Multi-scale Building Research Articles

China's first sub-meter building footprints derived by deep learning

The high spatial resolution building footprints are crucial for understanding urban development and its associated applications. However, up to now, the sub-meter-level building footprint data of China is still lacking. The challenges arise from two aspects: 1) the number of training samples is inadequate for large-scale building extraction. 2) the accuracy and efficiency of current models are insufficient to conduct large-scale building extraction. Therefore, we propose a framework for large-scale building extraction in this study, including semi-automated sample generation, building extraction model, model training, and post-processing. Specifically, the main technical contributions include: 1) BldgNet (Building Extraction Network) is proposed, including the Large Window Attention, Edge Attention, and Distribution Alignment Module with consideration of spatial contextual information, to address the challenge of the multi-scale building extraction, building boundary delineation, and class imbalance, respectively; 2) a semi-supervised training approach is proposed for large-scale building extraction, leveraging the incomplete information from OpenStreetMap (OSM) to enhance the diversity of building samples and the robustness of the model. Meanwhile, we created an open-source Global Building Dataset (GBD) comprising approximately 800,000 high-resolution (0.25 m) samples. This dataset incorporates diverse building styles worldwide, offering support for global building extraction. Based on the constructed sample set and the proposed deep net, we generated China's first sub-meter (0.5 m) building footprint dataset (CBF). Through testing on 750,000 buildings from 350 cities, the overall F1 score for CBF reached 83.71%. Finally, we validated that the proposed building extraction model can achieve satisfactory results compared to existing representative deep networks. GBD and CBF datasets can be publicly available and downloadable via https://zenodo.org/doi/10.5281/zenodo.10043351.

Robust multi-scale time series prediction for building carbon emissions with explainable deep learning

Accurate prediction dramatically enhances the effectiveness of carbon emission control, making it a valuable tool for achieving decarbonization goals. In this work, we propose a robust explainable encoder-decoder deep learning method for predicting multi-scale short-term carbon emissions and feature importance analysis to predict buildings’ emission patterns accurately. In the encoder, the initial inputs are transformed into a sequence to capture the time series and spatial information, where the model predicts the building emissions with an attention mechanism in the decoder. The hybrid model is trained and validated with a multi-scale dataset collected from a university. Results reveal that (1) After considering the buildings’ interactions, the R2 values regarding individual building emissions predictions are 0.943, 0.938, 0.941, and 0.943, respectively, climbing to 0.952, 0.943, 0.943, and 0.950. (2) Benefiting from the proposed model’s structure, the constructed model can accurately predict the community’s emissions with the R2 equal to 0.952 and keep excellent robustness after adding the white Gaussian noise data with all R2 are above 0.919. (3) The historical hourly emissions, outdoor humidity and time of day are considered the most essential factors responsible for the building emissions. The novelty of this work lies in the multi-scale building emissions prediction that is conducted by the developed hybrid model simultaneously considering the impact of environmental factors and building interactions, allowing us to expand the model’s application scenarios and provide valuable references for decarbonization decision-making.

Multi-Scale Attention Network for Building Extraction from High-Resolution Remote Sensing Images

The precise building extraction from high-resolution remote sensing images holds significant application for urban planning, resource management, and environmental conservation. In recent years, deep neural networks (DNNs) have garnered substantial attention for their adeptness in learning and extracting features, becoming integral to building extraction methodologies and yielding noteworthy performance outcomes. Nonetheless, prevailing DNN-based models for building extraction often overlook spatial information during the feature extraction phase. Additionally, many existing models employ a simplistic and direct approach in the feature fusion stage, potentially leading to spurious target detection and the amplification of internal noise. To address these concerns, we present a multi-scale attention network (MSANet) tailored for building extraction from high-resolution remote sensing images. In our approach, we initially extracted multi-scale building feature information, leveraging the multi-scale channel attention mechanism and multi-scale spatial attention mechanism. Subsequently, we employed adaptive hierarchical weighting processes on the extracted building features. Concurrently, we introduced a gating mechanism to facilitate the effective fusion of multi-scale features. The efficacy of the proposed MSANet was evaluated using the WHU aerial image dataset and the WHU satellite image dataset. The experimental results demonstrate compelling performance metrics, with the F1 scores registering at 93.76% and 77.64% on the WHU aerial imagery dataset and WHU satellite dataset II, respectively. Furthermore, the intersection over union (IoU) values stood at 88.25% and 63.46%, surpassing benchmarks set by DeepLabV3 and GSMC.

