Topological Map Extraction From Overhead Images

Zuoyue Li, Jan Dirk Wegner, Aurelien Lucchi; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 1715-1724

Abstract


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-arts also show that our approach achieves good levels of performance. To the best of our knowledge, the automatic extraction of large-scale topological maps is a novel contribution in the remote sensing community that we believe will help develop models with more informed geometrical constraints.

Related Material


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[bibtex]
@InProceedings{Li_2019_ICCV,
author = {Li, Zuoyue and Wegner, Jan Dirk and Lucchi, Aurelien},
title = {Topological Map Extraction From Overhead Images},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}