Self-Supervised Learning for Building Damage Assessment from Large-scale xBD Satellite Imagery Benchmark Datasets

• URL: http://arxiv.org/abs/2205.15688v2
• Date: Wed, 1 Jun 2022 03:18:34 GMT
• Title: Self-Supervised Learning for Building Damage Assessment from Large-scale xBD Satellite Imagery Benchmark Datasets
• Authors: Zaishuo Xia, Zelin Li, Yanbing Bai, Jinze Yu, Bruno Adriano
• Abstract summary: We propose a self-supervised comparative learning approach to address the task without the requirement of labeled data. We constructed a novel asymmetric twin network architecture and tested its performance on the xBD dataset.
• Score: 3.2248805768155826
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In the field of post-disaster assessment, for timely and accurate rescue and localization after a disaster, people need to know the location of damaged buildings. In deep learning, some scholars have proposed methods to make automatic and highly accurate building damage assessments by remote sensing images, which are proved to be more efficient than assessment by domain experts. However, due to the lack of a large amount of labeled data, these kinds of tasks can suffer from being able to do an accurate assessment, as the efficiency of deep learning models relies highly on labeled data. Although existing semi-supervised and unsupervised studies have made breakthroughs in this area, none of them has completely solved this problem. Therefore, we propose adopting a self-supervised comparative learning approach to address the task without the requirement of labeled data. We constructed a novel asymmetric twin network architecture and tested its performance on the xBD dataset. Experiment results of our model show the improvement compared to baseline and commonly used methods. We also demonstrated the potential of self-supervised methods for building damage recognition awareness.

Related papers

• Towards Efficient Disaster Response via Cost-effective Unbiased Class Rate Estimation through Neyman Allocation Stratified Sampling Active Learning [11.697034536189094]
  We present an innovative algorithm that constructs Neyman stratified random sampling trees for binary classification. Our findings demonstrate that our method surpasses both passive and conventional active learning techniques. It effectively addresses the'sampling bias' challenge in traditional active learning strategies.
  arXiv  Detail & Related papers  (2024-05-28T01:34:35Z)
• Learning Efficient Unsupervised Satellite Image-based Building Damage Detection [43.06758527206676]
  Building Damage Detection (BDD) methods always require labour-intensive pixel-level annotations of buildings and their conditions. In this paper, we investigate a challenging yet practical scenario of U-BDD, where only unlabelled pre- and post-disaster satellite image pairs are provided. We present a novel self-supervised framework, U-BDD++, which improves upon the U-BDD baseline by addressing domain-specific issues associated with satellite imagery.
  arXiv  Detail & Related papers  (2023-12-04T02:20:35Z)
• Terrain-Informed Self-Supervised Learning: Enhancing Building Footprint Extraction from LiDAR Data with Limited Annotations [1.3243401820948064]
  Building footprint maps offer promise of precise footprint extraction without extensive post-processing. Deep learning methods face challenges in generalization and label efficiency. We propose terrain-aware self-supervised learning tailored to remote sensing.
  arXiv  Detail & Related papers  (2023-11-02T12:34:23Z)
• Re-thinking Data Availablity Attacks Against Deep Neural Networks [53.64624167867274]
  In this paper, we re-examine the concept of unlearnable examples and discern that the existing robust error-minimizing noise presents an inaccurate optimization objective. We introduce a novel optimization paradigm that yields improved protection results with reduced computational time requirements.
  arXiv  Detail & Related papers  (2023-05-18T04:03:51Z)
• Multi-view deep learning for reliable post-disaster damage classification [0.0]
  This study aims to enable more reliable automated post-disaster building damage classification using artificial intelligence (AI) and multi-view imagery. The proposed model is trained and validated on reconnaissance visual dataset containing expert-labeled, geotagged images of the inspected buildings following hurricane Harvey.
  arXiv  Detail & Related papers  (2022-08-06T01:04:13Z)
• Triggering Failures: Out-Of-Distribution detection by learning from local adversarial attacks in Semantic Segmentation [76.2621758731288]
  We tackle the detection of out-of-distribution (OOD) objects in semantic segmentation. Our main contribution is a new OOD detection architecture called ObsNet associated with a dedicated training scheme based on Local Adversarial Attacks (LAA) We show it obtains top performances both in speed and accuracy when compared to ten recent methods of the literature on three different datasets.
  arXiv  Detail & Related papers  (2021-08-03T17:09:56Z)
• Towards Reducing Labeling Cost in Deep Object Detection [61.010693873330446]
  We propose a unified framework for active learning, that considers both the uncertainty and the robustness of the detector. Our method is able to pseudo-label the very confident predictions, suppressing a potential distribution drift.
  arXiv  Detail & Related papers  (2021-06-22T16:53:09Z)
• Occlusion-aware Unsupervised Learning of Depth from 4-D Light Fields [50.435129905215284]
  We present an unsupervised learning-based depth estimation method for 4-D light field processing and analysis. Based on the basic knowledge of the unique geometry structure of light field data, we explore the angular coherence among subsets of the light field views to estimate depth maps. Our method can significantly shrink the performance gap between the previous unsupervised method and supervised ones, and produce depth maps with comparable accuracy to traditional methods with obviously reduced computational cost.
  arXiv  Detail & Related papers  (2021-06-06T06:19:50Z)
• Assessing out-of-domain generalization for robust building damage detection [78.6363825307044]
  Building damage detection can be automated by applying computer vision techniques to satellite imagery. Models must be robust to a shift in distribution between disaster imagery available for training and the images of the new event. We argue that future work should focus on the OOD regime instead.
  arXiv  Detail & Related papers  (2020-11-20T10:30:43Z)
• RescueNet: Joint Building Segmentation and Damage Assessment from Satellite Imagery [83.49145695899388]
  RescueNet is a unified model that can simultaneously segment buildings and assess the damage levels to individual buildings and can be trained end-to-end. RescueNet is tested on the large scale and diverse xBD dataset and achieves significantly better building segmentation and damage classification performance than previous methods.
  arXiv  Detail & Related papers  (2020-04-15T19:52:09Z)
• Building Disaster Damage Assessment in Satellite Imagery with Multi-Temporal Fusion [0.76146285961466]
  We report findings on problem framing, data processing and training procedures. Our insights lead to substantial improvement over the xBD baseline models. We score among top results on the xView2 challenge leaderboard.
  arXiv  Detail & Related papers  (2020-04-12T02:06:12Z)

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.