Polygonal Building Extraction by Frame Field Learning

Girard, Nicolas; Smirnov, Dmitriy; Solomon, Justin; Tarabalka, Yuliya

doi:10.1109/cvpr46437.2021.00583

Cited by 81 publications

(74 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Typically, state of the art building extraction methods generate pixel-wise building segmentation. However, these building segments have to be converted into vector formats before they can be directly used by the mapping agencies (Girard et al, 2021). Manual identification and delineation of buildings from VHR remote sensing imagery is extremely time-consuming and unrealistic for large-scale datasets.…”

Section: Building Footprint Vectorizationmentioning

confidence: 99%

Application Oriented Quality Evaluation of Gaofen-7 Optical Stereo Satellite Imagery

Tian

Zhuo

Yuan

et al. 2022

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. GaoFen-7 (GF-7) satellite mission is further expanding the very high resolution 3D mapping application. Carrying the first civilian Chinese sub-meter resolution stereo satellite sensors, GF-7 satellite was launched on November 7, 2019. With 0.65 meter resolution on backward view and 0.8 meter resolution forward view, GF-7 has been designed to meet the demand of natural resource monitoring, land surveying, and other mapping applications in China. The use of GF-7 for 3D city reconstruction is unfortunately restricted by the fixed large stereo view angle of forward and backward cameras with +26 and −5 degrees respectively which is not optimal for dense stereo matching in urban regions. In this paper we intensively evaluate the quality of the GF-7 datasets by performing a series of urban monitoring applications, including road detection, building extraction and 3D reconstruction. In addition, we propose a 3D reconstruction workflow which uses the land cover classification result to refine the stereo matching result. Six sub-urban regions are selected from the available datasets in the middle of Germany. The results show that basic elements in urban scenes like buildings and roads could be detected from GF-7 datasets with high accuracy. With the proposed workflow, a 3D city model with a visually observed good quality can be delivered.

show abstract

Section: Building Footprint Vectorizationmentioning

confidence: 99%

Application Oriented Quality Evaluation of Gaofen-7 Optical Stereo Satellite Imagery

Tian

Zhuo

Yuan

et al. 2022

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…The conventional deep segmentation is often not able to produce sharp corners, which results in undesired artifacts. These methods need expensive and complicated post-processing procedures to refine the results (Girard et al, 2020). Due to these problems, traditional semantic segmentation methods are not able to produce accurate and regular buildings.…”

Section: Introductionmentioning

confidence: 99%

“…The PolyMapper could automatically delineate the building boundaries, but it performed worse on large buildings than Mask R-CNN (Li, Wegner, & Lucchi, 2019). Moreover, it could not deal with the polygons with holes (Girard, Smirnov, Solomon, & Tarabalka, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…However, these models face the challenge of producing precise and regular building outlines. Recently, a building delineation method based on frame field learning was proposed by Girard et al, (2020) to extract regular building footprints as vector polygons directly from aerial RGB images. An FCN is trained to learn simultaneously the building mask, contours, and frame field followed by a polygonization method.Optical imagery has some limitations.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Urbanization Detection Using LiDAR-Based Remote Sensing Images of Azad Kashmir Using Novel 3D CNNs

et al. 2022

View full text Add to dashboard Cite

An important measurable indicator of urbanization and its environmental implications has been identified as the urban impervious surface. It presents a strategy based on three-dimensional convolutional neural networks (3D CNNs) for extracting urbanization from the LiDAR datasets using deep learning technology. Various 3D CNN parameters are tested to see how they affect impervious surface extraction. For urban impervious surface delineation, this study investigates the synergistic integration of multiple remote sensing datasets of Azad Kashmir, State of Pakistan, to alleviate the restrictions imposed by single sensor data. Overall accuracy was greater than 95% and overall kappa value was greater than 90% in our suggested 3D CNN approach, which shows tremendous promise for impervious surface extraction. Because it uses multiscale convolutional processes to combine spatial and spectral information and texture and feature maps, we discovered that our proposed 3D CNN approach makes better use of urbanization than the commonly utilized pixel-based support vector machine classifier. In the fast-growing big data era, image analysis presents significant obstacles, yet our proposed 3D CNNs will effectively extract more urban impervious surfaces.

show abstract

“…Earth observation through satellites plays a vital role in understanding the world, and has attracted attention from a wide range of communities (Xia et al, 2018;Requena-Mesa et al, 2021;Girard et al, 2021). However, optical satellite images are often contaminated by clouds, which obstruct the view of the surface underneath, as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Potential of SAR Data for Cloud Removal in Optical Satellite Imagery

Fang¹,

Shi²,

Ebel³

et al. 2022

Preprint

View full text Add to dashboard Cite

The challenge of the cloud removal task can be alleviated with the aid of Synthetic Aperture Radar (SAR) images that can penetrate cloud cover. However, the large domain gap between optical and SAR images as well as the severe speckle noise of SAR images may cause significant interference in SAR-based cloud removal, resulting in performance degeneration. In this paper, we propose a novel global-local fusion based cloud removal (GLF-CR) algorithm to leverage the complementary information embedded in SAR images. Exploiting the power of SAR information to promote cloud removal entails two aspects. The first, global fusion, guides the relationship among all local optical windows to maintain the structure of the recovered region consistent with the remaining cloudfree regions. The second, local fusion, transfers complementary information embedded in the SAR image that corresponds to cloudy areas to generate reliable texture details of the missing regions, and uses dynamic filtering to alleviate the performance degradation caused by speckle noise. Extensive evaluation demonstrates that the proposed algorithm can yield high quality cloud-free images and performs favorably against state-of-the-art cloud removal algorithms.

show abstract

Polygonal Building Extraction by Frame Field Learning

Cited by 81 publications

References 28 publications

Application Oriented Quality Evaluation of Gaofen-7 Optical Stereo Satellite Imagery

Application Oriented Quality Evaluation of Gaofen-7 Optical Stereo Satellite Imagery

Urbanization Detection Using LiDAR-Based Remote Sensing Images of Azad Kashmir Using Novel 3D CNNs

Exploring the Potential of SAR Data for Cloud Removal in Optical Satellite Imagery

Contact Info

Product

Resources

About