Boundary Regularized Building Footprint Extraction From Satellite Images Using Deep Neural Network

06/23/2020
by   Kang Zhao, et al.
3

In recent years, an ever-increasing number of remote satellites are orbiting the Earth which streams vast amount of visual data to support a wide range of civil, public and military applications. One of the key information obtained from satellite imagery is to produce and update spatial maps of built environment due to its wide coverage with high resolution data. However, reconstructing spatial maps from satellite imagery is not a trivial vision task as it requires reconstructing a scene or object with high-level representation such as primitives. For the last decade, significant advancement in object detection and representation using visual data has been achieved, but the primitive-based object representation still remains as a challenging vision task. Thus, a high-quality spatial map is mainly produced through complex labour-intensive processes. In this paper, we propose a novel deep neural network, which enables to jointly detect building instance and regularize noisy building boundary shapes from a single satellite imagery. The proposed deep learning method consists of a two-stage object detection network to produce region of interest (RoI) features and a building boundary extraction network using graph models to learn geometric information of the polygon shapes. Extensive experiments show that our model can accomplish multi-tasks of object localization, recognition, semantic labelling and geometric shape extraction simultaneously. In terms of building extraction accuracy, computation efficiency and boundary regularization performance, our model outperforms the state-of-the-art baseline models.

READ FULL TEXT
research
10/14/2020

PP-LinkNet: Improving Semantic Segmentation of High Resolution Satellite Imagery with Multi-stage Training

Road network and building footprint extraction is essential for many app...
research
04/21/2021

Rapid Detection of Aircrafts in Satellite Imagery based on Deep Neural Networks

Object detection is one of the fundamental objectives in Applied Compute...
research
07/26/2021

SaRNet: A Dataset for Deep Learning Assisted Search and Rescue with Satellite Imagery

Access to high resolution satellite imagery has dramatically increased i...
research
07/23/2020

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

In this paper we present a method for building boundary refinement and r...
research
02/21/2016

Automatic Building Extraction in Aerial Scenes Using Convolutional Networks

Automatic building extraction from aerial and satellite imagery is highl...
research
10/04/2021

Automated Aerial Animal Detection When Spatial Resolution Conditions Are Varied

Knowing where livestock are located enables optimized management and mus...
research
02/05/2020

Generating Interpretable Poverty Maps using Object Detection in Satellite Images

Accurate local-level poverty measurement is an essential task for govern...

Please sign up or login with your details

Forgot password? Click here to reset