Accurate Polygonal Mapping of Buildings in Satellite Imagery

• URL: http://arxiv.org/abs/2208.00609v1
• Date: Mon, 1 Aug 2022 04:54:55 GMT
• Title: Accurate Polygonal Mapping of Buildings in Satellite Imagery
• Authors: Bowen Xu, Jiakun Xu, Nan Xue, Gui-Song Xia
• Abstract summary: This paper studies the problem of polygonal mapping of buildings by tackling the issue of mask reversibility. We propose a novel interaction mechanism of feature embedding sourced from different levels of supervision signals to obtain reversible building masks. We show that the learned reversible building masks take all the merits of the advances of deep convolutional neural networks for high-performing polygonal mapping of buildings.
• Score: 30.262871819346213
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper studies the problem of polygonal mapping of buildings by tackling the issue of mask reversibility that leads to a notable performance gap between the predicted masks and polygons from the learning-based methods. We addressed such an issue by exploiting the hierarchical supervision (of bottom-level vertices, mid-level line segments and the high-level regional masks) and proposed a novel interaction mechanism of feature embedding sourced from different levels of supervision signals to obtain reversible building masks for polygonal mapping of buildings. As a result, we show that the learned reversible building masks take all the merits of the advances of deep convolutional neural networks for high-performing polygonal mapping of buildings. In the experiments, we evaluated our method on the two public benchmarks of AICrowd and Inria. On the AICrowd dataset, our proposed method obtains unanimous improvements on the metrics of AP, APboundary and PoLiS. For the Inria dataset, our proposed method also obtains very competitive results on the metrics of IoU and Accuracy. The models and source code are available at https://github.com/SarahwXU.

Related papers

• MGFs: Masked Gaussian Fields for Meshing Building based on Multi-View Images [25.34182106938233]
  This paper proposes a novel framework, Masked Gaussian Fields (MGFs), designed to generate accurate surface reconstruction for building in a time-efficient way. The framework first applies EfficientSAM and COLMAP to generate multi-level masks of building and the corresponding masked point clouds. Comprehensive experiments on UAV images demonstrate that, compared to the traditional method and several NeRF-based and Gaussian-based SOTA solutions, our approach significantly improves both the accuracy and efficiency of building surface reconstruction.
  arXiv  Detail & Related papers  (2024-08-06T09:23:24Z)
• MaskInversion: Localized Embeddings via Optimization of Explainability Maps [49.50785637749757]
  MaskInversion generates a context-aware embedding for a query image region specified by a mask at test time. It can be used for a broad range of tasks, including open-vocabulary class retrieval, referring expression comprehension, as well as for localized captioning and image generation.
  arXiv  Detail & Related papers  (2024-07-29T14:21:07Z)
• Boosting Cross-Domain Point Classification via Distilling Relational Priors from 2D Transformers [59.0181939916084]
  Traditional 3D networks mainly focus on local geometric details and ignore the topological structure between local geometries. We propose a novel Priors Distillation (RPD) method to extract priors from the well-trained transformers on massive images. Experiments on the PointDA-10 and the Sim-to-Real datasets verify that the proposed method consistently achieves the state-of-the-art performance of UDA for point cloud classification.
  arXiv  Detail & Related papers  (2024-07-26T06:29:09Z)
• RoIPoly: Vectorized Building Outline Extraction Using Vertex and Logit Embeddings [5.093758132026397]
  We propose a novel query-based approach for extracting building outlines from aerial or satellite imagery. We formulate each polygon as a query and constrain the query attention on the most relevant regions of a potential building. We evaluate our method on the vectorized building outline extraction dataset CrowdAI and the 2D floorplan reconstruction dataset Structured3D.
  arXiv  Detail & Related papers  (2024-07-20T16:12:51Z)
• Enhancing Polygonal Building Segmentation via Oriented Corners [0.3749861135832072]
  This paper introduces a novel deep convolutional neural network named OriCornerNet, which directly extracts delineated building polygons from input images. Our approach involves a deep model that predicts building footprint masks, corners, and orientation vectors that indicate directions toward adjacent corners. Performance evaluations conducted on SpaceNet Vegas and CrowdAI-small datasets demonstrate the competitive efficacy of our approach.
  arXiv  Detail & Related papers  (2024-07-17T01:59:06Z)
• Mesh Denoising Transformer [104.5404564075393]
  Mesh denoising is aimed at removing noise from input meshes while preserving their feature structures. SurfaceFormer is a pioneering Transformer-based mesh denoising framework. New representation known as Local Surface Descriptor captures local geometric intricacies. Denoising Transformer module receives the multimodal information and achieves efficient global feature aggregation.
  arXiv  Detail & Related papers  (2024-05-10T15:27:43Z)
• MGMap: Mask-Guided Learning for Online Vectorized HD Map Construction [17.16231247910372]
  We propose MGMap, a mask-guided approach that effectively highlights the informative regions and achieves precise map element localization. Specifically, MGMap employs learned masks based on the enhanced multi-scale BEV features from two perspectives. Compared to the baselines, our proposed MGMap achieves a notable improvement of around 10 mAP for different input modalities.
  arXiv  Detail & Related papers  (2024-04-01T03:13:32Z)
• DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection [55.48770333927732]
  We propose a Difusion-based Anomaly Detection (DiAD) framework for multi-class anomaly detection. It consists of a pixel-space autoencoder, a latent-space Semantic-Guided (SG) network with a connection to the stable diffusion's denoising network, and a feature-space pre-trained feature extractor. Experiments on MVTec-AD and VisA datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-12-11T18:38:28Z)
• Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection [97.93062818228015]
  We propose to integrate the reconstruction-based functionality into a novel self-supervised predictive architectural building block. Our block is equipped with a loss that minimizes the reconstruction error with respect to the masked area in the receptive field. We demonstrate the generality of our block by integrating it into several state-of-the-art frameworks for anomaly detection on image and video.
  arXiv  Detail & Related papers  (2021-11-17T13:30:31Z)
• Image Inpainting by End-to-End Cascaded Refinement with Mask Awareness [66.55719330810547]
  Inpainting arbitrary missing regions is challenging because learning valid features for various masked regions is nontrivial. We propose a novel mask-aware inpainting solution that learns multi-scale features for missing regions in the encoding phase. Our framework is validated both quantitatively and qualitatively via extensive experiments on three public datasets.
  arXiv  Detail & Related papers  (2021-04-28T13:17:47Z)
• Machine-learned 3D Building Vectorization from Satellite Imagery [7.887221474814986]
  We propose a machine learning based approach for automatic 3D building reconstruction and vectorization. Taking a single-channel photogrammetric digital surface model (DSM) and panchromatic (PAN) image as input, we first filter out non-building objects and refine the building of shapes. The refined DSM and the input PAN image are then used through a semantic segmentation network to detect edges and corners of building roofs.
  arXiv  Detail & Related papers  (2021-04-13T19:57:30Z)

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.