Coupling ground-level panoramas and aerial imagery for change detection

Ghouaiel, Nehla; Lefèvre, Sébastien

doi:10.1080/10095020.2016.1244998

Cited by 23 publications

(14 citation statements)

References 18 publications

(24 reference statements)

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“…In this paper, the types of data used for change detection are divided into optical RS images, SAR images, and street view images. It should be noted that street view images are usually not used as RS data but as auxiliary data [29][30][31], so it is not common in the RS community. Still, there are overlapping ideas for change detection.…”

Section: Data Sources For Change Detectionmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Data Sources For Change Detectionmentioning

confidence: 99%

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

et al. 2020

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“…These lists consist of only geographic coordinates, for most of the classes, with exception of the classes forest, lake, river, and park, which we collected only place's that have a name assigned to it. The lists are then fed to scripts that utilize the Google StaticMap API 6 , to collect the aerial images, and the Google StreetView API 7 , to collect the frontal images. Except for the zoom parameter, which was set to a proper value per class empirically, the default values of the Google APIs were used for the aerial images.…”

Section: A the Airound Datasetmentioning

confidence: 99%

“…Such combination of multiple sources images can benefit many applications in different fields, like 3D human pose estimation [2], places geo-localization [3], and urban land use [4]. Motivated by these benefits, several approaches [5], [6], [7], [8], [9] have been proposed to exploit multi-view datasets to face distinct tasks. Although important, it is not easy to find multi-view datasets for image-related tasks, given the difficulty in creating and labeling such data.…”

Section: Introductionmentioning

confidence: 99%

AiRound and CV-BrCT: Novel Multiview Datasets for Scene Classification

Machado

Ferreira

Nogueira

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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It is undeniable that aerial/satellite images can provide useful information for a large variety of tasks. But, since these images are always looking from above, some applications can benefit from complementary information provided by other perspective views of the scene, such as ground-level images. Despite a large number of public repositories for both georeferenced photographs and aerial images, there is a lack of benchmark datasets that allow the development of approaches that exploit the benefits and complementarity of aerial/ground imagery. In this paper, we present two new publicly available datasets named AiRound and CV-BrCT. The first one contains triplets of images from the same geographic coordinate with different perspectives of view extracted from various places around the world. Each triplet is composed of an aerial RGB image, a ground-level perspective image, and a Sentinel-2 sample. The second dataset contains pairs of aerial and street-level images extracted from southeast Brazil. We design an extensive set of experiments concerning multi-view scene classification, using early and late fusion. Such experiments were conducted to show that image classification can be enhanced using multi-view data.

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“…Máttyus et al [20] perform joint inference over both monocular aerial and ground-level images from a stereo camera for fine-grained road segmentation, while Wegner et al [29] detect and classify trees using features extracted from overhead and ground-level images. Ghouaiel and Lefèvre [7] transform ground-level panoramas to an overhead perspective for change detection. Zhai et al [35] propose a transformation to extract meaningful features from overhead imagery.…”

Section: Related Workmentioning

confidence: 99%

A Unified Model for Near and Remote Sensing

Workman

Zhai

Crandall

et al. 2017

2017 IEEE International Conference on Computer Vision (ICCV)

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We propose a novel convolutional neural network architecture for estimating geospatial functions such as population density, land cover, or land use. In our approach, we combine overhead and ground-level images in an end-toend trainable neural network, which uses kernel regression and density estimation to convert features extracted from the ground-level images into a dense feature map. The output of this network is a dense estimate of the geospatial function in the form of a pixel-level labeling of the overhead image. To evaluate our approach, we created a large dataset of overhead and ground-level images from a major urban area with three sets of labels: land use, building function, and building age. We find that our approach is more accurate for all tasks, in some cases dramatically so.

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Coupling ground-level panoramas and aerial imagery for change detection

Abstract: International audienc

Cited by 23 publications

References 18 publications

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

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

AiRound and CV-BrCT: Novel Multiview Datasets for Scene Classification

A Unified Model for Near and Remote Sensing

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