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Found 6 papers, 1 papers with code
Re:PolyWorld - A Graph Neural Network for Polygonal Scene Parsing
Re:PolyWorld not only outperforms the original model on building extraction in aerial images, thanks to the proposed joint analysis of vertices and edges, but also beats the state-of-the-art in multiple other domains.
PolyWorld: Polygonal Building Extraction with Graph Neural Networks in Satellite Images
While most state-of-the-art instance segmentation methods produce binary segmentation masks, geographic and cartographic applications typically require precise vector polygons of extracted objects instead of rasterized output.
Machine-learned 3D Building Vectorization from Satellite Imagery
We propose a machine learning based approach for automatic 3D building reconstruction and vectorization.
Machine-learned Regularization and Polygonization of Building Segmentation Masks
We propose a machine learning based approach for automatic regularization and polygonization of building segmentation masks.
Map-Repair: Deep Cadastre Maps Alignment and Temporal Inconsistencies Fix in Satellite Images
In the fast developing countries it is hard to trace new buildings construction or old structures destruction and, as a result, to keep the up-to-date cadastre maps.
Regularization of Building Boundaries in Satellite Images using Adversarial and Regularized Losses
In this paper we present a method for building boundary refinement and regularization in satellite images using a fully convolutional neural network trained with a combination of adversarial and regularized losses.
