Fine-Grained Building Function Recognition from Street-View Images via Geometry-Aware Semi-Supervised Learning

• URL: http://arxiv.org/abs/2408.09460v3
• Date: Mon, 9 Sep 2024 03:21:53 GMT
• Title: Fine-Grained Building Function Recognition from Street-View Images via Geometry-Aware Semi-Supervised Learning
• Authors: Weijia Li, Jinhua Yu, Dairong Chen, Yi Lin, Runmin Dong, Xiang Zhang, Conghui He, Haohuan Fu,
• Abstract summary: We propose a geometry-aware semi-supervised framework for fine-grained building function recognition. We use geometric relationships among multi-source data to enhance pseudo-label accuracy in semi-supervised learning. Our proposed framework exhibits superior performance in fine-grained functional recognition of buildings.
• Score: 18.432786227782803
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we propose a geometry-aware semi-supervised framework for fine-grained building function recognition, utilizing geometric relationships among multi-source data to enhance pseudo-label accuracy in semi-supervised learning, broadening its applicability to various building function categorization systems. Firstly, we design an online semi-supervised pre-training stage, which facilitates the precise acquisition of building facade location information in street-view images. In the second stage, we propose a geometry-aware coarse annotation generation module. This module effectively combines GIS data and street-view data based on the geometric relationships, improving the accuracy of pseudo annotations. In the third stage, we combine the newly generated coarse annotations with the existing labeled dataset to achieve fine-grained functional recognition of buildings across multiple cities at a large scale. Extensive experiments demonstrate that our proposed framework exhibits superior performance in fine-grained functional recognition of buildings. Within the same categorization system, it achieves improvements of 7.6\% and 4.8\% compared to fully-supervised methods and state-of-the-art semi-supervised methods, respectively. Additionally, our method also performs well in cross-city scenarios, i.e., extending the model trained on OmniCity (New York) to new cities (i.e., Los Angeles and Boston) with different building function categorization systems. This study offers a new solution for large-scale multi-city applications with minimal annotation requirements, facilitating more efficient data updates and resource allocation in urban management.

Related papers

• Multi-Unit Floor Plan Recognition and Reconstruction Using Improved Semantic Segmentation of Raster-Wise Floor Plans [1.0436971860292366]
  We propose two novel pixel-wise segmentation methods based on the MDA-Unet and MACU-Net architectures. The proposed methods are compared with two other state-of-the-art techniques and several benchmark datasets. On the commonly used CubiCasa benchmark dataset, our methods have achieved the mean F1 score of 0.86 over five examined classes.
  arXiv  Detail & Related papers  (2024-08-02T18:36:45Z)
• IsUMap: Manifold Learning and Data Visualization leveraging Vietoris-Rips filtrations [0.08796261172196743]
  We present a systematic and detailed construction of a metric representation for locally distorted metric spaces. Our approach addresses limitations in existing methods by accommodating non-uniform data distributions and intricate local geometries.
  arXiv  Detail & Related papers  (2024-07-25T07:46:30Z)
• Semi-supervised Learning from Street-View Images and OpenStreetMap for Automatic Building Height Estimation [59.6553058160943]
  We propose a semi-supervised learning (SSL) method of automatically estimating building height from Mapillary SVI and OpenStreetMap data. The proposed method leads to a clear performance boosting in estimating building heights with a Mean Absolute Error (MAE) around 2.1 meters. The preliminary result is promising and motivates our future work in scaling up the proposed method based on low-cost VGI data.
  arXiv  Detail & Related papers  (2023-07-05T18:16:30Z)
• Cross-view Geo-localization via Learning Disentangled Geometric Layout Correspondence [11.823147814005411]
  Cross-view geo-localization aims to estimate the location of a query ground image by matching it to a reference geo-tagged aerial images database. Recent works achieve outstanding progress on cross-view geo-localization benchmarks. However, existing methods still suffer from poor performance on the cross-area benchmarks.
  arXiv  Detail & Related papers  (2022-12-08T04:54:01Z)
• Retrieval and Localization with Observation Constraints [12.010135672015704]
  We propose an integrated visual re-localization method called RLOCS. It combines image retrieval, semantic consistency and geometry verification to achieve accurate estimations. Our method achieves many performance improvements on the challenging localization benchmarks.
  arXiv  Detail & Related papers  (2021-08-19T06:14:33Z)
• FloorLevel-Net: Recognizing Floor-Level Lines with Height-Attention-Guided Multi-task Learning [49.30194762653723]
  This work tackles the problem of locating floor-level lines in street-view images, using a supervised deep learning approach. We first compile a new dataset and develop a new data augmentation scheme to synthesize training samples. Next, we design FloorLevel-Net, a multi-task learning network that associates explicit features of building facades and implicit floor-level lines.
  arXiv  Detail & Related papers  (2021-07-06T08:17:59Z)
• Semi-Supervised Domain Generalization with Stochastic StyleMatch [90.98288822165482]
  In real-world applications, we might have only a few labels available from each source domain due to high annotation cost. In this work, we investigate semi-supervised domain generalization, a more realistic and practical setting. Our proposed approach, StyleMatch, is inspired by FixMatch, a state-of-the-art semi-supervised learning method based on pseudo-labeling.
  arXiv  Detail & Related papers  (2021-06-01T16:00:08Z)
• Edge-assisted Democratized Learning Towards Federated Analytics [67.44078999945722]
  We show the hierarchical learning structure of the proposed edge-assisted democratized learning mechanism, namely Edge-DemLearn. We also validate Edge-DemLearn as a flexible model training mechanism to build a distributed control and aggregation methodology in regions.
  arXiv  Detail & Related papers  (2020-12-01T11:46:03Z)
• Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scene [76.4183572058063]
  We present a richly-annotated 3D point cloud dataset for multiple outdoor scene understanding tasks. The dataset has been point-wisely annotated with both hierarchical and instance-based labels. We formulate a hierarchical learning problem for 3D point cloud segmentation and propose a measurement evaluating consistency across various hierarchies.
  arXiv  Detail & Related papers  (2020-08-11T19:10:32Z)
• Mix Dimension in Poincar\'{e} Geometry for 3D Skeleton-based Action Recognition [57.98278794950759]
  Graph Convolutional Networks (GCNs) have already demonstrated their powerful ability to model the irregular data. We present a novel spatial-temporal GCN architecture which is defined via the Poincar'e geometry. We evaluate our method on two current largest scale 3D datasets.
  arXiv  Detail & Related papers  (2020-07-30T18:23:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.