Accurately identifying building distribution from remote sensing images with complex background information is challenging.The emergence of diffusion models has prompted the innovative idea of employing the reverse de...Accurately identifying building distribution from remote sensing images with complex background information is challenging.The emergence of diffusion models has prompted the innovative idea of employing the reverse denoising process to distill building distribution from these complex backgrounds.Building on this concept,we propose a novel framework,building extraction diffusion model(BEDiff),which meticulously refines the extraction of building footprints from remote sensing images in a stepwise fashion.Our approach begins with the design of booster guidance,a mechanism that extracts structural and semantic features from remote sensing images to serve as priors,thereby providing targeted guidance for the diffusion process.Additionally,we introduce a cross-feature fusion module(CFM)that bridges the semantic gap between different types of features,facilitating the integration of the attributes extracted by booster guidance into the diffusion process more effectively.Our proposed BEDiff marks the first application of diffusion models to the task of building extraction.Empirical evidence from extensive experiments on the Beijing building dataset demonstrates the superior performance of BEDiff,affirming its effectiveness and potential for enhancing the accuracy of building extraction in complex urban landscapes.展开更多
As one of the main geographical elements in urban areas,buildings are closely related to the development of the city.Therefore,how to quickly and accurately extract building information from remote sensing images is o...As one of the main geographical elements in urban areas,buildings are closely related to the development of the city.Therefore,how to quickly and accurately extract building information from remote sensing images is of great significance for urban map updating,urban planning and construction,etc.Extracting building information around power facilities,especially obtaining this information from high-resolution images,has become one of the current hot topics in remote sensing technology research.This study made full use of the characteristics of GF-2 satellite remote sensing images,adopted an object-oriented classification method,combined with multi-scale segmentation technology and CART classification algorithm,and successfully extracted the buildings in the study area.The research results showed that the overall classification accuracy reached 89.5%and the Kappa coefficient was 0.86.Using the object-oriented CART classification algorithm for building extraction could be closer to actual ground objects and had higher accuracy.The extraction of buildings in the city contributed to urban development planning and provided decision support for management.展开更多
Extraction of buildings from LIDAR data has been an active research field in recent years. A scheme for building detection and reconstruction from LIDAR data is presented with an object-oriented method which is based ...Extraction of buildings from LIDAR data has been an active research field in recent years. A scheme for building detection and reconstruction from LIDAR data is presented with an object-oriented method which is based on the buildings’ semantic rules. Two key steps are discussed: how to group the discrete LIDAR points into single objects and how to establish the buildings’ semantic rules. In the end, the buildings are reconstructed in 3D form and three common parametric building models (flat, gabled, hipped) are implemented.展开更多
Segmentation and edge regulation are studied deeply to extract buildings fromDSM data produced in this paper. Building segmentation is the first step to extract buildings, anda new segmentation method-adaptive iterati...Segmentation and edge regulation are studied deeply to extract buildings fromDSM data produced in this paper. Building segmentation is the first step to extract buildings, anda new segmentation method-adaptive iterative segmentation considering rati-o mean square-is proposedto extract the contour of buildings effectively. A sub-image (such as 50X50 pixels) of the image isprocessed in sequence, the average gray level and its ratio mean square are calculated first, thenthreshold of the sub-image is selected by using iterative threshold segmentation. The current pixelis segmented according to the threshold, the average gray level and the ratio mean square of thesub-image. The edge points of the building are grouped according to the azimuth of neighbor points,and then the optimal azimuth of the points that belong to the same group can be calculated by usingline interpolation.展开更多
The paper presents a general paradigm of semiautomatic building extraction from aerial stereo image pair.In the semiautomatic extraction system,the building model is defined by selected roof type through human-machine...The paper presents a general paradigm of semiautomatic building extraction from aerial stereo image pair.In the semiautomatic extraction system,the building model is defined by selected roof type through human-machine interface and input the approximation of area where the extracted building exists.Then under the knowledge of the roof type,low-level and mid-level processing including edge detection,straight line segments extraction and line segments grouping are used to establish the initial geometrical model of the roof-top.However,the initial geometrical model is not so accurate in geometry.To attain accurate results,a general least squares adjustment integrating the linear templates matching model with geometrical constraints in object-space is applied to refine the initial geometrical model.The adjustment model integrating the straight edge pattern and 3D constraints together is a well-studied optimal and anti-noise method.After gaining proper initial values,this adjustment model can flexibly process extraction of kinds of roof types by changing or assembling the geometrical constraints in object-space.展开更多
