Building Detection from Multiple Aerial Images in Dense Urban Areas

2001 ◽  
Vol 82 (3) ◽  
pp. 181-207 ◽  
Author(s):  
M. Fradkin ◽  
H. Maı̂tre ◽  
M. Roux
Keyword(s):  
Urban Areas ◽  
Aerial Images ◽  
Building Detection

scholarly journals Urban areas analysis using satellite image segmentation and deep neural network

2019 ◽  
Vol 135 ◽  
pp. 01064
Author(s):  
Vladimir Khryaschev ◽  
Leonid Ivanovsky
Keyword(s):  
Neural Networks ◽  
High Resolution ◽  
Convolutional Neural Networks ◽  
Urban Areas ◽  
High Performance ◽  
Satellite Images ◽  
Satellite Image ◽  
Aerial Images ◽  
Building Detection ◽  
High Resolution Satellite Images

The goal of our research was to develop methods based on convolutional neural networks for automatically extracting the locations of buildings from high-resolution aerial images. To analyze the quality of developed deep learning algorithms, there was used Sorensen-Dice coefficient of similarity which compares results of algorithms with real masks. These masks were generated automatically from json files and sliced on smaller parts together with respective aerial photos before the training of developed convolutional neural networks. This approach allows us to cope with the problem of segmentation for high-resolution satellite images. All in all we show how deep neural networks implemented and launched on modern GPUs of high-performance supercomputer NVIDIA DGX-1 can be used to efficiently learn and detect needed objects. The problem of building detection on satellite images can be put into practice for urban planning, building control of some municipal objects, search of the best locations for future outlets etc.

scholarly journals 3D RECONSTRUCTION OF BUILDINGS WITH GABLED AND HIPPED STRUCTURES USING LIDAR DATA

2014 ◽  
Vol XL-2/W3 ◽  
pp. 47-52
Author(s):  
H. Amini ◽  
P. Pahlavani ◽  
R. Karimi
Keyword(s):  
Urban Areas ◽  
3D Model ◽  
Remote Sensing Data ◽  
Training Data ◽  
Aerial Images ◽  
Lidar Data ◽  
Building Detection ◽  
3D Model Reconstruction ◽  
Accuracy And Precision ◽  
Intersection Line

Buildings are the most important objects in urban areas. Thus, building detection using photogrammetry and remote sensing data as well as 3D model of buildings are very useful for many applications such as mobile navigation, tourism, and disaster management. In this paper, an approach has been proposed for detecting buildings by LiDAR data and aerial images, as well as reconstructing 3D model of buildings. In this regard, firstly, building detection carried out by utilizing a Supper Vector Machine (SVM) as a supervise method. The supervise methods need training data that could be collected from some features. Hence, LiDAR data and aerial images were utilized to produce some features. The features were selected by considering their abilities for separating buildings from other objects. The evaluation results of building detection showed high accuracy and precision of the utilized approach. The detected buildings were labeled in order to reconstruct buildings, individually. Then the planes of each building were separated and adjacent planes were recognized to reduce the calculation volume and to increase the accuracy. Subsequently, the bottom planes of each building were detected in order to compute the corners of hipped roofs using intersection of three adjacent planes. Also, the corners of gabled roofs were computed by both calculating the intersection line of the adjacent planes and finding the intersection between the planes intersection line and their detected parcel. Finally, the coordinates of some nodes in building floor were computed and 3D model reconstruction was carried out. In order to evaluate the proposed method, 3D model of some buildings with different complexity level were generated. The evaluation results showed that the proposed method has reached credible performance.

