Smart City Gnosys
Smart city article details
| Title | Nesf-Net: Building Roof And Facade Segmentation Based On Neighborhood Relationship Awareness And Scale-Frequency Modulation Network For High-Resolution Remote Sensing Images |
|---|---|
| ID_Doc | 38966 |
| Authors | Zhou Y.; Jiang W.; Wang B. |
| Year | 2025 |
| Published | ISPRS Journal of Photogrammetry and Remote Sensing, 226 |
| DOI | http://dx.doi.org/10.1016/j.isprsjprs.2025.05.025 |
| Abstract | Building information extraction holds significant application value in smart city development, urban planning, and management. With the accelerating process of urbanization, mid- and high-rise buildings are increasingly prevalent. In orthophotos, the roofs of tall buildings often do not fully overlap with their footprints. In satellite images from oblique angles, buildings may also be obstructed or affected by shadows. Therefore, building information extraction should evolve from a roof-only extraction task to a comprehensive task that includes both roofs and facades. Current methods predominantly employ convolutional neural networks (CNNs) and Transformer models, focusing on describing building boundary and global features. However, these methods have the following limitations: insufficient utilization of information between pixels and limited spatial information recovery capabilities in decoders. This makes it difficult to distinguish between roofs and facades, and the morphological structure of buildings is challenging to maintain. To address these issues, this paper proposes a new network architecture—NeSF-Net, designed to focus on the accurate extraction of roofs and facades. NeSF-Net consists of two core modules: the neighborhood relationship awareness module (NRAM) and the scale-frequency modulation decoder (SFMD). NRAM enhances the connectivity between pixels by constructing sub-neighborhood relationship awareness in the latent space of deep features, effectively improving the integrity of the segmentation results. SFMD significantly reduces the loss of spatial information during the upsampling process by thoroughly extracting and integrating the scale and frequency features of buildings in the decoder. Experiments were conducted on the BANDON dataset, which contains images captured from oblique angles. The proposed method achieved a mIoU of 72.71 % and an F1 score of 83.04 %, outperforming state-of-the-art segmentation methods. The performance in facade extraction was particularly notable, with a mIoU score exceeding the second-best method by 4.92 %. Additionally, generalization experiments were conducted using GaoFen-7 satellite images, taking Shenzhen as a case study. The results demonstrate that the proposed method exhibits good generalization and robustness. © 2025 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) |
| Author Keywords | Building facade; Building roof; Frequency; Multi-scale; Neighborhood perception |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
12803  | 0.888 | 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) |
| 4137 | 0.886 | Yang M.; Zhao L.; Ye L.; Jiang H.; Yang Z. | A Review Of Convolutional Neural Networks Related Methods For Building Extraction From Remote Sensing Images; [基于卷积神经网络的遥感影像建筑物提取方法综述] | Journal of Geo-Information Science, 26, 6 (2024) |
| 3245 | 0.886 | 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) |
| 36116 | 0.883 | 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) |
| 13075 | 0.882 | 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) |
| 11750 | 0.88 | 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) |
| 102 | 0.879 | Yu D.; Ji S.; Wei S.; Khoshelham K. | 3-D Building Instance Extraction From High-Resolution Remote Sensing Images And Dsm With An End-To-End Deep Neural Network | IEEE Transactions on Geoscience and Remote Sensing, 62 (2024) |
| 24343 | 0.878 | 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) |
| 47466 | 0.877 | 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) |
| 13030 | 0.875 | Zhang Y.; Wang T.; Lin X.; Zhao Z.; Wang X. | Building Extraction From Lidar Point Clouds Based On Revised Randla-Net | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 48, 1 (2024) |