Automatic extraction features and buildings in particular from digital images is one of the most complex and challenging task faced by computer vision and photogrammetric communities. Extracted buildings are required ...Automatic extraction features and buildings in particular from digital images is one of the most complex and challenging task faced by computer vision and photogrammetric communities. Extracted buildings are required for varieties of applications including urban planning, creation of GIS databases and development of urban city models for taxation. For decades, extraction of features has been done by photogrammetric methods using stereo plotters and digital work stations. Photogrammetric methods are tedious, manually operated and require well-trained personnel. In recent years, there has been emergence of high-resolution space borne images, which have disclosed a large number of new opportunities for medium and large-scale topographic mapping. In this paper, a semi-automatic method is introduced to extract buildings in planned and informal settlements in urban areas from high resolution imagery. The proposed method uses modified snakes model and radial casting algorithm to initialize snakes contours and refinement of building outlines. The extraction rate is 91 percent as demonstrated by examples over selected test areas. The potential, limitations and future work is discussed.展开更多
In the management of land resources and the protection of cultivated land,the law enforcement of land satellite images is often used as one of the main means.In recent years,the policies and regulations of the law enf...In the management of land resources and the protection of cultivated land,the law enforcement of land satellite images is often used as one of the main means.In recent years,the policies and regulations of the law enforcement of land satellite images have become more and more strict and been adjusted increasingly frequently,playing a decisive role in preventing excessive non-agricultural and non-food urbanization.In the process of the law enforcement,the extraction of suspected illegal buildings is the most important and time-consuming content.Compared with the traditional deep learning model,fully convolutional networks(FCN)has a great advantage in remote sensing image processing because its input images are not limited by size,and both convolution and deconvolution are independent of the overall size of images.In this paper,an intelligent extraction model of suspected illegal buildings from land satellite images based on deep learning FCN was built.Kaiyuan City,Yunnan Province was taken as an example.The verification results show that the global accuracy of this model was 86.6%in the process of building extraction,and mean intersection over union(mIoU)was 73.6%.This study can provide reference for the extraction of suspected illegal buildings in the law enforcement work of land satellite images,and reduce the tedious manual operation to a certain extent.展开更多
Building outline extraction from segmented point clouds is a critical step of building footprint generation.Existing methods for this task are often based on the convex hull and α-shape algorithm.There are also some ...Building outline extraction from segmented point clouds is a critical step of building footprint generation.Existing methods for this task are often based on the convex hull and α-shape algorithm.There are also some methods using grids and Delaunay triangulation.The common challenge of these methods is the determination of proper parameters.While deep learning-based methods have shown promise in reducing the impact and dependence on parameter selection,their reliance on datasets with ground truth information limits the generalization of these methods.In this study,a novel unsupervised approach,called PH-shape,is proposed to address the aforementioned challenge.The methods of Persistence Homology(PH)and Fourier descriptor are introduced into the task of building outline extraction.The PH from the theory of topological data analysis supports the automatic and adaptive determination of proper buffer radius,thus enabling the parameter-adaptive extraction of building outlines through buffering and“inverse”buffering.The quantitative and qualitative experiment results on two datasets with different point densities demonstrate the effectiveness of the proposed approach in the face of various building types,interior boundaries,and the density variation in the point cloud data of one building.The PH-supported parameter adaptivity helps the proposed approach overcome the challenge of parameter determination and data variations and achieve reliable extraction of building outlines.展开更多
