| HAL-Inria | Publications, software ... of Inria's scientists |
Conference papers
Polygonal Building Segmentation by Frame Field Learning
Abstract : While state of the art image segmentation models typically output segmentations in raster format, applications in geographic information systems often require vector polygons. To help bridge the gap between deep network output and the format used in downstream tasks, we add a frame field output to a deep segmentation model for extracting buildings from remote sensing images. We train a deep neural network that aligns a predicted frame field to ground truth contours. This additional objective improves segmentation quality by leveraging multi-task learning and provides structural information that later facilitates polygonization; we also introduce a polygonization algorithm that utilizes the frame field along with the raster segmentation. Our code is available at https://github.com/Lydorn/Polygonization-by-Frame-Field-Learning.
https://hal.inria.fr/hal-02548545
Contributor : Nicolas Girard Connect in order to contact the contributor
Submitted on : Wednesday, March 31, 2021 - 2:05:40 PM
Last modification on : Thursday, January 20, 2022 - 5:32:54 PM
Contributor : Nicolas Girard Connect in order to contact the contributor
Submitted on : Wednesday, March 31, 2021 - 2:05:40 PM
Last modification on : Thursday, January 20, 2022 - 5:32:54 PM
File
archive.pdf
Files produced by the author(s)