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Multi-branch reverse attention semantic segmentation network for building extraction

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.

How the surrounding multi-scale building clusters affect the wind loads of the super high-rise building

Many super high-rise buildings emerge in modern cities with urban development, facilitating work, accommodations, etc. However, their safety risks and accidents due to the wind are urgent problems with the complex flow field in cities. The research on wind loads of super high-rise buildings is thus crucial, but most studies tend to consider only the influence of the surrounding single-scale building clusters, rarely considering multi-scale ones. In this paper, the influence of the surrounding multi-scale building clusters on the wind loads of a super high-rise building is investigated. The wind field of a super high-rise building surrounded by four different arrangements of idealized, simplified buildings is first simulated using computational fluid dynamics (CFD) methods: RANS and Hybrid LES/RANS models. It is found that surrounding tall buildings can significantly affect the pressure distribution on the windward and leeward sides of the super high-rise building, such as fluctuating, extreme, and mean wind pressure. The vortex, formed largely due to short buildings, increases the negative pressure at the back of the super high-rise building. In addition, simulations are conducted for the wind field around the CITIC Tower in Beijing CBD, and it is found that the flow field of the actual building group is more complex due to the strong interactions between buildings, and the flow near the ground is even more complex. All simulation results are validated by the wind tunnel tests. This study can provide important guidance for the wind safety design of super high-rise buildings and the future planning of urban buildings.

A novel deep neural network for multi-scale building extraction from remotely-sensed images

A novel deep neural network for multi-scale building extraction from remotely-sensed images

Sci-Net: scale-invariant model for buildings segmentation from aerial imagery

Buildings’ segmentation is a fundamental task in the field of earth observation and aerial imagery analysis. Most existing deep learning-based methods in the literature can be applied to a fixed or narrow-range spatial resolution imagery. In practical scenarios, users deal with a broad spectrum of image resolutions. Thus, a given aerial image often needs to be re-sampled to match the spatial resolution of the dataset used to train the deep learning model, which results in a degradation in segmentation performance. To overcome this challenge, we propose, in this manuscript, scale-invariant neural network (Sci-Net) architecture that segments buildings from wide-range spatial resolution aerial images. Specifically, our approach leverages UNet hierarchical representation and dense atrous spatial pyramid pooling to extract fine-grained multi-scale representations. Sci-Net significantly outperforms state-of-the-art models on the open cities AI and the multi-scale building datasets with a steady improvement margin across different spatial resolutions.

FERA-Net: A Building Change Detection Method for High-Resolution Remote Sensing Imagery Based on Residual Attention and High-Frequency Features

Buildings can represent the process of urban development, and building change detection can support land use management and urban planning. However, existing building change detection models are unable to extract multi-scale building features effectively or fully utilize the local and global information of the feature maps, such as building edges. These defections affect the detection accuracy and may restrict further applications of the models. In this paper, we propose the feature-enhanced residual attention network (FERA-Net) to improve the performance of the ultrahigh-resolution remote sensing image change detection task. The FERA-Net is an end-to-end network with a U-shaped encoder–decoder structure. The Siamese network is used as the encoder with an attention-guided high-frequency feature extraction module (AGFM) extracting building features and enriching detail information, and the decoder applies a feature-enhanced skip connection module (FESCM) to aggregate the enhanced multi-level differential feature maps and gradually recover the change feature maps in this structure. The FERA-Net can generate predicted building change maps by the joint supervision of building change information and building edge information. The performance of the proposed model is tested on the WHU-CD dataset and the LEVIR-CD dataset. The experimental results show that our model outperforms the state-of-the-art models, with 93.51% precision and a 92.48% F1 score on the WHU-CD dataset, and 91.57% precision and an 89.58% F1 score on the LEVIR-CD dataset.