Urban Thematic Mapping by Integrating LiDAR Point Cloud with Colour Imagery

GEOMATICA ◽  
2011 ◽  
Vol 65 (4) ◽  
pp. 375-385 ◽  
Author(s):  
Haiyan Guan ◽  
Jonathan Li ◽  
Michael A. Chapman
Keyword(s):  
Urban Areas ◽  
Point Cloud ◽  
Local Variation ◽  
Airborne Lidar ◽  
Aerial Imagery ◽  
Building Detection ◽  
Thematic Mapping ◽  
Airborne Lidar Data ◽  
Spectral Channels ◽  
Height Data

This paper presents an effective approach to integrating airborne lidar data and colour imagery acquired simultaneously for urban mapping. Texture and height information extracted from lidar point cloud is integrated with spectral channels of aerial imagery into an image segmentation process. Then, the segmented polygons are integrated with the extracted geometric features (height information between first- and lastreturn, eigenvalue-based local variation and filtered height data) and spectral features (line segments) into a supervised classifier. The results for two different urban areas in Toronto, Canada, demonstrated that a satisfactory overall accuracy of 84.96% and Kappa of 0.76 were achieved in Scene I, while a building detection rate of 92.11%, comission error of 2.10% and omission error of 9.25% were obtained in Scene II.

scholarly journals BUILDING CHANGE DETECTION FROM BITEMPORAL AERIAL IMAGES USING DEEP LEARNING

2020 ◽  
Vol V-2-2020 ◽  
pp. 565-571
Author(s):  
S. Su ◽  
T. Nawata ◽  
T. Fuse
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Urban Areas ◽  
Network Architecture ◽  
Aerial Images ◽  
Detection Accuracy ◽  
Detection Model ◽  
Wide Range ◽  
Surface Models ◽  
Data Source

Abstract. Automatic building change detection has become a topical issue owing to its wide range of applications, such as updating building maps. However, accurate building change detection remains challenging, particularly in urban areas. Thus far, there has been limited research on the use of the outdated building map (the building map before the update, referred to herein as the old-map) to increase the accuracy of building change detection. This paper presents a novel deep-learning-based method for building change detection using bitemporal aerial images containing RGB bands, bitemporal digital surface models (DSMs), and an old-map. The aerial images have two types of spatial resolutions, 12.5 cm or 16 cm, and the cell size of the DSMs is 50 cm × 50 cm. The bitemporal aerial images, the height variations calculated using the differences between the bitemporal DSMs, and the old-map were fed into a network architecture to build an automatic building change detection model. The performance of the model was quantitatively and qualitatively evaluated for an urban area that covered approximately 10 km2 and contained over 21,000 buildings. The results indicate that it can detect the building changes with optimum accuracy as compared to other methods that use inputs such as i) bitemporal aerial images only, ii) bitemporal aerial images and bitemporal DSMs, and iii) bitemporal aerial images and an old-map. The proposed method achieved recall rates of 89.3%, 88.8%, and 99.5% for new, demolished, and other buildings, respectively. The results also demonstrate that the old-map is an effective data source for increasing building change detection accuracy.

scholarly journals AN IMPROVED SNAKE MODEL FOR REFINEMENT OF LIDAR-DERIVED BUILDING ROOF CONTOURS USING AERIAL IMAGES

2016 ◽  
Vol XLI-B3 ◽  
pp. 583-589
Author(s):  
Qi Chen ◽  
Shugen Wang ◽  
Xiuguo Liu
Keyword(s):  
Urban Areas ◽  
Quality Index ◽  
Effective Means ◽  
Urban Air Pollution ◽  
High Elevation ◽  
Aerial Images ◽  
Snake Model ◽  
Building Roof ◽  
Searching Strategy ◽  
High Resolution Imagery

