Nazr-CNN: Fine-Grained Classification of UAV Imagery for Damage Assessment

Attari, Nazia; Ofli, Ferda; Awad, Maher; Lucas, Jacob; Chawla, Suruchi

doi:10.48550/arxiv.1611.06474

Cited by 4 publications

(4 citation statements)

References 27 publications

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“…In [73], an existing deep model [112], pre-trained on Ima-geNet [43], is fine-tuned on aerial photos captured through unmanned aerial vehicles (UAV) during or after different types of natural disasters, namely floods, fires and building collapsed. Another work aiming damage assessment of natural disasters in images taken through UAV has been proposed by Nazr et al [15]. The adopted network is composed of two components.…”

Section: Disaster Detection In Satellite Imagerymentioning

confidence: 99%

Natural disasters detection in social media and satellite imagery: a survey

Said

Ahmad

Riegler

et al. 2019

Multimed Tools Appl

103

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The analysis of natural disaster-related multimedia content got great attention in recent years. Being one of the most important sources of information, social media have been crawled over the years to collect and analyze disasterrelated multimedia content. Satellite imagery has also been widely explored for disasters analysis. In this paper, we survey the existing literature on disaster detection and analysis of the retrieved information from social media and satellites. Literature on disaster detection and analysis of related multimedia content on the basis of the nature of the content can be categorized into three groups, namely (i) disaster detection in text; (ii) analysis of disaster-related visual content from social media; and (iii) disaster detection in satellite imagery. We extensively review different approaches proposed in these three domains. Furthermore, we also review benchmarking datasets available for the evaluation of disaster detection frameworks. Moreover, we provide a detailed discussion on the insights obtained from the literature review, and identify future trends and challenges, which will provide an important starting point for the researchers in the field.

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Section: Disaster Detection In Satellite Imagerymentioning

confidence: 99%

Natural disasters detection in social media and satellite imagery: a survey

Said

Ahmad

Riegler

et al. 2019

Multimed Tools Appl

103

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show abstract

“…Kamilaris et al [79] rely on VGGNet [127] pre-trained on ImageNet [42], which is ne-tuned on remotely sensed images of natural disasters captured through Unmanned Aerial Vehicles (UAV). Nazr et al [15] adopt a deep architecture for damage assessment of natural disasters in aerial images from UAV. Liu et al [92] use deep models along with wavelet transformation for the automatic detection of natural disasters in satellite imagery.…”

Section: Event Recognition In Remote Sensed Datamentioning

confidence: 99%

How Deep Features Have Improved Event Recognition in Multimedia

Ahmad

Conci

2019

ACM Trans. Multimedia Comput. Commun. Appl.

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Event recognition is one of the areas in multimedia that is attracting great attention of researchers. Being applicable in a wide range of applications, from personal to collective events, a number of interesting solutions for event recognition using multimedia information sources have been proposed. On the other hand, following their immense success in classification, object recognition, and detection, deep learning has been shown to perform well in event recognition tasks also. Thus, a large portion of the literature on event analysis relies nowadays on deep learning architectures. In this article, we provide an extensive overview of the existing literature in this field, analyzing how deep features and deep learning architectures have changed the performance of event recognition frameworks. The literature on event-based analysis of multimedia contents can be categorized into four groups, namely (i) event recognition in single images; (ii) event recognition in personal photo collections; (iii) event recognition in videos; and (iv) event recognition in audio recordings. In this article, we extensively review different deep-learning-based frameworks for event recognition in these four domains. Furthermore, we also review some benchmark datasets made available to the scientific community to validate novel event recognition pipelines. In the final part of the manuscript, we also provide a detailed discussion on basic insights gathered from the literature review, and identify future trends and challenges.

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“…However, when we turn to fine-grained land use classification on large-scale instead of these toy examples, we could not avoid these challenges. There also exists some work [38], [39], [40] focusing on the concept of "fine-grained" in land use classification. However, [38] indicates fine granularity on time scale and [39] indicates fine granularity on levels of damage.…”

Section: Related Workmentioning

confidence: 99%

“…There also exists some work [38], [39], [40] focusing on the concept of "fine-grained" in land use classification. However, [38] indicates fine granularity on time scale and [39] indicates fine granularity on levels of damage. None of them refer to the granuality of land use types.…”

Section: Related Workmentioning

confidence: 99%

Fine-Grained Land Use Classification at the City Scale Using Ground-Level Images

Zhu

Deng

Newsam

2019

IEEE Trans. Multimedia

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We perform fine-grained land use mapping at the city scale using ground-level images. Mapping land use is considerably more difficult than mapping land cover and is generally not possible using overhead imagery as it requires close-up views and seeing inside buildings. We postulate that the growing collections of georeferenced, ground-level images suggest an alternate approach to this geographic knowledge discovery problem. We develop a general framework that uses Flickr images to map 45 different land-use classes for the City of San Francisco. Individual images are classified using a novel convolutional neural network containing two streams, one for recognizing objects and another for recognizing scenes. This network is trained in an end-to-end manner directly on the labeled training images. We propose several strategies to overcome the noisiness of our user-generated data including search-based training set augmentation and online adaptive training. We derive a ground truth map of San Francisco in order to evaluate our method. We demonstrate the effectiveness of our approach through geo-visualization and quantitative analysis. Our framework achieves over 29% recall at the individual land parcel level which represents a strong baseline for the challenging 45-way land use classification problem especially given the noisiness of the image data.

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Nazr-CNN: Fine-Grained Classification of UAV Imagery for Damage Assessment

Cited by 4 publications

References 27 publications

Natural disasters detection in social media and satellite imagery: a survey

Natural disasters detection in social media and satellite imagery: a survey

How Deep Features Have Improved Event Recognition in Multimedia

Fine-Grained Land Use Classification at the City Scale Using Ground-Level Images

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