Surface reconstruction from multiple aerial images in dense urban areas

2003 ◽  
Author(s):  
M. Fradkin ◽  
M. Roux ◽  
H. Maitre ◽  
U.M. Leloglu
Keyword(s):  
Surface Reconstruction ◽  
Urban Areas ◽  
Aerial Images

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 Thus far, there has been limited research on the use of the outdated building map (the building map before the update, referred to herein as the old-map) to increase the accuracy of building change detection.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 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.

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 High-quality observation of surface imperviousness for urban runoff modelling using UAV imagery

2015 ◽  
Vol 19 (10) ◽  
pp. 4215-4228 ◽  
Author(s):  
P. Tokarczyk ◽  
J. P. Leitao ◽  
J. Rieckermann ◽  
K. Schindler ◽  
F. Blumensaat
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Surface Runoff ◽  
Urban Areas ◽  
Image Data ◽  
Aerial Images ◽  
Urban Drainage ◽  
High Quality ◽  
Modern Classification

Abstract. Modelling rainfall–runoff in urban areas is increasingly applied to support flood risk assessment, particularly against the background of a changing climate and an increasing urbanization. These models typically rely on high-quality data for rainfall and surface characteristics of the catchment area as model input. While recent research in urban drainage has been focusing on providing spatially detailed rainfall data, the technological advances in remote sensing that ease the acquisition of detailed land-use information are less prominently discussed within the community. The relevance of such methods increases as in many parts of the globe, accurate land-use information is generally lacking, because detailed image data are often unavailable. Modern unmanned aerial vehicles (UAVs) allow one to acquire high-resolution images on a local level at comparably lower cost, performing on-demand repetitive measurements and obtaining a degree of detail tailored for the purpose of the study. In this study, we investigate for the first time the possibility of deriving high-resolution imperviousness maps for urban areas from UAV imagery and of using this information as input for urban drainage models. To do so, an automatic processing pipeline with a modern classification method is proposed and evaluated in a state-of-the-art urban drainage modelling exercise. In a real-life case study (Lucerne, Switzerland), we compare imperviousness maps generated using a fixed-wing consumer micro-UAV and standard large-format aerial images acquired by the Swiss national mapping agency (swisstopo). After assessing their overall accuracy, we perform an end-to-end comparison, in which they are used as an input for an urban drainage model. Then, we evaluate the influence which different image data sources and their processing methods have on hydrological and hydraulic model performance. We analyse the surface runoff of the 307 individual subcatchments regarding relevant attributes, such as peak runoff and runoff volume. Finally, we evaluate the model's channel flow prediction performance through a cross-comparison with reference flow measured at the catchment outlet. We show that imperviousness maps generated from UAV images processed with modern classification methods achieve an accuracy comparable to standard, off-the-shelf aerial imagery. In the examined case study, we find that the different imperviousness maps only have a limited influence on predicted surface runoff and pipe flows, when traditional workflows are used. We expect that they will have a substantial influence when more detailed modelling approaches are employed to characterize land use and to predict surface runoff. We conclude that UAV imagery represents a valuable alternative data source for urban drainage model applications due to the possibility of flexibly acquiring up-to-date aerial images at a quality compared with off-the-shelf image products and a competitive price at the same time. We believe that in the future, urban drainage models representing a higher degree of spatial detail will fully benefit from the strengths of UAV imagery.

scholarly journals A COMPARISON TEST OF FEATURE EXTRACTION FROM AERIAL PHOTOGRAPHY

2012 ◽  
Vol 30 (4) ◽  
pp. 112-116
Author(s):  
Birutė Ruzgienė
Keyword(s):  
Feature Extraction ◽  
Aerial Photography ◽  
Urban Areas ◽  
Data Accuracy ◽  
Aerial Images ◽  
Aerial Photographs ◽  
Accurate Data ◽  
Building Models ◽  
Photogrammetric Method ◽  
Automatic Systems

All features visible in the aerial photographs can be collected by traditional photogrammetric methods; however, such techniques require high operator skills and are very time-consuming. The decision which photogrammetric method uses in mapping is primarily economic, also workload, project deadline requirements and accurate data have to be considered. Up-to-date developed automatic or semi-automatic systems are highly effective for 3D features extraction in urban areas. The investigation objective is the comparison of analytical and digital semi-automatic photogrammetric mapping methods for 3D building models extraction from aerial images analysing in time-consuming and in collected data accuracy consideration.

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