Enhancing Road Maps by Parsing Aerial Images Around the World

Máttyus, Gellért; Wang, Shenlong; Fidler, Sanja; Urtasun, Raquel

doi:10.1109/iccv.2015.197

Cited by 111 publications

(81 citation statements)

References 36 publications

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“…We note that (nonconvolutional) deep networks in conjunction with OSM labels have also been applied for patch-based road extraction in overhead images of ≈ 1 m GSD at large scale [46], [47]. More recently, Máttyus et al [48] combine OSM data with aerial images to augment maps with additional information from imagery like road widths. They design a sophisticated random field to probabilistically combine various sources of road evidence, for instance, cars, to estimate road widths at global scale using OSM and aerial images.…”

Section: Related Workmentioning

confidence: 99%

Learning Aerial Image Segmentation From Online Maps

Kaiser

Wegner

Lucchi

et al. 2017

IEEE Trans. Geosci. Remote Sensing

264

157

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Abstract-This paper deals with semantic segmentation of high-resolution (aerial) images where a semantic class label is assigned to each pixel via supervised classification as a basis for automatic map generation. Recently, deep convolutional neural networks (CNNs) have shown impressive performance and have quickly become the de-facto standard for semantic segmentation, with the added benefit that task-specific feature design is no longer necessary. However, a major downside of deep learning methods is that they are extremely data hungry, thus aggravating the perennial bottleneck of supervised classification, to obtain enough annotated training data. On the other hand, it has been observed that they are rather robust against noise in the training labels. This opens up the intriguing possibility to avoid annotating huge amounts of training data, and instead train the classifier from existing legacy data or crowd-sourced maps that can exhibit high levels of noise. The question addressed in this paper is: can training with large-scale publicly available labels replace a substantial part of the manual labeling effort and still achieve sufficient performance? Such data will inevitably contain a significant portion of errors, but in return virtually unlimited quantities of it are available in larger parts of the world. We adapt a state-of-the-art CNN architecture for semantic segmentation of buildings and roads in aerial images, and compare its performance when using different training data sets, ranging from manually labeled pixel-accurate ground truth of the same city to automatic training data derived from OpenStreetMap data from distant locations. We report our results that indicate that satisfying performance can be obtained with significantly less manual annotation effort, by exploiting noisy large-scale training data.

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Section: Related Workmentioning

confidence: 99%

Learning Aerial Image Segmentation From Online Maps

Kaiser

Wegner

Lucchi

et al. 2017

IEEE Trans. Geosci. Remote Sensing

264

157

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“…[53] propose to model longevity and connectivity of road networks with a higher-order CRF, which is extended in [52] to sampling more flexible, road-like higherorder cliques through collections of shortest paths, and to also model buildings with higher-order cliques in [39]. [33] combine OSM and aerial images to augment maps with additional information like the road width using a MRF formulation, which scales to large regions and achieves good results at several locations world-wide. Two recent works apply deep learning to road center-line extraction in aerial images.…”

Section: Related Workmentioning

confidence: 99%

Topological Map Extraction From Overhead Images

Wegner

Lucchi

2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

141

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We propose a new approach, named PolyMapper, to circumvent the conventional pixel-wise segmentation of (aerial) images and predict objects in a vector representation directly. PolyMapper directly extracts the topological map of a city from overhead images as collections of building footprints and road networks. In order to unify the shape representation for different types of objects, we also propose a novel sequentialization method that reformulates a graph structure as closed polygons. Experiments are conducted on both existing and self-collected large-scale datasets of several cities. Our empirical results demonstrate that our end-to-end learnable model is capable of drawing polygons of building footprints and road networks that very closely approximate the structure of existing online map services, in a fully automated manner. Quantitative and qualitative comparison to the state-of-the-art also shows that our approach achieves good levels of performance. To the best of our knowledge, the automatic extraction of largescale topological maps is a novel contribution in the remote sensing community that we believe will help develop models with more informed geometrical constraints.

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“…Computer vision methods can be used to extract geo-localized information about elements contained in these images (e.g., landmarks, vehicles, buildings). Satellite images have been used to detect areas of urban development and buildings [Sirmacek and Unsalan 2009], roads [Mattyus et al 2015; Mokhtarzade and Zoej 2007] and vehicles [Leitloff et al 2010]. [Senlet and Elgammal 2012] propose sidewalk detection that corrects occlusion errors by interpolating available visual data.…”

Section: Related Workmentioning

confidence: 99%

Mind Your Crossings

Ahmetovic

Manduchi

Coughlan

et al. 2017

ACM Trans. Access. Comput.

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For blind travelers, finding crosswalks and remaining within their borders while traversing them is a crucial part of any trip involving street crossings. While standard Orientation & Mobility (O&M) techniques allow blind travelers to safely negotiate street crossings, additional information about crosswalks and other important features at intersections would be helpful in many situations, resulting in greater safety and/or comfort during independent travel. For instance, in planning a trip a blind pedestrian may wish to be informed of the presence of all marked crossings near a desired route. We have conducted a survey of several O&M experts from the United States and Italy to determine the role that crosswalks play in travel by blind pedestrians. The results show stark differences between survey respondents from the U.S. compared with Italy: the former group emphasized the importance of following standard O&M techniques at all legal crossings (marked or unmarked), while the latter group strongly recommended crossing at marked crossings whenever possible. These contrasting opinions reflect differences in the traffic regulations of the two countries and highlight the diversity of needs that travelers in different regions may have. To address the challenges faced by blind pedestrians in negotiating street crossings, we devised a computer vision-based technique that mines existing spatial image databases for discovery of zebra crosswalks in urban settings. Our algorithm first searches for zebra crosswalks in satellite images; all candidates thus found are validated against spatially registered Google Street View images. This cascaded approach enables fast and reliable discovery and localization of zebra crosswalks in large image datasets. While fully automatic, our algorithm can be improved by a final crowdsourcing validation. To this end, we developed a Pedestrian Crossing Human Validation (PCHV) web service, which supports crowdsourcing to rule out false positives and identify false negatives.

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Enhancing Road Maps by Parsing Aerial Images Around the World

Cited by 111 publications

References 36 publications

Learning Aerial Image Segmentation From Online Maps

Learning Aerial Image Segmentation From Online Maps

Topological Map Extraction From Overhead Images

Mind Your Crossings

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