Regularization of Building Boundaries in Satellite Images using Adversarial and Regularized Losses

2020-07-23 · Stefano Zorzi, Friedrich Fraundorfer

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. Compared to a pure Mask R-CNN model, the overall algorithm can achieve equivalent performance in terms of accuracy and completeness. However, unlike Mask R-CNN that produces irregular footprints, our framework generates regularized and visually pleasing building boundaries which are beneficial in many applications.

📄 PDF Abstract BibTeX arXiv:2007.11840

Code (0)

등록된 구현이 없습니다.

Methods 이 논문이 사용한 방법론

RPN A Region Proposal Network, or RPN, is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained…

Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…

Convolution A convolution is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the…

RoIAlign Region of Interest Align, or RoIAlign, is an operation for extracting a small feature map from each RoI in detection and segmentation based tasks. It removes the harsh…

Mask R-CNN Mask R-CNN extends Faster R-CNN to solve instance segmentation tasks. It achieves this by adding a branch for predicting an…

HPix: Generating Vector Maps from Satellite Images

2024-07-18 · Aditya Taparia, Keshab Nath

Vector maps find widespread utility across diverse domains due to their capacity to not only store but also represent discrete data boundaries such as building footprints, disaster impact analysis, digitization, urban pl…

A Novel Adaptive Deep Network for Building Footprint Segmentation

2021-02-27 · A. Ziaee, R. Dehbozorgi, M. Döller

Building footprint segmentations for high resolution images are increasingly demanded for many remote sensing applications. By the emerging deep learning approaches, segmentation networks have made significant advances i…

Segmentation Semantic Segmentation

Machine-learned Regularization and Polygonization of Building Segmentation Masks

2020-07-24 · Stefano Zorzi, Ksenia Bittner, Friedrich Fraundorfer

We propose a machine learning based approach for automatic regularization and polygonization of building segmentation masks. Taking an image as input, we first predict building segmentation maps exploiting generic fully …

Generative Adversarial Network Segmentation

Building Footprint Generation Using Improved Generative Adversarial Networks

2018-10-26 · Yilei Shi, Qingyu Li, Xiao Xiang Zhu

Building footprint information is an essential ingredient for 3-D reconstruction of urban models. The automatic generation of building footprints from satellite images presents a considerable challenge due to the complex…

Runway Extraction and Improved Mapping from Space Imagery

2021-12-30 · David A. Noever

Change detection methods applied to monitoring key infrastructure like airport runways represent an important capability for disaster relief and urban planning. The present work identifies two generative adversarial netw…

Change Detection Style Transfer