Utilizing Bounding Box Annotations for Weakly Supervised Building Extraction From Remote-Sensing Images

Image-level weakly supervised semantic segmentation (WSSS) methods have greatly facilitated the extraction of buildings from remote sensing (RS) images. However, the lack of the locations and extents of individual buildings in image-level labels results in some limitations of the methods, especially in the cases of cluttered backgrounds, diverse building shapes and sizes. By utilizing bounding box annotations, a novel WSSS model is developed to improve building extraction from RS images in this paper. Specifically, during the training phase, a multiscale feature retrieval (MFR) module is designed to learn multiscale building features and suppress the background noise inside the bounding box. In the inference phase, multiscale class activation maps (CAM) are generated from multiscale features to achieve accurate building localization. Finally, a pseudo mask generation and correction (PGC) module refines the CAMs to generate and correct the building pseudo masks. Experiments are conducted to examine the proposed model in three datasets, namely, the WHU aerial building dataset, the CrowdAI building dataset, and a self-annotated building dataset. Experimental results demonstrate that the proposed method outperforms baselines, achieving 76.99%, 75.51% and 67.35% in terms of IoU scores on the three challenging datasets, respectively. This paper provides a methodological reference for the application of weakly supervised learning on RS images.

MSFTrans: a multi-task frequency-spatial learning transformer for building extraction from high spatial resolution remote sensing images

ABSTRACT Building extraction is significant in urban planning, economic evaluation, and driverless technology development. However, automatic building extraction from high spatial resolution remote sensing images has been a challenging task due to the various building shapes and colors, imaging conditions, and complex background objects. Current methods in building extraction are generally based on deep convolution networks, and they mostly use an encoder-decoder architecture, wherein detailed building features and small buildings are easily omitted in continuous convolution operations. Moreover, buildings with blurred boundaries are only completely extracted with difficulty. To meet these challenges, we propose a multi-task architecture of frequency-spatial learning Transformer to extract buildings from high spatial resolution remote sensing images. Different from current architecture, we designed a frequency-spatial learning module in the framework of multi-task to synthesize the multi-scale spatial features and frequency decomposition features of high-resolution image. Spiking convolution is proposed in this study to enhance the frequency features of buildings by mimicking the neural transmission in human brains. In this way, multi-scale building features can be better preserved and distinguished from background objects. Moreover, a masked-attention Transformer is adopted to improve multi-scale building mask prediction accuracy by synthesizing successive pixel-wise up-sampled feature maps. We also propose a strategy to evaluate the practical transferability of the proposed method by mimicking practical application cases through training and evaluating images with different spatial resolutions from different study areas and datasets. Experiments using five public building datasets (WHU-Building Satellite Dataset I, WHU-Building Satellite Dataset II, Massachusetts Buildings Dataset, Inria Aerial Image Dataset, xBD Building Dataset) demonstrate the strong potential applicability of our proposed method for practical application cases. Our method outperforms five recently proposed state-of-the-art semantic segmentation methods with 36.60% accuracy improvement on extracted buildings and approximately 53.55% recall progress in extracting small building instances. The implementation code will be released after the paper is published.

GCCINet: Global feature capture and cross-layer information interaction network for building extraction from remote sensing imagery

• Our GCCINet can effectively solve the current building extraction problems. • GCCINet performs state-of-the-art compared with eleven methods in recent studies. • The sufficient comparative experiments and analysis are implemented. • An efficient plug and play CAFE module is proposed. The extraction of buildings from remote sensing images is a challenging task. However, existing methods are insufficiently accurate because of the diverse types of buildings, large-scale variability, and complex backgrounds in remote sensing images. There are many deficiencies of the extraction results, such as small building omission, building internal discontinuity, blurred boundary, and irregular building appearance extraction. To solve these problems, a global feature capture and cross-layer information interaction network (GCCINet) is proposed, in which the continuous atrous convolution feature enhancement module is designed to capture a larger range of multiscale building feature information by using continuous atrous convolution to generate different sizes of receptive fields, thus alleviating the problem of discontinuities and the overall appearance of irregular buildings. The global high-low feature cross-fusion module reduces the loss of local information to enhance the ability to identify small buildings through the effective cross-fusion of high-low features. The cross-layer refined fusion and boundary refinement module adopts a unique fusion method to form information fusion between different layers, obtain multiscale context information, and further refine boundaries, thus improving the capability of boundary extraction. The WHU Building Dataset and Inria Dataset are selected for validation. The results show that GCCINet performs state-of-the-art (SOTA) compared with other existing methods, and the performance of GCCINet is verified in terms of usability, interference resistance, robustness, and ablation study. Furthermore, a plug-and-play CAFE module is designed, which can introduce a few parameters to other models while improving their performance.

