City-Scale Change Detection in Cadastral 3D Models Using Images

Taneja, Aparna; Ballan, Luca; Pollefeys, Marc

doi:10.1109/cvpr.2013.22

Cited by 69 publications

(56 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several methods were proposed with different input data: satellite images (Champion et al, 2010), aerial images (Zhu et al, 2009), aerial lidar (Rutzinger et al, 2010). Other approaches use terrestrial data like panoramical images (Taneja et al, 2013) or terrestrial laser scans (Kang and Lu, 2010). In any case, environmental changes (weather, illumination...) negatively impact the decisions.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Quality evaluation of 3D city building Models with automatic error diagnosis

Michelin

Tierny²,

Tupin³

et al. 2013

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Automatic building modelling allows a cost effective access to 3D semantic information of cities. However, even state-of-the-art algorithms have intrinsic limits and many errors exist in 3D reconstructions, requiring expensive manual corrections. A new approach is proposed in this paper for the automatic diagnosis of 3D building databases in urban areas. A novel error taxonomy which allows a subsequent high-level diagnosis is first proposed. Then, relevant raster and vector features are extracted from very high resolution multi-view images and Digital Surface Models so as that to retrieve such errors. In a supervised way, a set of functions is presented in order to take high-level decisions from these low-level features. Experiments on 355 buildings in an European dense city center with 10 cm airborne images demonstrate the high accuracy on error detection and show promising results.

show abstract

Section: Related Workmentioning

confidence: 99%

“…• 3D change detection: if a DB is correct at the time t1, the AD computation with more recent data acquired at time t1 + may provide a knowledge of areas requiring updates (Taneja et al, 2013). …”

Section: Introductionmentioning

confidence: 99%

Quality evaluation of 3D city building Models with automatic error diagnosis

Michelin

Tierny²,

Tupin³

et al. 2013

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…The most closely related to our study is that of Taneja et al [12]. It considers the same problem of estimating the building-level changes by using images and a map.…”

Section: Related Workmentioning

confidence: 99%

Detecting Building-Level Changes of a City Using Street Images and a 2D City Map

Tetsuka

Okatani

2015

2015 IEEE Winter Conference on Applications of Computer Vision

View full text Add to dashboard Cite

This paper presents a method for detecting city-scale changes of a city from its street images and a 2D map. Using SfM to reconstruct point cloud of the structures of the city, the method estimates the existence of each building by matching the point cloud with the 3D building structures recovered from the map. There are multiple difficulties, such as inaccuracy of the recovered building structures, large differences in observation and thus in point cloud size of individual buildings, and mutual dependency of building existences due to potential occlusions. To solve these, we develop a model of how point cloud is generated in the sequential processes of SfM, an observation model of a building wall, and a greedy iterative approach to cope with the mutual dependency. We experimentally apply the method to the cities damaged by the tsunami that struck Japan in 2011. The results show the effectiveness of the method.

show abstract

“…In particular, change detection of man-made objects is still an emerging challenge due to the significant importance for various engineering and environmental applications [18,5,10,25,3]. Apart from National and local government applications like the update of cadastral and other GIS databases, companies like Google and Microsoft are seeking to include extensively up-to-date 2D and 3D urban models in their products (e.g., Microsoft Virtual Earth and Google Earth).…”

Section: Introductionmentioning

confidence: 99%

Simultaneous registration and change detection in multitemporal, very high resolution remote sensing data

Vakalopoulou

Kαράτζαλος

Komodakis

et al. 2015

2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

View full text Add to dashboard Cite

In order to exploit the currently continuous streams of massive, multi-temporal, high-resolution remote sensing datasets there is an emerging need to address efficiently the image registration and change detection challenges. To this end, in this paper we propose a modular, scalable, metric free single shot change detection/registration method. The approach exploits a decomposed interconnected graphical model formulation where registration similarity constraints are relaxed in the presence of change detection. The deformation space is discretized, while efficient linear programming and duality principles are used to optimize a joint solution space where local consistency is imposed on the deformation and the detection space. Promising results on large scale experiments demonstrate the extreme potentials of our method.

show abstract

City-Scale Change Detection in Cadastral 3D Models Using Images

Cited by 69 publications

References 13 publications

Quality evaluation of 3D city building Models with automatic error diagnosis

Quality evaluation of 3D city building Models with automatic error diagnosis

Detecting Building-Level Changes of a City Using Street Images and a 2D City Map

Simultaneous registration and change detection in multitemporal, very high resolution remote sensing data

Contact Info

Product

Resources

About