Extracting building contours from aerial images is a fundamental task in remote sensing.Current building extraction methods cannot accurately extract building contour information and have errors in extracting small-sc...Extracting building contours from aerial images is a fundamental task in remote sensing.Current building extraction methods cannot accurately extract building contour information and have errors in extracting small-scale buildings.This paper introduces a novel dense feature iterative(DFI)fusion network,denoted as DFINet,for extracting building contours.The network uses a DFI decoder to fuse semantic information at different scales and learns the building contour knowledge,producing the last features through iterative fusion.The dense feature fusion(DFF)module combines features at multiple scales.We employ the contour reconstruction(CR)module to access the final predictions.Extensive experiments validate the effectiveness of the DFINet on two different remote sensing datasets,INRIA aerial image dataset and Wuhan University(WHU)building dataset.On the INRIA aerial image dataset,our method achieves the highest intersection over union(IoU),overall accuracy(OA)and F 1 scores compared to other state-of-the-art methods.展开更多
An advanced edge-based method of feature detection and extraction is developed for object description in digital images. It is useful for the comparison of different images of the same scene in aerial imagery, for des...An advanced edge-based method of feature detection and extraction is developed for object description in digital images. It is useful for the comparison of different images of the same scene in aerial imagery, for describing and recognizing categories, for automatic building extraction and for finding the mutual regions in image matching. The method includes directional filtering and searching for straight edge segments in every direction and scale, taking into account edge gradient signs. Line segments are ordered with respect to their orientation and average gradients in the region in question. These segments are used for the construction of an object descriptor. A hierarchical set of feature descriptors is developed, taking into consideration the proposed straight line segment detector. Comparative performance is evaluated on the noisy model and in real aerial and satellite imagery.展开更多
Predicting resource consumption in the built environment and its associated environmental consequences is one of the core challenges facing policy-makers and planners seeking to increase the sustainability of urban ar...Predicting resource consumption in the built environment and its associated environmental consequences is one of the core challenges facing policy-makers and planners seeking to increase the sustainability of urban areas.The study of land-use change has many implications for infrastructure design,resource allocation,and urban metabolism simulation.While most urban models focus on horizontal growth patterns,few investigate the impacts of vertical characteristics of urbanscapes in predicting land-use changes.In this paper,Building-form variables are introduced as a new determinant factor for investigating effects of vertical characteristics of an urbanscape in predicting land-use change.This work outlines an automated method for generating building-form variables from Light Detection and Ranging(LIDAR)data by using Density-Based Spatial Clustering and normal equations.This paper presents a Land-Use Model that uses Remote Sensing,GIS,and Artificial Neural Networks(ANNs)to predict urban growth patterns within the IUMAT framework(Integrated Urban Metabolism Analysis Tool),which is an analytical platform for quantifying the overall sustainability in the urbanscape.The town of Amherst in Western Massachusetts(for the period of 1971-2005)is used as a case study for testing the model.By isolating the weights of each explanatory variable in models,this study highlights the influence of building geometry on future development scenarios.展开更多
基金supported by the National Natural Science Foundation of China(Nos.61906168,62202429 and 62272267)the Zhejiang Provincial Natural Science Foundation of China(No.LY23F020023)the Construction of Hubei Provincial Key Laboratory for Intelligent Visual Monitoring of Hydropower Projects(No.2022SDSJ01)。
文摘Accurately identifying building distribution from remote sensing images with complex background information is challenging.The emergence of diffusion models has prompted the innovative idea of employing the reverse denoising process to distill building distribution from these complex backgrounds.Building on this concept,we propose a novel framework,building extraction diffusion model(BEDiff),which meticulously refines the extraction of building footprints from remote sensing images in a stepwise fashion.Our approach begins with the design of booster guidance,a mechanism that extracts structural and semantic features from remote sensing images to serve as priors,thereby providing targeted guidance for the diffusion process.Additionally,we introduce a cross-feature fusion module(CFM)that bridges the semantic gap between different types of features,facilitating the integration of the attributes extracted by booster guidance into the diffusion process more effectively.Our proposed BEDiff marks the first application of diffusion models to the task of building extraction.Empirical evidence from extensive experiments on the Beijing building dataset demonstrates the superior performance of BEDiff,affirming its effectiveness and potential for enhancing the accuracy of building extraction in complex urban landscapes.
基金Research on Algorithm Model for Monitoring and Evaluating Typical Disaster Situations of Electric Power Equipment Based on Remote Sensing Imaging Technology of Heaven and Earth,South Grid Guangxi Power Grid Company Science and Technology Project(GXKJXM20222160).
