Automating global landslide detection with heterogeneous ensemble deep-learning classification

• URL: http://arxiv.org/abs/2310.05959v1
• Date: Tue, 12 Sep 2023 10:56:16 GMT
• Title: Automating global landslide detection with heterogeneous ensemble deep-learning classification
• Authors: Alexandra Jarna Ganer{\o}d, Gabriele Franch, Erin Lindsay, Martina Calovi
• Abstract summary: Landslides threaten infrastructure, including roads, railways, buildings, and human life. Hazard-based spatial planning and early warning systems are cost-effective strategies to reduce the risk to society from landslides. Deep learning models have recently been applied for landside mapping using medium- to high-resolution satellite images as input.
• Score: 44.99833362998488
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With changing climatic conditions, we are already seeing an increase in extreme weather events and their secondary consequences, including landslides. Landslides threaten infrastructure, including roads, railways, buildings, and human life. Hazard-based spatial planning and early warning systems are cost-effective strategies to reduce the risk to society from landslides. However, these both rely on data from previous landslide events, which is often scarce. Many deep learning (DL) models have recently been applied for landside mapping using medium- to high-resolution satellite images as input. However, they often suffer from sensitivity problems, overfitting, and low mapping accuracy. This study addresses some of these limitations by using a diverse global landslide dataset, using different segmentation models, such as Unet, Linknet, PSP-Net, PAN, and DeepLab and based on their performances, building an ensemble model. The ensemble model achieved the highest F1-score (0.69) when combining both Sentinel-1 and Sentinel-2 bands, with the highest average improvement of 6.87 % when the ensemble size was 20. On the other hand, Sentinel-2 bands only performed very well, with an F1 score of 0.61 when the ensemble size is 20 with an improvement of 14.59 % when the ensemble size is 20. This result shows considerable potential in building a robust and reliable monitoring system based on changes in vegetation index dNDVI only.

Related papers

• SOOD++: Leveraging Unlabeled Data to Boost Oriented Object Detection [59.868772767818975]
  We propose a simple yet effective Semi-supervised Oriented Object Detection method termed SOOD++. Specifically, we observe that objects from aerial images are usually arbitrary orientations, small scales, and aggregation. Extensive experiments conducted on various multi-oriented object datasets under various labeled settings demonstrate the effectiveness of our method.
  arXiv  Detail & Related papers  (2024-07-01T07:03:51Z)
• Landslide mapping from Sentinel-2 imagery through change detection [0.12289361708127873]
  Landslides are one of the most critical and destructive geohazards. In this paper, we explore methodologies to map newly-occurred landslides using Sentinel-2 imagery automatically. We introduce a novel deep learning architecture for fusing Sentinel-2 bi-temporal image pairs with Digital Elevation Model (DEM) data.
  arXiv  Detail & Related papers  (2024-05-30T15:33:32Z)
• UniMix: Towards Domain Adaptive and Generalizable LiDAR Semantic Segmentation in Adverse Weather [55.95708988160047]
  LiDAR semantic segmentation (LSS) is a critical task in autonomous driving. Prior LSS methods are investigated and evaluated on datasets within the same domain in clear weather. We propose UniMix, a universal method that enhances the adaptability and generalizability of LSS models.
  arXiv  Detail & Related papers  (2024-04-08T02:02:15Z)
• Scaling Data Generation in Vision-and-Language Navigation [116.95534559103788]
  We propose an effective paradigm for generating large-scale data for learning. We apply 1200+ photo-realistic environments from HM3D and Gibson datasets and synthesizes 4.9 million instruction trajectory pairs. Thanks to our large-scale dataset, the performance of an existing agent can be pushed up (+11% absolute with regard to previous SoTA) to a significantly new best of 80% single-run success rate on the R2R test split by simple imitation learning.
  arXiv  Detail & Related papers  (2023-07-28T16:03:28Z)
• DAM-Net: Global Flood Detection from SAR Imagery Using Differential Attention Metric-Based Vision Transformers [22.885444177106873]
  Detection of flooded areas using high-resolution synthetic aperture radar (SAR) imagery is a critical task with applications in crisis and disaster management. To address this issue, we propose a novel differential attention metric-based network (DAM-Net) in this study. The DAM-Net comprises two key components: a weight-sharing Siamese backbone to obtain multi-scale change features of multi-temporal images and tokens containing high-level semantic information of water-body changes.
  arXiv  Detail & Related papers  (2023-06-01T14:12:33Z)
• t-RAIN: Robust generalization under weather-aliasing label shift attacks [0.0]
  We analyze the impact of label shift on the task of multi-weather classification for autonomous vehicles. We propose t-RAIN a similarity mapping technique for synthetic data augmentation using large scale generative models. We present state-of-the-art pedestrian detection results on real and synthetic weather domains with best performing 82.69 AP (snow) and 62.31 AP (fog) respectively.
  arXiv  Detail & Related papers  (2023-05-15T02:05:56Z)
• The Second Monocular Depth Estimation Challenge [93.1678025923996]
  The second edition of the Monocular Depth Estimation Challenge (MDEC) was open to methods using any form of supervision. The challenge was based around the SYNS-Patches dataset, which features a wide diversity of environments with high-quality dense ground-truth. The top supervised submission improved relative F-Score by 27.62%, while the top self-supervised improved it by 16.61%.
  arXiv  Detail & Related papers  (2023-04-14T11:10:07Z)
• Benchmarking the Physical-world Adversarial Robustness of Vehicle Detection [14.202833467294765]
  Adversarial attacks in the physical world can harm the robustness of detection models. Yolo v6 had strongest resistance, with only a 6.59% average AP drop, and ASA was the most effective attack algorithm with a 14.51% average AP reduction.
  arXiv  Detail & Related papers  (2023-04-11T09:48:25Z)
• Relict landslide detection using Deep-Learning architectures for image segmentation in rainforest areas: A new framework [0.5735035463793008]
  A new CNN framework is proposed for semi-automatic detection of relict landslides. A total of 42 combinations of CNNs are tested. Predictions of the proposed framework were more accurate and correctly detected more landslides.
  arXiv  Detail & Related papers  (2022-08-04T14:46:02Z)
• SODA10M: Towards Large-Scale Object Detection Benchmark for Autonomous Driving [94.11868795445798]
  We release a Large-Scale Object Detection benchmark for Autonomous driving, named as SODA10M, containing 10 million unlabeled images and 20K images labeled with 6 representative object categories. To improve diversity, the images are collected every ten seconds per frame within 32 different cities under different weather conditions, periods and location scenes. We provide extensive experiments and deep analyses of existing supervised state-of-the-art detection models, popular self-supervised and semi-supervised approaches, and some insights about how to develop future models.
  arXiv  Detail & Related papers  (2021-06-21T13:55:57Z)

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.