Road network extraction via deep learning and line integral convolution

Li, Peikang; Zang, Yu; Wang, Cheng; Li, Jonathan; Cheng, Ming; Luo, Lei; Yu, Yao

doi:10.1109/igarss.2016.7729408

Cited by 44 publications

(29 citation statements)

References 13 publications

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“…Buildings (Maggiori et al, 2017;Xu et al, 2018;Wu et al, 2018) Farmlands (Lu et al, 2015); Land cover (Chen, Fan, Wang, Wu, & Sun, 2017;Zhang, Zheng, Tang, & Zhao, 2017); Water bodies (He, Du, Chen, & Chen, 2017); Roads (Li, Zang, et al, 2016); Roads and buildings (Alshehhi, Marpu, Woon, & Mura, 2017;Saito & Aoki, 2015) 6.1. Insufficient training data in remote sensing Sufficient training data is the key for training CNN models for RS image classification.…”

Section: Challenges and Conclusionmentioning

confidence: 99%

A survey of remote sensing image classification based on CNNs

et al. 2019

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With the development of earth observation technologies, the acquired remote sensing images are increasing dramatically, and a new era of big data in remote sensing is coming. How to effectively mine these massive volumes of remote sensing data are new challenges. Deep learning provides a new approach for analyzing these remote sensing data. As one of the deep learning models, convolutional neural networks (CNNs) can directly extract features from massive amounts of imagery data and is good at exploiting semantic features of imagery data. CNNs have achieved remarkable success in computer vision. In recent years, quite a few researchers have studied remote sensing image classification using CNNs, and CNNs can be applied to realize rapid, economical and accurate analysis and feature extraction from remote sensing data. This paper aims to provide a survey of the current state-of-the-art application of CNN-based deep learning in remote sensing image classification. We first briefly introduce the principles and characteristics of CNNs. We then survey developments and structural improvements on CNN models that make CNNs more suitable for remote sensing image classification, available datasets for remote sensing image classification, and data augmentation techniques. Then, three typical CNN application cases in remote sensing image classification: scene classification, object detection and object segmentation are presented. We also discuss the problems and challenges of CNN-based remote sensing image classification, and propose corresponding measures and suggestions. We hope that the survey can facilitate the advancement of remote sensing image classification research and help remote-sensing scientists to tackle classification tasks with the state-of-art deep learning algorithms and techniques.

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Section: Challenges and Conclusionmentioning

confidence: 99%

A survey of remote sensing image classification based on CNNs

et al. 2019

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“…To transfer the latter to our case, we may investigate whether connecting tiles of similar boundary likelihood can omit the need for an initial MCG image segmentation: By using Fully Convolutional Networks (FCNs) [52] each pixel of the input image would be assigned a boundary likelihood, which can be connected using Ultrametric Contour Maps (UCMs) [53] included in MCG [54]. Connecting pixels of corresponding boundary likelihoods could also be realized by using MCG-based contour closure [55], line integral convolution [56], or template matching [57].…”

Section: Comparison To Previous Studiesmentioning

confidence: 99%

Application of Deep Learning for Delineation of Visible Cadastral Boundaries from Remote Sensing Imagery

et al. 2019

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Cadastral boundaries are often demarcated by objects that are visible in remote sensing imagery. Indirect surveying relies on the delineation of visible parcel boundaries from such images. Despite advances in automated detection and localization of objects from images, indirect surveying is rarely automated and relies on manual on-screen delineation. We have previously introduced a boundary delineation workflow, comprising image segmentation, boundary classification and interactive delineation that we applied on Unmanned Aerial Vehicle (UAV) data to delineate roads. In this study, we improve each of these steps. For image segmentation, we remove the need to reduce the image resolution and we limit over-segmentation by reducing the number of segment lines by 80% through filtering. For boundary classification, we show how Convolutional Neural Networks (CNN) can be used for boundary line classification, thereby eliminating the previous need for Random Forest (RF) feature generation and thus achieving 71% accuracy. For interactive delineation, we develop additional and more intuitive delineation functionalities that cover more application cases. We test our approach on more varied and larger data sets by applying it to UAV and aerial imagery of 0.02-0.25 m resolution from Kenya, Rwanda and Ethiopia. We show that it is more effective in terms of clicks and time compared to manual delineation for parcels surrounded by visible boundaries. Strongest advantages are obtained for rural scenes delineated from aerial imagery, where the delineation effort per parcel requires 38% less time and 80% fewer clicks compared to manual delineation.

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“…Inspired from these approaches, we extract the urban elements of a particular area from satellite images using deep learning to capture their representative features. Similar approaches extract road networks using neural networks for dynamic environments [18] from LiDAR data [19], using line integrals [20] and using image processing approaches [21][22][23]. In our approach, to provide scalability across countries and terrains, we have explored and modified state-of-the-art image segmentation networks.…”

Section: Related Workmentioning

confidence: 99%

Generative Street Addresses from Satellite Imagery

Demir

Hughes

Raj

et al. 2018

IJGI

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Abstract:We describe our automatic generative algorithm to create street addresses from satellite images by learning and labeling roads, regions, and address cells. Currently, 75% of the world's roads lack adequate street addressing systems. Recent geocoding initiatives tend to convert pure latitude and longitude information into a memorable form for unknown areas. However, settlements are identified by streets, and such addressing schemes are not coherent with the road topology. Instead, we propose a generative address design that maps the globe in accordance with streets. Our algorithm starts with extracting roads from satellite imagery by utilizing deep learning. Then, it uniquely labels the regions, roads, and structures using some graph-and proximity-based algorithms. We also extend our addressing scheme to (i) cover inaccessible areas following similar design principles; (ii) be inclusive and flexible for changes on the ground; and (iii) lead as a pioneer for a unified street-based global geodatabase. We present our results on an example of a developed city and multiple undeveloped cities. We also compare productivity on the basis of current ad hoc and new complete addresses. We conclude by contrasting our generative addresses to current industrial and open solutions.

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Road network extraction via deep learning and line integral convolution

Cited by 44 publications

References 13 publications

A survey of remote sensing image classification based on CNNs

A survey of remote sensing image classification based on CNNs

Application of Deep Learning for Delineation of Visible Cadastral Boundaries from Remote Sensing Imagery

Generative Street Addresses from Satellite Imagery

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