MSL-Net: An Efficient Network for Building Extraction from Aerial Imagery

There remains several challenges that are encountered in the task of extracting buildings from aerial imagery using convolutional neural networks (CNNs). First, the tremendous complexity of existing building extraction networks impedes their practical application. In addition, it is arduous for networks to sufficiently utilize the various building features in different images. To address these challenges, we propose an efficient network called MSL-Net that focuses on both multiscale building features and multilevel image features. First, we use depthwise separable convolution (DSC) to significantly reduce the network complexity, and then we embed a group normalization (GN) layer in the inverted residual structure to alleviate network performance degradation. Furthermore, we extract multiscale building features through an atrous spatial pyramid pooling (ASPP) module and apply long skip connections to establish long-distance dependence to fuse features at different levels of the given image. Finally, we add a deformable convolution network layer before the pixel classification step to enhance the feature extraction capability of MSL-Net for buildings with irregular shapes. The experimental results obtained on three publicly available datasets demonstrate that our proposed method achieves state-of-the-art accuracy with a faster inference speed than that of competing approaches. Specifically, the proposed MSL-Net achieves 90.4%, 81.1% and 70.9% intersection over union (IoU) values on the WHU Building Aerial Imagery dataset, Inria Aerial Image Labeling dataset and Massachusetts Buildings dataset, respectively, with an inference speed of 101.4 frames per second (FPS) for an input image of size 3 × 512 × 512 on an NVIDIA RTX 3090 GPU. With an excellent tradeoff between accuracy and speed, our proposed MSL-Net may hold great promise for use in building extraction tasks.

A multiscale building detection method based on boundary preservation for remote sensing images: Taking the Yangbi M6.4 earthquake as an example

Building detection is an important topic in remote sensing image applications. This study proposes a multiscale building detection method based on boundary preservation, to detect building roofs from a large number of high-resolution remote sensing images at a fast speed. A lightweight network extracts a feature map and a feature pyramid network (FPN) aggregates low- and high-level features. A multidimensional attention network (MDA) enhances building features and weakens the complex background information. The method uses four branches to extract buildings: classification, box, direction box, and mask. We manually labeled approximately 870,000 buildings of different types and selected about 300 1 ​km ​× ​1 ​km image plots of different ground objects without buildings, to construct positive and negative sample sets for 27 provinces in China. The accuracy and recall of test results of the proposed method are 12.4% and 3.6% higher than those of Mask R–CNN, respectively, while its accuracy and detection time of segmentation results are 6% and about 30% higher than those of Mask R–CNN, respectively. Pre-disaster buildings were extracted using the proposed method in several key provinces across the country, which were applied to quick assessment in emergency work for Yangbi earthquake. The method was used on post-disaster UAV images as well, achieving 95.61% precision and 91.36% recall of detection results. Experiments show that: the detection method and its results are beneficial to reduce manual interpretation time significantly, and detections on pre- and post-disaster images can be compared to help identification of damaged buildings.

