A coarse-to-fine boundary refinement network for building footprint extraction from remote sensing imagery

2022 ◽  
Vol 183 ◽  
pp. 240-252
Author(s):  
Haonan Guo ◽  
Bo Du ◽  
Liangpei Zhang ◽  
Xin Su
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Imagery ◽  
Building Footprint ◽  
Coarse To Fine ◽  
Boundary Refinement

scholarly journals Automated Processing of Remote Sensing Imagery Using Deep Semantic Segmentation: A Building Footprint Extraction Case

2020 ◽  
Vol 9 (8) ◽  
pp. 486 ◽  
Author(s):  
Aleksandar Milosavljević
Keyword(s):  
Remote Sensing ◽  
Semantic Segmentation ◽  
Aerial Image ◽  
Empirical Examination ◽  
Remote Sensing Imagery ◽  
Automated Processing ◽  
Building Footprint ◽  
Segmentation Task ◽  
Segmentation Models ◽  
Selection Of

The proliferation of high-resolution remote sensing sensors and platforms imposes the need for effective analyses and automated processing of high volumes of aerial imagery. The recent advance of artificial intelligence (AI) in the form of deep learning (DL) and convolutional neural networks (CNN) showed remarkable results in several image-related tasks, and naturally, gain the focus of the remote sensing community. In this paper, we focus on specifying the processing pipeline that relies on existing state-of-the-art DL segmentation models to automate building footprint extraction. The proposed pipeline is organized in three stages: image preparation, model implementation and training, and predictions fusion. For the first and third stages, we introduced several techniques that leverage remote sensing imagery specifics, while for the selection of the segmentation model, we relied on empirical examination. In the paper, we presented and discussed several experiments that we conducted on Inria Aerial Image Labeling Dataset. Our findings confirmed that automatic processing of remote sensing imagery using DL semantic segmentation is both possible and can provide applicable results. The proposed pipeline can be potentially transferred to any other remote sensing imagery segmentation task if the corresponding dataset is available.

scholarly journals A Coarse-to-Fine Contour Optimization Network for Extracting Building Instances from High-Resolution Remote Sensing Imagery

2021 ◽  
Vol 13 (19) ◽  
pp. 3814
Author(s):  
Fang Fang ◽  
Kaishun Wu ◽  
Yuanyuan Liu ◽  
Shengwen Li ◽  
Bo Wan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
State Of The Art ◽  
Complex Structures ◽  
Building Extraction ◽  
Remote Sensing Imagery ◽  
Feature Pyramid ◽  
Coarse To Fine ◽  
Original Feature

Building instances extraction is an essential task for surveying and mapping. Challenges still exist in extracting building instances from high-resolution remote sensing imagery mainly because of complex structures, variety of scales, and interconnected buildings. This study proposes a coarse-to-fine contour optimization network to improve the performance of building instance extraction. Specifically, the network contains two special sub-networks: attention-based feature pyramid sub-network (AFPN) and coarse-to-fine contour sub-network. The former sub-network introduces channel attention into each layer of the original feature pyramid network (FPN) to improve the identification of small buildings, and the latter is designed to accurately extract building contours via two cascaded contour optimization learning. Furthermore, the whole network is jointly optimized by multiple losses, that is, a contour loss, a classification loss, a box regression loss and a general mask loss. Experimental results on three challenging building extraction datasets demonstrated that the proposed method outperformed the state-of-the-art methods’ accuracy and quality of building contours.

scholarly journals A BOUNDARY AWARE NEURAL NETWORK FOR ROAD EXTRACTION FROM HIGH-RESOLUTION REMOTE SENSING IMAGERY

2020 ◽  
Vol V-3-2020 ◽  
pp. 67-73
Author(s):  
H. Sui ◽  
M. Zhou ◽  
M. Peng ◽  
N. Xiong
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Ground Truth ◽  
Road Extraction ◽  
Remote Sensing Imagery ◽  
Automatic Navigation ◽  
Road Maps ◽  
The Difference ◽  
Road Segmentation ◽  
Coarse To Fine

Abstract. Automatic road extraction from high-resolution remote sensing imagery has various applications like urban planning and automatic navigation. Existing methods for automatic road extraction however, focus on regional accuracy but not on the boundary quality. To address this problem, a Boundary-aware Road extraction Network (BARoadNet) is proposed. BARoadNet is a coarse-to-fine architecture composed of two encoder-to-decoder networks, a Coarse Map Predicting Module (CMPM) and Fine Map Predicting Module (FMPM). The CMPM learns to predict coarse road segmentation maps. The FMPM is used to refine the coarse road maps by learning the difference between the coarse road extraction result and the ground truth. Experiments are conducted on the open Massachusetts Road Dataset. Quantitative and qualitative analysis demonstrate that the proposed BARoadNet can improve the quality and accuracy of road extraction results compared with the state-of-the-art methods.

Estimations for Percentage of Impervious Area by the Use of Satellite Remote Sensing Imagery

1994 ◽  
Vol 29 (1-2) ◽  
pp. 135-144 ◽  
Author(s):  
C. Deguchi ◽  
S. Sugio
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Satellite Imagery ◽  
Satellite Remote Sensing ◽  
Aerial Photographs ◽  
Estimation Errors ◽  
Remote Sensing Imagery ◽  
Impervious Area ◽  
Urbanized Areas

This study aims to evaluate the applicability of satellite imagery in estimating the percentage of impervious area in urbanized areas. Two methods of estimation are proposed and applied to a small urbanized watershed in Japan. The area is considered under two different cases of subdivision; i.e., 14 zones and 17 zones. The satellite imageries of LANDSAT-MSS (Multi-Spectral Scanner) in 1984, MOS-MESSR(Multi-spectral Electronic Self-Scanning Radiometer) in 1988 and SPOT-HRV(High Resolution Visible) in 1988 are classified. The percentage of imperviousness in 17 zones is estimated by using these classification results. These values are compared with the ones obtained from the aerial photographs. The percent imperviousness derived from the imagery agrees well with those derived from aerial photographs. The estimation errors evaluated are less than 10%, the same as those obtained from aerial photographs.

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