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Building outline extraction from aerial imagery and digital surface model with a frame field learning framework
- Source :
- The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 43(B2-2021), 487-493, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLIII-B2-2021, Pp 487-493 (2021)
- Publication Year :
- 2021
- Publisher :
- International Society for Photogrammetry and Remote Sensing (ISPRS), 2021.
-
Abstract
- Deep learning-based semantic segmentation models for building delineation face the challenge of producing precise and regular building outlines. Recently, a building delineation method based on frame field learning was proposed by Girard et al. (2020) to extract regular building footprints as vector polygons directly from aerial RGB images. A fully convolution network (FCN) is trained to learn simultaneously the building mask, contours, and frame field followed by a polygonization method. With the direction information of the building contours stored in the frame field, the polygonization algorithm produces regular outlines accurately detecting edges and corners. This paper investigated the contribution of elevation data from the normalized digital surface model (nDSM) to extract accurate and regular building polygons. The 3D information provided by the nDSM overcomes the aerial images’ limitations and contributes to distinguishing the buildings from the background more accurately. Experiments conducted in Enschede, the Netherlands, demonstrate that the nDSM improves building outlines’ accuracy, resulting in better-aligned building polygons and prevents false positives. The investigated deep learning approach (fusing RGB + nDSM) results in a mean intersection over union (IOU) of 0.70 in the urban area. The baseline method (using RGB only) results in an IOU of 0.58 in the same area. A qualitative analysis of the results shows that the investigated model predicts more precise and regular polygons for large and complex structures.
- Subjects :
- Technology
Frame Field
Computer science
business.industry
Deep learning
Frame (networking)
Convolutional Neural Networks
Regular polygon
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Regularized Polygonization
Engineering (General). Civil engineering (General)
Convolutional neural network
TA1501-1820
Building Outline Delineation
Intersection
Face (geometry)
RGB color model
Segmentation
Computer vision
Applied optics. Photonics
Artificial intelligence
TA1-2040
business
ITC-GOLD
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 43(B2-2021), 487-493, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLIII-B2-2021, Pp 487-493 (2021)
- Accession number :
- edsair.doi.dedup.....1724d9391c8097c17a70be9edf6f17ee