scholarly journals Usability of aerial video footage for 3-D scene reconstruction and structural damage assessment

2018 ◽  
Vol 18 (6) ◽  
pp. 1583-1598 ◽  
Author(s):  
Johnny Cusicanqui ◽  
Norman Kerle ◽  
Francesco Nex
Keyword(s):  
News Media ◽  
Damage Assessment ◽  
Structural Damage ◽  
Moving Objects ◽  
Point Clouds ◽  
Video Data ◽  
Aerial Imagery ◽  
Video Footage ◽  
Post Disaster ◽  
Aerial Video

Abstract. Remote sensing has evolved into the most efficient approach to assess post-disaster structural damage, in extensively affected areas through the use of spaceborne data. For smaller, and in particular, complex urban disaster scenes, multi-perspective aerial imagery obtained with unmanned aerial vehicles and derived dense color 3-D models are increasingly being used. These type of data allow the direct and automated recognition of damage-related features, supporting an effective post-disaster structural damage assessment. However, the rapid collection and sharing of multi-perspective aerial imagery is still limited due to tight or lacking regulations and legal frameworks. A potential alternative is aerial video footage, which is typically acquired and shared by civil protection institutions or news media and which tends to be the first type of airborne data available. Nevertheless, inherent artifacts and the lack of suitable processing means have long limited its potential use in structural damage assessment and other post-disaster activities. In this research the usability of modern aerial video data was evaluated based on a comparative quality and application analysis of video data and multi-perspective imagery (photos), and their derivative 3-D point clouds created using current photogrammetric techniques. Additionally, the effects of external factors, such as topography and the presence of smoke and moving objects, were determined by analyzing two different earthquake-affected sites: Tainan (Taiwan) and Pescara del Tronto (Italy). Results demonstrated similar usabilities for video and photos. This is shown by the short 2 cm of difference between the accuracies of video- and photo-based 3-D point clouds. Despite the low video resolution, the usability of these data was compensated for by a small ground sampling distance. Instead of video characteristics, low quality and application resulted from non-data-related factors, such as changes in the scene, lack of texture, or moving objects. We conclude that not only are current video data more rapidly available than photos, but they also have a comparable ability to assist in image-based structural damage assessment and other post-disaster activities.

scholarly journals Usability of aerial video footage for 3D-scene reconstruction and structural damage assessment

2018 ◽  
Author(s):  
Johnny Cusicanqui ◽  
Norman Kerle ◽  
Francesco Nex
Keyword(s):  
Damage Assessment ◽  
Structural Damage ◽  
Moving Objects ◽  
Point Clouds ◽  
Video Data ◽  
Aerial Imagery ◽  
Video Footage ◽  
3D Point Clouds ◽  
Post Disaster ◽  
Aerial Video

Abstract. Remote sensing has evolved into the most efficient approach to assess post-disaster structural damage, in extensively affected areas through the use of space-borne data. For smaller, and in particular, complex urban disaster scenes, multi-perspective aerial imagery obtained with Unmanned Aerial Vehicles and derived dense colour 3D-models are increasingly being used. These type of data allow the direct and automated recognition of damage-related features, supporting an effective post-disaster structural damage assessment. However, the rapid collection and sharing of multi-perspective aerial imagery is still limited due to tight or lacking regulations and legal frameworks. A potential alternative is aerial video footage, typically acquired and shared by civil protection institutions or news media, and which tend to be the first type of airborne data available. Nevertheless, inherent artifacts and the lack of suitable processing means, have long limited its potential use in structural damage assessment and other post-disaster activities. In this research the usability of modern aerial video data was evaluated based on a comparative quality and application analysis of video data and multi-perspective imagery (photos), and their derivative 3D point clouds created using current photogrammetric techniques. Additionally, the effects of external factors, such as topography and the presence of smoke and moving objects were determined by analyzing two different earthquake-affected sites: Tainan (Taiwan) and Pescara del Tronto (Italy). Results demonstrated similar usabilities for video and photos. This is shown by the short 2 cm of difference between the accuracies of video and photo-based 3D Point clouds. Despite the low video resolution, the usability of this data was compensated by a small ground sampling distance. Instead of video characteristics, low quality and application resulted from non-data related factors, such as changes in the scene, lack of texture or moving objects. We conclude that current video data are not only more rapidly available than photos, but they also have a comparable ability to assist in image-based structural damage assessment and other post-disaster activities.

