Temporal city modeling using street level imagery

Satoh, Ken; Tetsuka, Daiki; Okatani, Takayuki

doi:10.1016/j.cviu.2017.01.012

Cited by 5 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Satellite images [129,214,233] Damage assessment a. Satellite images (caused by tsunami [190,234], particular incident [156], flood [235], or earthquake [19]) b. Aerial images (caused by tsunami [92]) c. SAR images (caused by fires [104], or earthquake [110]) d. Street view images (caused by tsunami [119]) e. Street view images and GIS map (caused by tsunami [236])…”

Section: Applications Data Typesmentioning

confidence: 99%

See 1 more Smart Citation

Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

et al. 2020

View full text Add to dashboard Cite

Change detection based on remote sensing (RS) data is an important method of detecting changes on the Earth’s surface and has a wide range of applications in urban planning, environmental monitoring, agriculture investigation, disaster assessment, and map revision. In recent years, integrated artificial intelligence (AI) technology has become a research focus in developing new change detection methods. Although some researchers claim that AI-based change detection approaches outperform traditional change detection approaches, it is not immediately obvious how and to what extent AI can improve the performance of change detection. This review focuses on the state-of-the-art methods, applications, and challenges of AI for change detection. Specifically, the implementation process of AI-based change detection is first introduced. Then, the data from different sensors used for change detection, including optical RS data, synthetic aperture radar (SAR) data, street view images, and combined heterogeneous data, are presented, and the available open datasets are also listed. The general frameworks of AI-based change detection methods are reviewed and analyzed systematically, and the unsupervised schemes used in AI-based change detection are further analyzed. Subsequently, the commonly used networks in AI for change detection are described. From a practical point of view, the application domains of AI-based change detection methods are classified based on their applicability. Finally, the major challenges and prospects of AI for change detection are discussed and delineated, including (a) heterogeneous big data processing, (b) unsupervised AI, and (c) the reliability of AI. This review will be beneficial for researchers in understanding this field.

show abstract

Section: Applications Data Typesmentioning

confidence: 99%

“…After a natural disaster, AI-based change techniques can help to identify damaged areas using pre-event and post-event data. Existing research mainly includes tsunamis [92,119,190,234,236], floods [235], fires [104], and earthquakes [19,110].…”

Section: Astronomymentioning

confidence: 99%

Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The Manhattan-Modeler retrieves the façade using the height map, which can only be used to repair vertical walls [39]. Other methods involve ground data aimed at façade modeling, which introduces extra data cost [40][41][42][43].…”

Section: Topology Repair Towards the Incomplete Datasetmentioning

confidence: 99%

TopoLAP: Topology Recovery for Building Reconstruction by Deducing the Relationships between Linear and Planar Primitives

et al. 2019

View full text Add to dashboard Cite

Limited by the noise, missing data and varying sampling density of the point clouds, planar primitives are prone to be lost during plane segmentation, leading to topology errors when reconstructing complex building models. In this paper, a pipeline to recover the broken topology of planar primitives (TopoLAP) is proposed to reconstruct level of details 3 (LoD3) models. Firstly, planar primitives are segmented from the incomplete point clouds and feature lines are detected both from point clouds and images. Secondly, the structural contours of each plane segment are reconstructed by subset selection from intersections of these feature lines. Subsequently, missing planes are recovered by plane deduction according to the relationships between linear and planar primitives. Finally, the manifold and watertight polyhedral building models are reconstructed based on the optimized PolyFit framework. Experimental results demonstrate that the proposed pipeline can handle partial incomplete point clouds and reconstruct the LoD3 models of complex buildings automatically. A comparative analysis indicates that the proposed method performs better to preserve sharp edges and achieves a higher fitness and correction rate than rooftop-based modeling and the original PolyFit algorithm.

show abstract