The modern road network topology comprises intricately designed structures that introduce complexity when automatically reconstructing road networks. While open resources like OpenStreetMap (OSM) offer road networks with well-defined topology, they may not always be up to date worldwide. In this paper, we propose a fully automated pipeline for extracting road networks from very-high-resolution (VHR) satellite imagery. Our approach directly generates road line-strings that are seamlessly connected and precisely positioned. The process involves three key modules: a CNN-based neural network for road segmentation, a graph optimization algorithm to convert road predictions into vector line-strings, and a machine learning model for classifying road materials. Compared to OSM data, our results demonstrate significant potential for providing the latest road layouts and precise positions of road segments.

Automatic footprint extraction from remote sensing images remains a popular yet challenging research topic, driven by numerous applications such as telecommunications and urban management. In this paper, we propose an automatic pipeline for reliable building footprint extraction from high-resolution satellite images. The proposed method takes as the input two images with the corresponding rational polynomial coefficient models, applies deep learning to each image for building segmentation and estimation of contour orientations, then matches extracted building footprints from two images for estimating reliability of each footprint. The pipeline has proven to be efficient for reconstructing accurate building footprints, and estimated reliability scores help to efficiently validate and correct predicted maps.

Automatic 3D reconstruction of urban scenes from stereo pairs of satellite images remains a popular yet challenging research topic, driven by numerous applications such as telecommunications and defense. The quality of reconstruction results depends particularly on the quality of the available stereo pair. In this paper, we propose an operational pipeline for large-scale 3D reconstruction of buildings from stereo satellite images. The proposed chain uses U-net to extract contour polygons of buildings, and the combination of optimization and computational geometry techniques to reconstruct a digital terrain model and a digital height model, and to correctly estimate the position of building footprints. The pipeline has proven to be efficient for 3D building reconstruction, even if the close-to-nadir image is not available.

The over-segmentation of images into atomic regions has become a standard and powerful tool in Vision. Traditional superpixel methods, that operate at the pixel level, cannot directly capture the geometric information disseminated into the images. We propose an alternative to these methods by operating at the level of geometric shapes. Our algorithm partitions images into convex polygons. It presents several interesting properties in terms of geometric guarantees, region compactness and scalability. The overall strategy consists in building a Voronoi diagram that conforms to preliminarily detected line-segments, before homogenizing the partition by spatial point process distributed over the image gradient. Our method is particularly adapted to images with strong geometric signatures, typically man-made objects and environments. We show the potential of our approach with experiments on large-scale images and comparisons with state-of-the-art superpixel methods.