Building roof contours are considered as very important geometric data, which have been widely applied in many fields, including but not limited to urban planning, land investigation, change detection and military reconnaissance. Currently, the demand on building contours at a finer scale (especially in urban areas) has been raised in a growing number of studies such as urban environment quality assessment, urban sprawl monitoring and urban air pollution modelling. LiDAR is known as an effective means of acquiring 3D roof points with high elevation accuracy. However, the precision of the building contour obtained from LiDAR data is restricted by its relatively low scanning resolution. With the use of the texture information from high-resolution imagery, the precision can be improved. In this study, an improved snake model is proposed to refine the initial building contours extracted from LiDAR. First, an improved snake model is constructed with the constraints of the deviation angle, image gradient, and area. Then, the nodes of the contour are moved in a certain range to find the best optimized result using greedy algorithm. Considering both precision and efficiency, the candidate shift positions of the contour nodes are constrained, and the searching strategy for the candidate nodes is explicitly designed. The experiments on three datasets indicate that the proposed method for building contour refinement is effective and feasible. The average quality index is improved from 91.66% to 93.34%. The statistics of the evaluation results for every single building demonstrated that 77.0% of the total number of contours is updated with higher quality index.

scholarly journals AUTOMATIC COREGISTRATION FOR MULTIVIEW SAR IMAGES IN URBAN AREAS

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 651-654 ◽  
Author(s):  
Y. Xiang ◽  
W. Kang ◽  
F. Wang ◽  
H. You
Keyword(s):  
High Resolution ◽  
Urban Areas ◽  
Structural Information ◽  
Transformation Model ◽  
Reference Image ◽  
Building Detection ◽  
Phase Congruency ◽  
Sar Images ◽  
Local Variance ◽  
Complex Structural

Due to the high resolution property and the side-looking mechanism of SAR sensors, complex buildings structures make the registration of SAR images in urban areas becomes very hard. In order to solve the problem, an automatic and robust coregistration approach for multiview high resolution SAR images is proposed in the paper, which consists of three main modules. First, both the reference image and the sensed image are segmented into two parts, urban areas and nonurban areas. Urban areas caused by double or multiple scattering in a SAR image have a tendency to show higher local mean and local variance values compared with general homogeneous regions due to the complex structural information. Based on this criterion, building areas are extracted. After obtaining the target regions, L-shape structures are detected using the SAR phase congruency model and Hough transform. The double bounce scatterings formed by wall and ground are shown as strong L- or T-shapes, which are usually taken as the most reliable indicator for building detection. According to the assumption that buildings are rectangular and flat models, planimetric buildings are delineated using the L-shapes, then the reconstructed target areas are obtained. For the orignal areas and the reconstructed target areas, the SAR-SIFT matching algorithm is implemented. Finally, correct corresponding points are extracted by the fast sample consensus (FSC) and the transformation model is also derived. The experimental results on a pair of multiview TerraSAR images with 1-m resolution show that the proposed approach gives a robust and precise registration performance, compared with the orignal SAR-SIFT method.

scholarly journals Consequences of Anthropic Actions in Cullera Bay (Spain)

2020 ◽  
Vol 8 (4) ◽  
pp. 240
Author(s):  
José Ignacio Pagán ◽  
Isabel López ◽  
Luis Bañón ◽  
Luis Aragonés
Keyword(s):  
Urban Areas ◽  
Anthropogenic Activities ◽  
Aerial Images ◽  
Geographical Information ◽  
General Increase ◽  
Mountain Areas ◽  
Shoreline Evolution ◽  
Sediment Size ◽  
Human Interventions ◽  
A Current

Urbanization and anthropogenic activities have generated significant imbalances in coastal areas. This study analysed the shoreline evolution of the Bay of Cullera (Spain), characterized by strong urban and tourist pressure and with important human interventions during the last century. The evolution of the shoreline was analysed using 60 years of aerial images since the 1950s of the seabed, the maritime climate and the distribution of sediment, as well as anthropogenic actions, such as urban development or the channelling of the Júcar River through the integration of information in a geographical information system (GIS). The results showed: (i) Changes in land-use, in which the substitution of the crop and mountain areas by urban areas was mainly observed. (ii) A general increase in the beach area, although there were important periods of erosion in some points due to anthropic actions. (iii) A significant decrease in the median sediment size in the whole bay since 1987, with a current D50 of 0.125–0.180 mm. The analysis carried out has made it possible to identify trends in coastal accumulation and regression in the different sections of the sector, as well as to demonstrate the usefulness and advantages of GIS.

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