learning have been widely used in building extraction, which requires large sample data and does not consider the geometric features of the building, and the effect of the extraction is greatly affected by the data scene, while the traditional methods are difficult to extract the remote sensing buildings accurately because they only consider their greyscale features when extracting them. To solve this problem, we propose a method for building classification extraction from remote sensing images that combine over-pixel and maximum interclass variance. The method combines superpixel and maximum interclass variance (OTSU). First, a number of superpixel subregions with different shapes and sizes are generated based on the watershed transform. Then, the superpixels of buildings are merged using the spectral features of buildings, so the first extraction of buildings is achieved by this method.Then, the noise is suppressed with median filtering. Finally, the post-extraction of buildings is performed according to the OTSU algorithm. In this paper, seven images of buildings located in different landscapes were selected. The experimental results show that the algorithm is more advantageous than the classical algorithm and the deep learning algorithm.. [ABSTRACT FROM AUTHOR]

Extraction of color and texture features of buildings from high-resolution remote sensing images often encounters the problems of interference of background information and varying target scales. In addition, most of the current attention mechanisms focus on building key feature selection for building extraction optimization, but ignore the influence of the complex background. Hence, we propose incorporating a novel reverse attention module into the network. The innovative module enables the model to selectively extract crucial building features while suppressing the impact of intricate background noise. It mitigates the influence of uniform spectral and structurally similar heterogeneous background targets on building segmentation and extraction. As a result, the overall generalizability of the model is improved. The reverse attention can also emphasize and amplify the specific details pertaining to the boundaries of the target. Furthermore, we couple a new multi-branch convolution block into the encoder, integrating dilated convolutions with multiple dilation rates. Compared to other methods that use only one multi-scale module to extract multi-scale information from high-level features, we use different receptive field convolutions to simultaneously capture multi-scale targets from multi-level features, effectively improving the ability of the model to extract multi-scale building features. The experimental findings demonstrate that our proposed multi-branch reverse attention semantic segmentation network achieves IoU of 90.59% and 81.79% on the well-known WHU building and Inria aerial image datasets, respectively. [ABSTRACT FROM AUTHOR]

The efficient semantic segmentation of buildings in high spatial resolution remote sensing images is a technical prerequisite for land resource management, high-precision mapping, construction planning and other applications. Current building extraction methods based on deep learning can obtain high-level abstract features of images. However, the extraction of some occluded buildings is inaccurate, and as the network deepens, small-volume buildings are lost and edges are blurred. Therefore, we introduce a multi-resolution attention combination network, which employs a multiscale channel and spatial attention module (MCAM) to adaptively capture key features and eliminate irrelevant information, which improves the accuracy of building extraction. In addition, we present a layered residual connectivity module (LRCM) to enhance the expression of information at different scales through multi-level feature fusion, significantly improving the understanding of context and the capturing of fine edge details. Extensive experiments were conducted on the WHU aerial image dataset and the Massachusetts building dataset. Compared with state-of-the-art semantic segmentation methods, this network achieves better building extraction results in remote sensing images, proving the effectiveness of the method. [ABSTRACT FROM AUTHOR]

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There are problems such as poor interpretability and insufficient generalization ability when extracting buildings from high-resolution remote sensing images based on deep learning. This paper proposes a building extraction model called BPKG-SegFormer (Building Prior Knowledge Guided SegFormer) that combines prior knowledge of buildings with data-driven methods. This model constructs a building feature attention module and utilizes the multi-task loss function to optimize the extraction of buildings. Experimental results show that on the WHU building dataset, the proposed model outperforms UNet, Deeplabv3 + , and SegFormer models with OA, P, R, and MIoU of 96.63%, 95.94%, 94.76%, and 90.6%, respectively. The BPKG-SegFormer model extracts buildings with more regular shapes and flatter edges, reducing internal voids and increasing the number of correctly detected buildings. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNN) have been developed for several years in the field of extracting buildings from remote sensing images. Vision Transformer (ViT) has recently demonstrated superior performance over CNN, thanks to its ability to model long-range dependencies through self-attention mechanisms. However, most existing ViT models lack shape information enhancement for the building objects, resulting in insufficient fine-grained segmentation. To address this limitation, we construct an efficient dual-path ViT framework for building segmentation, termed shape-aware enhancement Vision Transformer (SAEViT). Our approach incorporates shape-aware enhancement module (SAEM) that perceives and enhances the shape features of buildings using multi-shapes of convolutional kernels. We also introduce multi-pooling channel attention (MPCA) to exploit channel-wise information without squeezing the channel dimension. Furthermore, we propose a progressive aggregation upsampling model (PAUM) in the decoder to aggregate multilevel features using a progressive upsampling methodology, coupled with the utilization of the soft-pool algorithm operating on the channel axis. We evaluate our model on three public building datasets. The experimental results show that SAEViT obtains a significant improvement on various datasets, confirming its efficacy. Compared with several state-of-the-art models, SAEViT achieves a comprehensive transcendence in overall performance. [ABSTRACT FROM AUTHOR]