MS-CNN: multiscale recognition of building rooftops from high spatial resolution remote sensing imagery

ABSTRACT The effective recognition and precise positioning of multiscale building rooftop is one of the key scientific problems that have yet to be resolved urgently in the current implementation of high-resolution remote sensing. In recent years, the automatic recognition of high-resolution image targets often employs convolutional neural networks to extract features. However, such traditional methods often ignore multiscale features of geographical objects, while lacking effective multiscale information extraction strategies. By utilizing the feature learning capability of deep neural networks, this study proposes a multiscale convolutional neural network named MS-CNN to recognize building rooftops from high-resolution remote sensing imagery. In addition, this study constructs a pedigree deep learning sample library based on the remote sensing Tupu theory that considers the spectral and geometric characteristics of building rooftops. Able to utilize feature segmentation mechanism and fusion enhancement strategy, MS-CNN enriches the receptive fields obtained by each convolution layer. The proposed network of this study is also compared with the famous Mask R-CNN method, proving the relative advantages of the MS-CNN method with multiscale characteristics. The experimental results show that the precision and recall metrics of the MS-CNN are 4.18% (.8655 vs. .8238) and 5.71% (.8380 vs. .7809) higher than those of the Mask R-CNN, respectively. The proposed method has been deployed in practical engineering projects in Vietnam and Myanmar, etc.

Tuning urban microclimate: A morpho-patch approach for multi-scale building group energy simulation

• A morpho-patch generation approach based on urban street networks. • Urban microclimate estimation with urban building morphological features. • Localized microcliamte computation through tuning cities’ TMY weather. • More reliable urban building energy simulation with high-resoluation microclimates. Building energy simulation is a powerful tool for developing sustainable and low-emission urban built environments. The conventional simulation model relies on the climate conditions of the entire city that are measured by few weather stations. However, due to urban morphologies, natural conditions, and man-made structures, microclimate conditions vary across geographic locations. This results in unrealistic and unreliable simulation outputs and failure to properly support urban sustainability decision-making. To fill this gap, this study proposed a microclimate tuning approach based on the morphological analysis of local buildings and green land. The proposed tuning approach implements a morpho-patch as the analyzing unit to incorporate various morphological conditions for localized microclimate estimation. Extending the urban weather generator model, the inter-building effects are introduced in the urban canopy component computation and a unique microclimate weather conditions for each patch can be computed. To illustrate the proposed model, this study t simulated the energy dynamic of the entire city of Tucson [AZ, USA] using the proposed model. The results suggested that the root mean square temperature differences between using the single weather conditions and the tunned microclimates can be up to 2.49 °C. For one sample patch, this difference in underestimation of cooling energy demand by 6.69 MW•h in June and overestimation of the heating energy demand by 7.85 MW•h in November. Thus, the proposed method provides more accurate and realistic microclimate estimation for large-scale energy dynamic simulation for cities.

ASF-Net: Adaptive Screening Feature Network for Building Footprint Extraction From Remote-Sensing Images

Building footprint extraction plays an important role in many remote-sensing (RS) applications such as urban planning and disaster monitoring. Mainly, the exploitation of contextual information in a fixed receptive field is the focus of previous research, which makes it difficult to generically extract buildings that vary greatly in size and shape, especially when isolated large buildings are surrounded by dense small buildings. To improve this problem, we attempt to teach the network to adjust the receptive field and enhance useful feature information adaptively. In this article, we propose a novel adaptive screening feature network (ASF-Net), which can independently screen and enhance effective feature information from two aspects. On the one hand, we propose a deepened space up-sampling block to screen useful information and help establish boundaries. On the other hand, we propose an Adaptive Information Utilization Block (AIUB) to enlarge the receptive field of feature maps and refine the incomplete building footprint. As a result, the more accurate multiscale building footprint is inferred from the enhanced features. Experimental results on the popular aerial image segmentation datasets show that ASF-Net obtains competitive results [80.2% intersection over union (IoU) on the Inria aerial image labeling dataset and 74.2% IoU on the Massachusetts buildings dataset] in comparison with several state-of-the-art models. The TensorFlow implementation is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/jyx0516/ASF-Net</uri> .