文摘As one of the main geographical elements in urban areas,buildings are closely related to the development of the city.Therefore,how to quickly and accurately extract building information from remote sensing images is of great significance for urban map updating,urban planning and construction,etc.Extracting building information around power facilities,especially obtaining this information from high-resolution images,has become one of the current hot topics in remote sensing technology research.This study made full use of the characteristics of GF-2 satellite remote sensing images,adopted an object-oriented classification method,combined with multi-scale segmentation technology and CART classification algorithm,and successfully extracted the buildings in the study area.The research results showed that the overall classification accuracy reached 89.5%and the Kappa coefficient was 0.86.Using the object-oriented CART classification algorithm for building extraction could be closer to actual ground objects and had higher accuracy.The extraction of buildings in the city contributed to urban development planning and provided decision support for management.
基金Supported by the Key Laboratory of Geo Informatics of State Bureau of Surveying and Mapping.
文摘Extraction of buildings from LIDAR data has been an active research field in recent years. A scheme for building detection and reconstruction from LIDAR data is presented with an object-oriented method which is based on the buildings’ semantic rules. Two key steps are discussed: how to group the discrete LIDAR points into single objects and how to establish the buildings’ semantic rules. In the end, the buildings are reconstructed in 3D form and three common parametric building models (flat, gabled, hipped) are implemented.
基金theNationalNaturalScienceFoundationofChina (No 40 2 0 1 0 35)
文摘Segmentation and edge regulation are studied deeply to extract buildings fromDSM data produced in this paper. Building segmentation is the first step to extract buildings, anda new segmentation method-adaptive iterative segmentation considering rati-o mean square-is proposedto extract the contour of buildings effectively. A sub-image (such as 50X50 pixels) of the image isprocessed in sequence, the average gray level and its ratio mean square are calculated first, thenthreshold of the sub-image is selected by using iterative threshold segmentation. The current pixelis segmented according to the threshold, the average gray level and the ratio mean square of thesub-image. The edge points of the building are grouped according to the azimuth of neighbor points,and then the optimal azimuth of the points that belong to the same group can be calculated by usingline interpolation.
文摘The paper presents a general paradigm of semiautomatic building extraction from aerial stereo image pair.In the semiautomatic extraction system,the building model is defined by selected roof type through human-machine interface and input the approximation of area where the extracted building exists.Then under the knowledge of the roof type,low-level and mid-level processing including edge detection,straight line segments extraction and line segments grouping are used to establish the initial geometrical model of the roof-top.However,the initial geometrical model is not so accurate in geometry.To attain accurate results,a general least squares adjustment integrating the linear templates matching model with geometrical constraints in object-space is applied to refine the initial geometrical model.The adjustment model integrating the straight edge pattern and 3D constraints together is a well-studied optimal and anti-noise method.After gaining proper initial values,this adjustment model can flexibly process extraction of kinds of roof types by changing or assembling the geometrical constraints in object-space.
文摘Automatic extraction features and buildings in particular from digital images is one of the most complex and challenging task faced by computer vision and photogrammetric communities. Extracted buildings are required for varieties of applications including urban planning, creation of GIS databases and development of urban city models for taxation. For decades, extraction of features has been done by photogrammetric methods using stereo plotters and digital work stations. Photogrammetric methods are tedious, manually operated and require well-trained personnel. In recent years, there has been emergence of high-resolution space borne images, which have disclosed a large number of new opportunities for medium and large-scale topographic mapping. In this paper, a semi-automatic method is introduced to extract buildings in planned and informal settlements in urban areas from high resolution imagery. The proposed method uses modified snakes model and radial casting algorithm to initialize snakes contours and refinement of building outlines. The extraction rate is 91 percent as demonstrated by examples over selected test areas. The potential, limitations and future work is discussed.
文摘In the management of land resources and the protection of cultivated land,the law enforcement of land satellite images is often used as one of the main means.In recent years,the policies and regulations of the law enforcement of land satellite images have become more and more strict and been adjusted increasingly frequently,playing a decisive role in preventing excessive non-agricultural and non-food urbanization.In the process of the law enforcement,the extraction of suspected illegal buildings is the most important and time-consuming content.Compared with the traditional deep learning model,fully convolutional networks(FCN)has a great advantage in remote sensing image processing because its input images are not limited by size,and both convolution and deconvolution are independent of the overall size of images.In this paper,an intelligent extraction model of suspected illegal buildings from land satellite images based on deep learning FCN was built.Kaiyuan City,Yunnan Province was taken as an example.The verification results show that the global accuracy of this model was 86.6%in the process of building extraction,and mean intersection over union(mIoU)was 73.6%.This study can provide reference for the extraction of suspected illegal buildings in the law enforcement work of land satellite images,and reduce the tedious manual operation to a certain extent.