Accurately extracting pixel-level buildings from high-resolution remote sensing images is significant for various geographical information applications. Influenced by different natural, cultural, and social development levels, buildings may vary in shape and distribution, making it difficult for the network to maintain a stable segmentation effect of buildings in different areas of the image. In addition, the complex spectra of features in remote sensing images can affect the extracted details of multi-scale buildings in different ways. To this end, this study selects parts of Xi'an City, Shaanxi Province, China, as the study area. A parallel encoded building extraction network (MARS-Net) incorporating multiple attention mechanisms is proposed. MARS-Net builds its parallel encoder through DCNN and transformer to take advantage of their extraction of local and global features. According to the different depth positions of the network, coordinate attention (CA) and convolutional block attention module (CBAM) are introduced to bridge the encoder and decoder to retain richer spatial and semantic information during the encoding process, and adding the dense atrous spatial pyramid pooling (DenseASPP) captures multi-scale contextual information during the upsampling of the layers of the decoder. In addition, a spectral information enhancement module (SIEM) is designed in this study. SIEM further enhances building segmentation by blending and enhancing multi-band building information with relationships between bands. The experimental results show that MARS-Net performs better extraction results and obtains more effective enhancement after adding SIEM. The IoU on the self-built Xi'an and WHU building datasets are 87.53% and 89.62%, respectively, while the respective F1 scores are 93.34% and 94.52%. [ABSTRACT FROM AUTHOR]

Identifying and extracting urban features such as buildings and producing accurate information about the location of features from remotely sensed images is important in updating maps, spatial databases, urban planning, and meeting the needs of urban services. Supervised methods of producing training data were widely applied in deriving accurate information from such images. However, those approaches require human intervention and are therefore time-consuming, and associated with classification accuracy limitations. This study presents a novel approach for extracting buildings from remotely sensed images by automating training data selection steps for supervised classification. To prepare training samples intelligently and automatically, a knowledge-based integration of radar and optical images was used. First, a general height constraint was applied to a digital surface model (DSM) obtained from radar images to divide the image pixels into two parts. Then, by applying a local height filter in each part, pixels representing buildings were extracted. Considering that the buildings are not the only features with significant heights, another constraint defined as coherence constraint was considered. Then, histogram of the optical image pixels corresponding to the filtered radar DSM pixels extracted from the previous steps was used to select sample training pixels. By considering pixels within the maximum range of the histogram, the final building samples for Support Vector Machine (SVM) classification were derived and applied for building extraction. The output showed that it sufficiently and reliably improved the existing supervised classification limitation to extract building. The performance of the proposed method was evaluated over different image datasets regardless of input image type and region. It achieved an overall average accuracy of 92% and a kappa coefficient of 0.83. [ABSTRACT FROM AUTHOR]

Automated building extraction is imperative for several geospatial applications such as monitoring disaster-affected buildings and urban planning. Existing deep learning (DL)-based building extraction methods fail to capture high-level semantic features due to the complex nature and diverse appearance of visually similar structures. To address this issue, in this letter, we propose an enhanced multi-scale attentive feature fusion network (EMAFF-Net) for building extraction from remote sensing (RS) images. EMAFF-Net is an end-to-end DL architecture based on U-Net that includes: i) an encoder; ii) an enhanced multi-scale feature fusion (EMFF) module; iii) a refined multi-scale convolutional block attention (RM-CBAM) module and iv) a decoder with refinement layers. To extract multi-scale contextual information, we incorporate an RM-CBAM module into the lateral connections of encoder-decoder layers of EMAFF-Net. Further, a novel EMFF module is integrated to obtain fine-grained features from the lowest encoder layer with minimal trainable parameters required. We evaluate the performance of the proposed network on two benchmark datasets: Massachusetts (MAS) and WHU building datasets. The experimental results show that the proposed approach outperforms the existing reference methods showcasing its potential in practical applications. [ABSTRACT FROM AUTHOR]