Near-Real Time Post-Disaster Damage Assessment with Airborne Oblique Video Data

2005 ◽  
pp. 337-353 ◽  
Author(s):  
Norman Kerle ◽  
Rob Stekelenburg ◽  
Frank van den Heuvel ◽  
Ben Gorte
Keyword(s):  
Real Time ◽  
Damage Assessment ◽  
Video Data ◽  
Post Disaster

scholarly journals Deep Learning Classification of 2D Orthomosaic Images and 3D Point Clouds for Post-Event Structural Damage Assessment

Drones ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 24 ◽  
Author(s):  
Yijun Liao ◽  
Mohammad Ebrahim Mohammadi ◽  
Richard L. Wood
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Damage Assessment ◽  
Structural Damage ◽  
Point Clouds ◽  
Depth Information ◽  
3D Point Clouds ◽  
Aerial Imaging ◽  
Damage States

Efficient and rapid data collection techniques are necessary to obtain transitory information in the aftermath of natural hazards, which is not only useful for post-event management and planning, but also for post-event structural damage assessment. Aerial imaging from unpiloted (gender-neutral, but also known as unmanned) aerial systems (UASs) or drones permits highly detailed site characterization, in particular in the aftermath of extreme events with minimal ground support, to document current conditions of the region of interest. However, aerial imaging results in a massive amount of data in the form of two-dimensional (2D) orthomosaic images and three-dimensional (3D) point clouds. Both types of datasets require effective and efficient data processing workflows to identify various damage states of structures. This manuscript aims to introduce two deep learning models based on both 2D and 3D convolutional neural networks to process the orthomosaic images and point clouds, for post windstorm classification. In detail, 2D convolutional neural networks (2D CNN) are developed based on transfer learning from two well-known networks AlexNet and VGGNet. In contrast, a 3D fully convolutional network (3DFCN) with skip connections was developed and trained based on the available point cloud data. Within this study, the datasets were created based on data from the aftermath of Hurricanes Harvey (Texas) and Maria (Puerto Rico). The developed 2DCNN and 3DFCN models were compared quantitatively based on the performance measures, and it was observed that the 3DFCN was more robust in detecting the various classes. This demonstrates the value and importance of 3D datasets, particularly the depth information, to distinguish between instances that represent different damage states in structures.

scholarly journals POTENTIAL OF MULTI-TEMPORAL OBLIQUE AIRBORNE IMAGERY FOR STRUCTURAL DAMAGE ASSESSMENT

2016 ◽  
Vol III-3 ◽  
pp. 355-362 ◽  
Author(s):  
A. Vetrivel ◽  
D. Duarte ◽  
F. Nex ◽  
M. Gerke ◽  
N. Kerle ◽  
...  
Keyword(s):  
Urban Areas ◽  
Damage Assessment ◽  
Structural Damage ◽  
Point Clouds ◽  
Structural Elements ◽  
Building Facades ◽  
Event Information ◽  
Multi Temporal ◽  
Oblique Images ◽  
The City

Quick post-disaster actions demand automated, rapid and detailed building damage assessment. Among the available technologies, post-event oblique airborne images have already shown their potential for this task. However, existing methods usually compensate the lack of pre-event information with aprioristic assumptions of building shapes and textures that can lead to uncertainties and misdetections. However, oblique images have been already captured over many cities of the world, and the exploitation of pre- and post-event data as inputs to damage assessment is readily feasible in urban areas. In this paper, we investigate the potential of multi-temporal oblique imagery for detailed damage assessment focusing on two methodologies: the first method aims at detecting severe structural damages related to geometrical deformation by combining the complementary information provided by photogrammetric point clouds and oblique images. The developed method detected 87% of damaged elements. The failed detections are due to varying noise levels within the point cloud which hindered the recognition of some structural elements. We observed, in general that the façade regions are very noisy in point clouds. To address this, we propose our second method which aims to detect damages to building façades using the oriented oblique images. The results show that the proposed methodology can effectively differentiate among the three proposed categories: collapsed/highly damaged, lower levels of damage and undamaged buildings, using a computationally light-weight approach. We describe the implementations of the above mentioned methods in detail and present the promising results achieved using multi-temporal oblique imagery over the city of L’Aquila (Italy).

scholarly journals POTENTIAL OF MULTI-TEMPORAL OBLIQUE AIRBORNE IMAGERY FOR STRUCTURAL DAMAGE ASSESSMENT

2016 ◽  
Vol III-3 ◽  
pp. 355-362 ◽  
Author(s):  
A. Vetrivel ◽  
D. Duarte ◽  
F. Nex ◽  
M. Gerke ◽  
N. Kerle ◽  
...  
Keyword(s):  
Urban Areas ◽  
Damage Assessment ◽  
Structural Damage ◽  
Point Clouds ◽  
Structural Elements ◽  
Building Facades ◽  
Event Information ◽  
Multi Temporal ◽  
Oblique Images ◽  
The City

