Optimization of OpenStreetMap Building Footprints Based on Semantic Information of Oblique UAV Images

Zhuo, Xiangyu; Fraundorfer, Friedrich; Kurz, Franz; Reinartz, Peter

doi:10.3390/rs10040624

Cited by 30 publications

(13 citation statements)

References 32 publications

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“…However, the graph models are exploited only as postprocessing steps in these studies. In [47], the FullCRF is plugged in at the end of the FCN for end-to-end training, which has preserved sharp boundaries, but requires longer training time and greater efforts to find optimal parameters.…”

Section: Graph Modelmentioning

confidence: 99%

Building Footprint Generation by Integrating Convolution Neural Network With Feature Pairwise Conditional Random Field (FPCRF)

Shi

Huang

et al. 2020

IEEE Trans. Geosci. Remote Sensing

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Building footprint maps are vital to many remote sensing (RS) applications, such as 3-D building modeling, urban planning, and disaster management. Due to the complexity of buildings, the accurate and reliable generation of the building footprint from RS imagery is still a challenging task. In this article, an end-to-end building footprint generation approach that integrates convolution neural network (CNN) and graph model is proposed. CNN serves as the feature extractor, while the graph model can take spatial correlation into consideration. Moreover, we propose to implement the feature pairwise conditional random field (FPCRF) as a graph model to preserve sharp boundaries and fine-grained segmentation. Experiments are conducted on four different data sets: 1) Planetscope satellite imagery of the cities of Munich, Paris, Rome, and Zurich; 2) ISPRS Benchmark data from the city of Potsdam; 3) Dstl Kaggle data set; and 4) Inria Aerial Image Labeling data of Austin, Chicago, Kitsap County, Western Tyrol, and Vienna. It is found that the proposed end-toend building footprint generation framework with the FPCRF as the graph model can further improve the accuracy of building footprint generation by using only CNN, which is the current state of the art.

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Section: Graph Modelmentioning

confidence: 99%

Building Footprint Generation by Integrating Convolution Neural Network With Feature Pairwise Conditional Random Field (FPCRF)

Shi

Huang

et al. 2020

IEEE Trans. Geosci. Remote Sensing

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“…Xu et al [ 18 ] improved building extraction accuracy using a new model based on deep residual network (ResNet) which is defined as Res-U-Net and object-oriented guided filter, and the reported accuracy is around 5% higher than other standard models (SegNet, FCN). For better DL application in UAV image processing, Zhuo et al [ 19 ] proposed a DL semantic segmentation on oblique images from UAV to optimize building footprint extraction from OpenStreetMap (OSM).…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach on Building Detection from Unmanned Aerial Vehicle-Based Images in Riverbank Monitoring

Boonpook

Tan

et al. 2018

Sensors

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Buildings along riverbanks are likely to be affected by rising water levels, therefore the acquisition of accurate building information has great importance not only for riverbank environmental protection but also for dealing with emergency cases like flooding. UAV-based photographs are flexible and cloud-free compared to satellite images and can provide very high-resolution images up to centimeter level, while there exist great challenges in quickly and accurately detecting and extracting building from UAV images because there are usually too many details and distortions on UAV images. In this paper, a deep learning (DL)-based approach is proposed for more accurately extracting building information, in which the network architecture, SegNet, is used in the semantic segmentation after the network training on a completely labeled UAV image dataset covering multi-dimension urban settlement appearances along a riverbank area in Chongqing. The experiment results show that an excellent performance has been obtained in the detection of buildings from untrained locations with an average overall accuracy more than 90%. To verify the generality and advantage of the proposed method, the procedure is further evaluated by training and testing with another two open standard datasets which have a variety of building patterns and styles, and the final overall accuracies of building extraction are more than 93% and 95%, respectively.

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“…The last step of the method is the prediction of new building annotations that are missing by using a CNN model that predicts building footprint with predefined shape priors. The method in [31] aims at correcting OSM building annotations by using contour information from image segmentation of oblique images, acquired by Unmanned Aerial Vehicles (UAV). The paper uses contour information of multiple-view images and 3D building models to correct OSM building annotations.…”

Section: Correct and Create New Annotationsmentioning

confidence: 99%

OpenStreetMap: Challenges and Opportunities in Machine Learning and Remote Sensing

Muñoz

Srivastava

Tuia

et al. 2021

IEEE Geosci. Remote Sens. Mag.

102

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OpenStreetMap (OSM) is a community-based, freely available, editable map service that was created as an alternative to authoritative ones. Given that it is edited mainly by volunteers with different mapping skills, the completeness and quality of its annotations are heterogeneous across different geographical locations. Despite that, OSM has been widely used in several applications in Geosciences, Earth Observation and environmental sciences. In this work, we present a review of recent methods based on machine learning to improve and use OSM data. Such methods aim either 1) at improving the coverage and quality of OSM layers, typically using GIS and remote sensing technologies, or 2) at using the existing OSM layers to train models based on image data to serve applications like navigation or land use classification. We believe that OSM (as well as other sources of open land maps) can change the way we interpret remote sensing data and that the synergy with machine learning can scale participatory map making and its quality to the level needed to serve global and up-to-date land mapping. A preliminary version of this manuscript has been presented in the first authors dissertation [1].

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Optimization of OpenStreetMap Building Footprints Based on Semantic Information of Oblique UAV Images

Cited by 30 publications

References 32 publications

Building Footprint Generation by Integrating Convolution Neural Network With Feature Pairwise Conditional Random Field (FPCRF)

Building Footprint Generation by Integrating Convolution Neural Network With Feature Pairwise Conditional Random Field (FPCRF)

A Deep Learning Approach on Building Detection from Unmanned Aerial Vehicle-Based Images in Riverbank Monitoring

OpenStreetMap: Challenges and Opportunities in Machine Learning and Remote Sensing

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