Automatic building extraction from very high-resolution remote sensing images is of great significance in several application domains, such as emergency information analysis and intelligent city construction. In recent years, with the development of deep learning technology, convolutional neural networks (CNNs) have made considerable progress in improving the accuracy of building extraction from remote sensing imagery. However, most existing methods require numerous parameters and large amounts of computing and storage resources. This affects their efficiency and limits their practical application. In this study, to balance the accuracy and amount of computation required for building extraction, a novel efficient lightweight residual network (ELRNet) with an encoder-decoder structure is proposed for building extraction. ELRNet consists of a series of downsampling blocks and lightweight feature extraction modules (LFEMs) for the encoder and an appropriate combination of LFEMs and upsampling blocks for the decoder. The key to the proposed ELRNet is the LFEM which has depthwise-factorised convolution incorporated in its design. In addition, the effective channel attention (ECA) added to LFEM, performs local cross-channel interactions, thereby fully extracting the relevant information between channels. The performance of ELRNet was evaluated on the public WHU Building dataset, achieving 88.24% IoU with 2.92 GFLOPs and 0.23 million parameters. The proposed ELRNet was compared with six state-of-the-art baseline networks (SegNet, U-Net, ENet, EDANet, ESFNet, and ERFNet). The results show that ELRNet offers a better tradeoff between accuracy and efficiency in the automatic extraction of buildings in very highresolution remote sensing images. This code is publicly available on GitHub (https://github.com/GaoAi/ELRNet).

Building extraction from remote sensing (RS) image holds a crucial position in the fields of urban planning and sustainable development. In high-resolution (HR) RS images, the characteristics of buildings, including their shapes, structures, and textures, become increasingly complex. This complexity poses considerable challenges to the prediction and recognition of small, dense, and complex-shaped buildings. To address these problems, we present a novel overcomplete convolutional attention network (OCANet) to enhance the accuracy of building extraction from HR RS image. Specifically, the proposed method adopts a multiscale convolutional attention encoder to focus on the two-dimensional structure of the building image while enhancing computational efficiency. Additionally, an overcomplete fusion branch module is introduced to control the network's deep receptive field size, enabling a more concentrated focus on smaller and denser buildings. Furthermore, an edge refinement fusion module is proposed to further enhance the network's capability to extract building edge details by integrating shallow feature information from different scales with deep semantic information. The efficacy of the individually designed modules is validated through ablation studies on public datasets, including the WHU aerial building dataset and the Massachusetts building dataset. Additionally, a dataset leveraging Gaofen-2 imagery, featuring a variety of building types, is introduced to benchmark against other state-of-the-art networks. Both qualitative and quantitative evaluations demonstrate the ability of OCANet to extract dense, small, and complex-shaped buildings in complex urban landscapes. The proposed method provides excellent performance compared to other networks while reducing computational overhead.

Building extraction is a challenging task in remote sensing images (RSI) interpretation. Fusing RSI from different sources, such as high-resolution RSI and LiDAR, is a common strategy to improve the building extraction accuracy. However, the acquisition of multisource registered RSI of the same region is time-consuming and laborious, which limits the practical application of multisource fusion strategy. Benefiting from the success of large models in different fields in recent years, we proposed to extract depth information from large models and fuse it with corresponding RSI to improve the building extraction accuracy. Directly introducing the extracted depth information into the original image is not helpful for improving the building extraction accuracy. Therefore, we designed a dedicated deep feature fusion module (DFFM) for deep feature fusion. Specifically, we designed a depth feature fusion network (DFF-Net) composed of a depth information extraction module, a DFFM and a U-Net. The DFF-Net successfully improves building extraction accuracy by fusing the extracted depth information. To evaluate the validity of this method, we conducted abundant building extraction experiments on the WHU, Massachusetts, and Inria building datasets. Compared with comparative methods, our method obtained the highest IoU on all these three RSI building datasets, which demonstrates its simplicity and effectiveness.