基金supported by NTNU Digital project[grant number 81771593].
文摘Building outline extraction from segmented point clouds is a critical step of building footprint generation.Existing methods for this task are often based on the convex hull and α-shape algorithm.There are also some methods using grids and Delaunay triangulation.The common challenge of these methods is the determination of proper parameters.While deep learning-based methods have shown promise in reducing the impact and dependence on parameter selection,their reliance on datasets with ground truth information limits the generalization of these methods.In this study,a novel unsupervised approach,called PH-shape,is proposed to address the aforementioned challenge.The methods of Persistence Homology(PH)and Fourier descriptor are introduced into the task of building outline extraction.The PH from the theory of topological data analysis supports the automatic and adaptive determination of proper buffer radius,thus enabling the parameter-adaptive extraction of building outlines through buffering and“inverse”buffering.The quantitative and qualitative experiment results on two datasets with different point densities demonstrate the effectiveness of the proposed approach in the face of various building types,interior boundaries,and the density variation in the point cloud data of one building.The PH-supported parameter adaptivity helps the proposed approach overcome the challenge of parameter determination and data variations and achieve reliable extraction of building outlines.
基金National Natural Science Foundation of China(No.61903078)Fundamental Research Funds for the Central Universities,China(No.2232021A-10)+1 种基金Shanghai Sailing Program,China(No.22YF1401300)Natural Science Foundation of Shanghai,China(No.20ZR1400400)。
文摘Extracting building contours from aerial images is a fundamental task in remote sensing.Current building extraction methods cannot accurately extract building contour information and have errors in extracting small-scale buildings.This paper introduces a novel dense feature iterative(DFI)fusion network,denoted as DFINet,for extracting building contours.The network uses a DFI decoder to fuse semantic information at different scales and learns the building contour knowledge,producing the last features through iterative fusion.The dense feature fusion(DFF)module combines features at multiple scales.We employ the contour reconstruction(CR)module to access the final predictions.Extensive experiments validate the effectiveness of the DFINet on two different remote sensing datasets,INRIA aerial image dataset and Wuhan University(WHU)building dataset.On the INRIA aerial image dataset,our method achieves the highest intersection over union(IoU),overall accuracy(OA)and F 1 scores compared to other state-of-the-art methods.
文摘An advanced edge-based method of feature detection and extraction is developed for object description in digital images. It is useful for the comparison of different images of the same scene in aerial imagery, for describing and recognizing categories, for automatic building extraction and for finding the mutual regions in image matching. The method includes directional filtering and searching for straight edge segments in every direction and scale, taking into account edge gradient signs. Line segments are ordered with respect to their orientation and average gradients in the region in question. These segments are used for the construction of an object descriptor. A hierarchical set of feature descriptors is developed, taking into consideration the proposed straight line segment detector. Comparative performance is evaluated on the noisy model and in real aerial and satellite imagery.
文摘Predicting resource consumption in the built environment and its associated environmental consequences is one of the core challenges facing policy-makers and planners seeking to increase the sustainability of urban areas.The study of land-use change has many implications for infrastructure design,resource allocation,and urban metabolism simulation.While most urban models focus on horizontal growth patterns,few investigate the impacts of vertical characteristics of urbanscapes in predicting land-use changes.In this paper,Building-form variables are introduced as a new determinant factor for investigating effects of vertical characteristics of an urbanscape in predicting land-use change.This work outlines an automated method for generating building-form variables from Light Detection and Ranging(LIDAR)data by using Density-Based Spatial Clustering and normal equations.This paper presents a Land-Use Model that uses Remote Sensing,GIS,and Artificial Neural Networks(ANNs)to predict urban growth patterns within the IUMAT framework(Integrated Urban Metabolism Analysis Tool),which is an analytical platform for quantifying the overall sustainability in the urbanscape.The town of Amherst in Western Massachusetts(for the period of 1971-2005)is used as a case study for testing the model.By isolating the weights of each explanatory variable in models,this study highlights the influence of building geometry on future development scenarios.
