Smart City Gnosys

Smart city article details

Title	Egafnet: An Edge Guidance And Scale-Aware Adaptive Fusion Network For Building Extraction From Remote Sensing Images
ID_Doc	22451
Authors	Yang M.; Zhao L.; Ye L.; Jia W.; Jiang H.; Yang Z.
Year	2025
Published	IEEE Transactions on Geoscience and Remote Sensing, 63
DOI	http://dx.doi.org/10.1109/TGRS.2024.3524547
Abstract	Accurately extracting buildings from high-resolution remote sensing images is crucial for urban planning, smart city construction, map updating, and other fields. However, the buildings extracted from remote sensing images still have deficiencies such as fuzzy boundaries and poor integrity due to the occlusion of trees and shadows, the interference of redundant information, and the characteristics of buildings with different shapes and scales. In this article, a novel building extraction model edge guidance adaptive fusion network (EGAFNet) is proposed for remote sensing images, which utilizes EfficientNetV2 as the encoder and multiscale feature enhancement module (MFEM) as the decoder to improve the accuracy and efficiency. To address the issue of missing boundary information, a branch for extract boundary features and an edge guidance module (EGM) are constructed to enhance the network's ability to express the boundary. In addition, a scale-aware adaptive fusion module (SAFM) is designed to adaptively aggregate the multiscale features to enhance the network's ability to capture the different scale features. We also adopt a multilayer supervision strategy that predicts the outputs for each layer of the decoding phase and imposes multilevel loss constraints to achieve fast fitting of the model. Experiments are conducted on the WHU building dataset and a dataset of building instances of typical cities in China. The experimental results demonstrate that the EGAFNet achieves the best F1 -score and intersection over union (IoU) compared with other typical building extraction methods. It suggests that EGAFNet can effectively improve the learning ability of the boundary information and enhance the multiscale building feature representation, thus realizing more accurate building extraction. The code will be available at https://github.com/Mw-yang/EGAFNet. © 2025 IEEE.
Author Keywords	Building extraction; deep learning; edge guidance; multiscale features; remote sensing images

Similar Articles

Id	Similarity	Authors	Title	Published
36116	0.88	Yang X.	Maf-Unet: Building Extraction Algorithm Based On Improved U-Net Network	2024 3rd International Conference on Artificial Intelligence, Internet of Things and Cloud Computing Technology, AIoTC 2024 (2024)
12803	0.878	Hu A.; Wu L.; Chen S.; Xu Y.; Wang H.; Xie Z.	Boundary Shape-Preserving Model For Building Mapping From High-Resolution Remote Sensing Images	IEEE Transactions on Geoscience and Remote Sensing, 61 (2023)
11750	0.877	Lin H.; Hao M.; Luo W.; Yu H.; Zheng N.	Bearnet: A Novel Buildings Edge-Aware Refined Network For Building Extraction From High-Resolution Remote Sensing Images	IEEE Geoscience and Remote Sensing Letters, 20 (2023)
24343	0.874	Lu Q.; Liu W.; Zhang L.; Li E.; Qiu J.; Wang L.	Essegnext:A Novel Building Extraction Framework Based On Generalized Samgeo And Segnext Models Using High-Resolution Remote Sensing Images	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2025)
38966	0.873	Zhou Y.; Jiang W.; Wang B.	Nesf-Net: Building Roof And Facade Segmentation Based On Neighborhood Relationship Awareness And Scale-Frequency Modulation Network For High-Resolution Remote Sensing Images	ISPRS Journal of Photogrammetry and Remote Sensing, 226 (2025)
47466	0.873	Pan X.; Xu K.; Yang S.; Liu Y.; Zhang R.; He P.	Sda-Net: A Spatially Optimized Dual-Stream Network With Adaptive Global Attention For Building Extraction In Multi-Modal Remote Sensing Images	Sensors, 25, 7 (2025)
2888	0.862	Zhang X.; Li X.; Huang J.; Li E.; Liu W.; Zhang L.	A Multifeature Fusion Framework Based On D-S Theory For Automatic Building Extraction From High-Resolution Remote Sensing Imagery	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17 (2024)
2916	0.86	Yin J.; Wu F.; Qiu Y.; Li A.; Liu C.; Gong X.	A Multiscale And Multitask Deep Learning Framework For Automatic Building Extraction	Remote Sensing, 14, 19 (2022)
13075	0.856	Han Z.; Li X.; Wang X.; Wu Z.; Liu J.	Building Segmentation In Urban And Rural Areas With Mfa-Net: A Multidimensional Feature Adjustment Approach	Sensors, 25, 8 (2025)
3245	0.855	Hussain Z.K.; Congshi J.; Adrees M.; Chaudhary H.; Shafqat R.	A Novel Architecture For Building Rooftop Extraction Using Remote Sensing And Deep Learning	Remote Sensing Applications: Society and Environment, 38 (2025)