Building Extraction from Remote Sensing Images via an Uncertainty-Aware Network

• URL: http://arxiv.org/abs/2307.12309v1
• Date: Sun, 23 Jul 2023 12:42:15 GMT
• Title: Building Extraction from Remote Sensing Images via an Uncertainty-Aware Network
• Authors: Wei He, Jiepan Li, Weinan Cao, Liangpei Zhang, Hongyan Zhang
• Abstract summary: Building extraction plays an essential role in many applications, such as city planning and urban dynamic monitoring. We propose a novel and straightforward Uncertainty-Aware Network (UANet) to alleviate this problem. Results demonstrate that the proposed UANet outperforms other state-of-the-art algorithms by a large margin.
• Score: 18.365220543556113
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Building extraction aims to segment building pixels from remote sensing images and plays an essential role in many applications, such as city planning and urban dynamic monitoring. Over the past few years, deep learning methods with encoder-decoder architectures have achieved remarkable performance due to their powerful feature representation capability. Nevertheless, due to the varying scales and styles of buildings, conventional deep learning models always suffer from uncertain predictions and cannot accurately distinguish the complete footprints of the building from the complex distribution of ground objects, leading to a large degree of omission and commission. In this paper, we realize the importance of uncertain prediction and propose a novel and straightforward Uncertainty-Aware Network (UANet) to alleviate this problem. To verify the performance of our proposed UANet, we conduct extensive experiments on three public building datasets, including the WHU building dataset, the Massachusetts building dataset, and the Inria aerial image dataset. Results demonstrate that the proposed UANet outperforms other state-of-the-art algorithms by a large margin.

Related papers

• Innovative Horizons in Aerial Imagery: LSKNet Meets DiffusionDet for Advanced Object Detection [55.2480439325792]
  We present an in-depth evaluation of an object detection model that integrates the LSKNet backbone with the DiffusionDet head. The proposed model achieves a mean average precision (MAP) of approximately 45.7%, which is a significant improvement. This advancement underscores the effectiveness of the proposed modifications and sets a new benchmark in aerial image analysis.
  arXiv  Detail & Related papers  (2023-11-21T19:49:13Z)
• TFNet: Tuning Fork Network with Neighborhood Pixel Aggregation for Improved Building Footprint Extraction [11.845097068829551]
  We propose a novel tuning Fork Network (TFNet) design for deep semantic segmentation. The TFNet design is coupled with a novel methodology of incorporating neighborhood information at the tile boundaries during the training process. For performance comparisons, we utilize the SpaceNet2 and WHU datasets, as well as a dataset from an area in Lahore, Pakistan that captures closely connected buildings.
  arXiv  Detail & Related papers  (2023-11-05T10:52:16Z)
• Performance Analysis of Various EfficientNet Based U-Net++ Architecture for Automatic Building Extraction from High Resolution Satellite Images [0.0]
  Building extraction heavily relies on semantic segmentation of high-resolution remote sensing imagery. Various efficientNet backbone based U-Net++ has been proposed in this study. According on the experimental findings, the suggested model significantly outperforms previous cutting-edge approaches.
  arXiv  Detail & Related papers  (2023-09-05T18:14:14Z)
• MetaGraspNet: A Large-Scale Benchmark Dataset for Scene-Aware Ambidextrous Bin Picking via Physics-based Metaverse Synthesis [72.85526892440251]
  We introduce MetaGraspNet, a large-scale photo-realistic bin picking dataset constructed via physics-based metaverse synthesis. The proposed dataset contains 217k RGBD images across 82 different article types, with full annotations for object detection, amodal perception, keypoint detection, manipulation order and ambidextrous grasp labels for a parallel-jaw and vacuum gripper. We also provide a real dataset consisting of over 2.3k fully annotated high-quality RGBD images, divided into 5 levels of difficulties and an unseen object set to evaluate different object and layout properties.
  arXiv  Detail & Related papers  (2022-08-08T08:15:34Z)
• BuildFormer: Automatic building extraction with vision transformer [7.577142111447444]
  We propose a novel transformer-based network for extracting buildings from fine-resolution remote sensing images, namely BuildFormer. In Comparision with the ResNet, the proposed method achieves an improvement of 2% in mIoU on the WHU building dataset.
  arXiv  Detail & Related papers  (2021-11-29T11:23:52Z)
• Salient Objects in Clutter [130.63976772770368]
  This paper identifies and addresses a serious design bias of existing salient object detection (SOD) datasets. This design bias has led to a saturation in performance for state-of-the-art SOD models when evaluated on existing datasets. We propose a new high-quality dataset and update the previous saliency benchmark.
  arXiv  Detail & Related papers  (2021-05-07T03:49:26Z)
• Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges [124.48654341780431]
  We present a large-scale dataset of Object deTection in Aerial images (DOTA) and comprehensive baselines for ODAI. The proposed DOTA dataset contains 1,793,658 object instances of 18 categories of oriented-bounding-box annotations collected from 11,268 aerial images. We build baselines covering 10 state-of-the-art algorithms with over 70 configurations, where the speed and accuracy performances of each model have been evaluated.
  arXiv  Detail & Related papers  (2021-02-24T11:20:55Z)
• CG-Net: Conditional GIS-aware Network for Individual Building Segmentation in VHR SAR Images [25.87229252642239]
  This paper addresses the issue of individual building segmentation from a single VHR SAR image in large-scale urban areas. We introduce building footprints from GIS data as complementary information and propose a novel conditional GIS-aware network (CG-Net) The proposed model learns multi-level visual features and employs building footprints to normalize the features for predicting building masks in the SAR image.
  arXiv  Detail & Related papers  (2020-11-17T01:52:22Z)
• Counting from Sky: A Large-scale Dataset for Remote Sensing Object Counting and A Benchmark Method [52.182698295053264]
  We are interested in counting dense objects from remote sensing images. Compared with object counting in a natural scene, this task is challenging in the following factors: large scale variation, complex cluttered background, and orientation arbitrariness. To address these issues, we first construct a large-scale object counting dataset with remote sensing images, which contains four important geographic objects. We then benchmark the dataset by designing a novel neural network that can generate a density map of an input image.
  arXiv  Detail & Related papers  (2020-08-28T03:47:49Z)
• Boundary Regularized Building Footprint Extraction From Satellite Images Using Deep Neural Network [6.371173732947292]
  We propose a novel deep neural network, which enables to jointly detect building instance and regularize noisy building boundary shapes from a single satellite imagery. Our model can accomplish multi-tasks of object localization, recognition, semantic labelling and geometric shape extraction simultaneously.
  arXiv  Detail & Related papers  (2020-06-23T17:24:09Z)
• Counting dense objects in remote sensing images [52.182698295053264]
  Estimating number of interested objects from a given image is a challenging yet important task. In this paper, we are interested in counting dense objects from remote sensing images. To address these issues, we first construct a large-scale object counting dataset based on remote sensing images. We then benchmark the dataset by designing a novel neural network which can generate density map of an input image.
  arXiv  Detail & Related papers  (2020-02-14T09:13:54Z)

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.