Deep learning methods are widely used in building information extraction from remote sensing images (RSIs). However, this task still faces great challenges. First, it is difficult to perform accurate boundary localization due to the complex contextual relationship of the building boundary area. Second, the heterogeneity caused by different materials on building tops, as well as environmental factors such as climate and vegetation, complicates the extraction of top information. Finally, given the complexity of the RSIs, the accuracy and generalization of the existing method still need to be further enhanced. This study introduces the boundary enhancement and multiscale refinement fusion network to overcome these challenges. The boundary-aware self-attention module is initially proposed to refine the network's boundary detection capabilities. It applies the distance transform algorithm across both channel and spatial dimensions, reducing boundary fluctuation and enhancing the accuracy of building boundary extraction. Subsequently, we present the multiscale refined fusion module to resolve discontinuities within buildings caused by rooftop obstructions. This module effectively merges high and low-level features, overcoming information gaps through the strategic integration of multiscale data. To demonstrate the efficacy of our methodology, we conducted comprehensive experiments and analyses using the WHU Building Dataset and the Inria Aerial Image Labeling Dataset, both known for their complexity. Our method surpassed 16 contemporary state-of-the-art techniques, achieving IoU scores of 91.15% and 79.52% for each dataset, respectively, marking the highest accuracy levels reported. Furthermore, in-depth discussions on efficiency, generalizability, and ablation studies emphasized our method's robustness and adaptability.

Effective extraction of building edge information based on high-resolution remote sensing images is the basis for efficient urban 3D modeling. Existing building extraction methods still have some problems, such as an uncertain segmentation scale, effective feature selection, and sample selection. In this paper, we propose a practical building extraction method based on convolutional network edge-enhanced attention U2-Net (EA U2-Net) to accurately achieve multi-scale extraction of buildings from remote sensing imagery. First, the U2-Net is used as the backbone network for building extraction because each stage of the network is filled by residual U-block (RSU), and the network can better aggregate multi-scale features. Second, the building edge feature map is introduced into the generation network to compensate for the problems of insufficient extracted building edge features and loss of detail. Finally, the convolutional block attention module is used to achieve effective feature extraction of buildings. We performed the experiment on the WHU building dataset, and the experimental results showed that the EA U2-Net model has significantly improved the ability to extract buildings, with an accuracy of 96.30%, a recall rate of 94.91%, f1 of 95.26%, and iou of 91.57%. This proves that EA U 2 - Net can achieve better remote sensing image-building segmentation results. Finally, in view of the problem that the deep learning network relies on training samples, this study examined the influence of the number of building samples, sample purity, and sample resolution on the effect of building extraction. The results confirmed that reasonable sample parameter settings can improve the target extraction accuracy and the optimal sample parameter combination was verified in this experiment.

Extraction of vectorized building instances from remote sensing images has made significant progress in recent years. The extraction of vectorized building maps enables rapid, large-scale updates to geospatial databases. Recently, the contour-based methods have achieved amazing success because their learning goals are more compatible with vectorized polygons. However, existing contour-based approaches achieve contour updates only by learning the local features at each vertex, which can lead to a contour smoothing problem. Besides, these methods achieve classification via features at the center point, which hinders the receptive field at the instance level. Our primary motivation is to overcome existing limitations introduced by local modeling during contour regression while simultaneously improving the instance classification performance. In this article, we utilize instance-level features to guide the contour learning process. An instance-aware contour regression (IACR) module is designed to update the local contour features via cross attention on the instance-level features. Furthermore, based on the IACR module, we propose a novel vectorized building extraction framework BuildingVec, which interacts between the contour regression branch and instance classification branch through a well-designed cascade architecture. We also build a vectorized building dataset with fine-grained categories, named UBCv2 (vec), to benefit the study of vectorized extraction for fine-grained buildings. Experiments on the UBCv2 (vec) dataset demonstrate that BuildingVec achieves state-of-the-art performance compared to both mask-based methods and contour-based methods. When compared to other vectorized extraction methods on the Crowd AI dataset, BuildingVec also achieves a state-of-the-art performance with an $\text{AP}_{50}$ of 93.1%.

Building information serves as a critical foundational dataset in the fields of urban planning, smart cities and surveying and mapping, and high-resolution remote sensing (HRRS) imagery has become a vital data source for extracting building information. However, automatically extracting building information from HRRS imagery using a single feature or method remains a challenging task. On one hand, buildings exhibit significant variations in terms of their size, color, geometric structures, and other aspects. On the other hand, there are also numerous features in the environment that bear spectral and morphological resemblances to buildings. In this article, we proposed a multifeature fusion framework based on Dempster–Shafer (D-S) theory that consists of two steps for automated building extraction from HRRS imagery. The initial D-S fusion step involves two branches: object-level and pixel-level feature fusion. Then the outcomes are further combined to derive the ultimate building confidence information. In the framework, we introduced a proportional consistency and centroid consistency index to convert pixel-level features to object-level features, thereby facilitating their fusion. In addition, we proposed an initialization module for the basic probability assignment formula, enabling the elimination of the impact of nonbuilding objects and simplifying the construction process of BPAF. The experimental results based on the Nanjing, WHU, and Washington datasets demonstrate the effectiveness of our method, the accuracy outperforms the other four advanced algorithms.