Quick post-disaster actions demand automated, rapid and detailed building damage assessment. Among the available technologies, post-event oblique airborne images have already shown their potential for this task. However, existing methods usually compensate the lack of pre-event information with aprioristic assumptions of building shapes and textures that can lead to uncertainties and misdetections. However, oblique images have been already captured over many cities of the world, and the exploitation of pre- and post-event data as inputs to damage assessment is readily feasible in urban areas. In this paper, we investigate the potential of multi-temporal oblique imagery for detailed damage assessment focusing on two methodologies: the first method aims at detecting severe structural damages related to geometrical deformation by combining the complementary information provided by photogrammetric point clouds and oblique images. The developed method detected 87% of damaged elements. The failed detections are due to varying noise levels within the point cloud which hindered the recognition of some structural elements. We observed, in general that the façade regions are very noisy in point clouds. To address this, we propose our second method which aims to detect damages to building façades using the oriented oblique images. The results show that the proposed methodology can effectively differentiate among the three proposed categories: collapsed/highly damaged, lower levels of damage and undamaged buildings, using a computationally light-weight approach. We describe the implementations of the above mentioned methods in detail and present the promising results achieved using multi-temporal oblique imagery over the city of L’Aquila (Italy).

scholarly journals Features of Cross-Correlation Analysis in a Data-Driven Approach for Structural Damage Assessment

Sensors ◽  
2018 ◽  
Vol 18 (5) ◽  
pp. 1571 ◽  
Author(s):  
Jhonatan Camacho Navarro ◽  
Magda Ruiz ◽  
Rodolfo Villamizar ◽  
Luis Mujica ◽  
Jabid Quiroga
Keyword(s):  
Correlation Analysis ◽  
Damage Assessment ◽  
Structural Damage ◽  
Cross Correlation ◽  
Data Driven ◽  
Cross Correlation Analysis ◽  
Data Driven Approach

scholarly journals Natural Language Description of Videos for Smart Surveillance

2021 ◽  
Vol 11 (9) ◽  
pp. 3730
Author(s):  
Aniqa Dilawari ◽  
Muhammad Usman Ghani Khan ◽  
Yasser D. Al-Otaibi ◽  
Zahoor-ur Rehman ◽  
Atta-ur Rahman ◽  
...  
Keyword(s):  
Natural Language ◽  
Feature Recognition ◽  
Scene Recognition ◽  
Video Data ◽  
Surveillance Video ◽  
Video Footage ◽  
Parallel Pipeline ◽  
September 11 Attacks ◽  
Description Framework ◽  
High Level

After the September 11 attacks, security and surveillance measures have changed across the globe. Now, surveillance cameras are installed almost everywhere to monitor video footage. Though quite handy, these cameras produce videos in a massive size and volume. The major challenge faced by security agencies is the effort of analyzing the surveillance video data collected and generated daily. Problems related to these videos are twofold: (1) understanding the contents of video streams, and (2) conversion of the video contents to condensed formats, such as textual interpretations and summaries, to save storage space. In this paper, we have proposed a video description framework on a surveillance dataset. This framework is based on the multitask learning of high-level features (HLFs) using a convolutional neural network (CNN) and natural language generation (NLG) through bidirectional recurrent networks. For each specific task, a parallel pipeline is derived from the base visual geometry group (VGG)-16 model. Tasks include scene recognition, action recognition, object recognition and human face specific feature recognition. Experimental results on the TRECViD, UET Video Surveillance (UETVS) and AGRIINTRUSION datasets depict that the model outperforms state-of-the-art methods by a METEOR (Metric for Evaluation of Translation with Explicit ORdering) score of 33.9%, 34.3%, and 31.2%, respectively. Our results show that our framework has distinct advantages over traditional rule-based models for the recognition and generation of natural language descriptions.

scholarly journals The 2011 Tohoku Tsunami from the Sky: A Review on the Evolution of Artificial Intelligence Methods for Damage Assessment

Geosciences ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 133
Author(s):  
Jérémie Sublime
Keyword(s):  
Artificial Intelligence ◽  
Case Studies ◽  
Damage Assessment ◽  
Structural Damage ◽  
North East ◽  
2011 Tohoku Tsunami ◽  
Artificial Intelligence Methods ◽  
The Future ◽  
Tohoku Tsunami

The Tohoku tsunami was a devastating event that struck North-East Japan in 2011 and remained in the memory of people worldwide. The amount of devastation was so great that it took years to achieve a proper assessment of the economical and structural damage, with the consequences still being felt today. However, this tsunami was also one of the first observed from the sky by modern satellites and aircrafts, thus providing a unique opportunity to exploit these data and train artificial intelligence methods that could help to better handle the aftermath of similar disasters in the future. This paper provides a review of how artificial intelligence methods applied to case studies about the Tohoku tsunami have evolved since 2011. We focus on more than 15 studies that are compared and evaluated in terms of the data they require, the methods used, their degree of automation, their metric performances, and their strengths and weaknesses.

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