MSF-Net: A Multiscale Supervised Fusion Network for Building Change Detection in High-Resolution Remote Sensing Images

Building change detection is a primary task in the application of remote sensing images, especially in city land resource management and urbanization process assesment. Due to the rich textural features of remote sensing images and the multiscale characteristics of buildings, it is still a huge challenge to effectively filter out irrelevant change information (e.g., roads) and fuse multiscale building features. To date, deep learning-based methods have demonstrated powerful capabilities in this field. To fill these gaps, this study proposes a multiscale supervised fusion network (MSF-Net), which is an attention mechanism-based approach for building change detection using bi-temporal high-resolution satellite imagery. Especially, we built a dual-context fusion module to obtain abundant global context information of buildings and suppressing irrelevant features. We also used channel attention mechanism, selective kernel convolution and multiscale supervision module to fuse multiscale feature of buildings. The ablation experiments verified the availability of these modules. The MSF-Net model has been tested on the LEVIR-CD building change detection dataset. Compared with other state-of-the-art change detection methods, the study showed that our method obtained 0.8866 and 0.8130 in F1-score and Intersection over Union (IOU), respectively. The results indicate that the MSF-Net method has stronger multiscale building feature extraction capability and suppression ability of irrelevant features, which could produce clearer building boundaries and more accurate building change maps.

A coarse-to-fine boundary refinement network for building footprint extraction from remote sensing imagery

Extracting building footprints from remotely sensed imagery has long been a challenging task and is not yet fully solved. Obstructions from nearby shadows or trees, varying shapes of rooftops, omission of small buildings, and varying scale of buildings hinder existing automated models for extracting sharp building boundaries. Different reasons account for these challenges. In convolutional neural network-based methods, the down-sampling operation loses spatial details of the input images; and small buildings are omitted from the high-level features. The sheltering trees and adjacent objects shadowing may cause errors since semantic information cannot be effectively preserved. Moreover, the insufficient use of multi-scale building features causes blurry edges in the predictions for buildings with complex shapes. To address these challenges, we propose a novel coarse-to-fine boundary refinement network (CBR-Net) that accurately extracts building footprints from remote sensing imagery. Unlike the existing semantic segmentation methods that directly generate building predictions at the highest level, we designed a module that progressively refines the building prediction in a coarse-to-fine manner. In this way, the advantages of both the high-level and low-level features can be retained. We also present a novel boundary refinement (BR) module that enhances the ability of the CBR-Net model to perceive and refine building edges. The BR module refines building prediction by perceiving the direction of each pixel in a remotely sensed optical image to the center of the nearest object to which it might belong. The refined results are used as pseudo labels in a self-supervision process that increases model robustness to noisy labels or obstructions. Experimental results on three public building datasets, including the WHU building dataset, the Massachusetts building dataset, and the Inria aerial image dataset, demonstrate the effectiveness of the proposed method. In evaluation tests, CBR-Net outperformed other state-of-the-art algorithms on the three datasets by maintaining both the continuous entities and accurate boundaries of buildings. The source code of the proposed CBR-Net is available at https://github.com/HaonanGuo/CBRNet.

An approach for multi-scale urban building data integration and enrichment through geometric matching and semantic web

ABSTRACT The advent of Web 2.0 has emerged abundant but often unstructured user-generated georeferenced data, such as those from Volunteered Geographic Information (VGI) initiatives. In many cases, these data can be considered as complementary to the authoritative geospatial data. With the increasing availability of multi-source geospatial data, the efforts for geospatial data integration have gained momentum, aiming at gathering maximum information to answer sophisticated questions that cannot be answered using a single data source. Although there are various approaches employed for this purpose with different degrees of success, semantic web methods and tools have not been tested sufficiently in this scope, particularly for multi-scale urban building data integration and enrichment. Attempting to fill this gap, in this study, multi-source and multi-scale urban building data were integrated with a geometric matching method based on the overlapping area, then a geospatial ontology was developed to define multi-scale representations and detailed cardinality relations of the building features. Finally, some features from the geospatial ontology were then linked to popular knowledge bases such as DBpedia and YAGO. For the exploitation on the web, query and visualization processes were demonstrated using sample questions. The semantic web enabled to model complex cardinality of relations between the features from three different building data sets using inferencing and Semantic Web Rule Language (SWRL). The study showed that integrating different geospatial data sets as a knowledge base can facilitate answering sophisticated questions from different users.