Building extraction is significant for the intelligent interpretation of high-resolution remote sensing images (HRSIs). However, in some complex scenarios where the features of the building and its adjacent ground objects are similar, the current segmentation model cannot distinguish them effectively. Therefore, we propose a complex scenarios adaptive network (CSA-Net) for building extraction. CSA-Net is comprised of the hierarchical-context feature extraction (HFE) module, the global-local feature interaction (GFI) module, and the multiscale-adaptive feature fusion (MFF) structure. The HFE obtains high-level semantic information at different levels and fuses it with low-level detailed information by skipping connections to enhance the reasoning and perception ability of building structure in complex scenes. Then, the GFI acquires global-local features of buildings and their surrounding environment via dense multiscale dilated convolution. The information can be shared through efficient interaction among features, and irrelevant backgrounds can be suppressed. Then, in the up-sampling process, the MFF alleviates the feature loss and enhances the robustness of the network by using feature fusion after layer-by-layer adaptive weight allocation. Experiments show that CSA-Net outperforms other comparable methods, with intersection over union values of 79.99%, 89.75%, and 73.59%, respectively, on the Google Arlinton, WHU, and Massachusetts building datasets. The visual comparison results demonstrate that our method can enhance the accuracy of building extraction in complex scenes. Meanwhile, the efficiency results indicate our approach strikes a balance between calculation parameters and time and achieves high levels of efficiency.

Building extraction and change monitoring in remote sensing (RS) imagery play pivotal roles in various applications, including urban planning, disaster management, and infrastructure monitoring. While significant progress has been made in single and bitemporal RS images, effectively harnessing the rich temporal information of time series RS images remains a challenge. Time series RS images offer an extended temporal span for monitoring dynamic changes in building instances. However, they often exhibit noticeable appearance discrepancies and feature variations, presenting substantial obstacles to effective multitemporal information aggregation. To address these challenges, we introduce a Building Extraction and Change Monitoring Network (BuildMon), which jointly explores the segmentation masks, location tracking, and construction status of building instances. Our approach incorporates a spatial-temporal transformer to model relationships between images at different time spans. The windowed attention module within it can capture spatial-temporal context for a larger scope of feature aggregation. For enhancing the performance on both tasks, we adopted ground truth masks and semantic change information together as supervisory signals for BuildMon. This is complemented by the specially designed change-guided loss function, which specifically highlights regions of change and assigns targeted weights to building areas within the imagery. To validate the effectiveness of our method, we conduct comprehensive experiments on the SpaceNet 7 dataset. The results showcase the state-of-the-art performance of our approach, achieving mIoU and SpaceNet Change and Object Tracking metrics of 67.90 and 39.73, respectively.

Accurately extracting buildings from high-resolution remote sensing images is crucial for human productivity and livelihood in urban areas. Due to varying scales and indistinct boundaries of buildings, it is crucial to fully leverage the high- and low-frequency features in building extraction from remote sensing images. However, previous studies have solely relied on either low- or high-frequency features, leading to errors such as omissions or internal holes in the detected buildings at various scales. Although some studies have considered the integration between both high- and low-frequency features, they overlook the suitability of different network depths for extracting different frequency features. A novel network called Cascaded Inception Conv-Former Network (CICF-Net) is proposed in this study to solve these problems. It leverages the parallel combination of convolutional neural network and Transformer to efficiently extract high- and low-frequency features for building extraction. In the encoder, as the network depth grows, we gradually reduce the contribution of high-frequency branch and enhance the focus on low-frequency branch. Moreover, a cascaded fusion strategy is employed to extract and integrate multiscale high- and low-frequency features. Meanwhile, we propose gated convolution UperNet as the decoder, which utilizes recursive gated convolution to facilitate multilevel spatial interactions and better restoration of fine-grained spatial details for building segmentation. The proposed CICF-Net achieves competitive accuracies on three public benchmarks: Massachusetts Building Dataset, WHU Aerial Building Dataset, and Inria Aerial Image Labeling Dataset, with IoU of 75.17%, 91.45%, and 81.28%, respectively. This provides strong evidence of its effectiveness in building extraction, as it can accurately capture spatial details and context of buildings.