A Multi-Source Data Fusion-based Semantic Segmentation Model for Relic Landslide Detection

• URL: http://arxiv.org/abs/2308.01251v3
• Date: Wed, 04 Dec 2024 01:52:57 GMT
• Title: A Multi-Source Data Fusion-based Semantic Segmentation Model for Relic Landslide Detection
• Authors: Yiming Zhou, Yuexing Peng, Junchuan Yu, Daqing Ge, Wei Xiang,
• Abstract summary: The proposed HPCL-Net is evaluated on the Loess Plateau relic landslide dataset. The proposed HPCL-Net greatly outperforms existing models, where the mIoU is increased from 0.620 to 0.651, the Landslide IoU is improved from 0.334 to 0.394 and the F1score is enhanced from 0.501 to 0.565.
• Score: 7.095295046737303
• License:
• Abstract: As a natural disaster, landslide often brings tremendous losses to human lives, so it urgently demands reliable detection of landslide risks. When detecting relic landslides that present important information for landslide risk warning, problems such as visual blur and small-sized dataset cause great challenges when using remote sensing images. To extract accurate semantic features, a hyper-pixel-wise contrastive learning augmented segmentation network (HPCL-Net) is proposed, which augments the local salient feature extraction from boundaries of landslides through HPCL and fuses heterogeneous information in the semantic space from high-resolution remote sensing images and digital elevation model data. For full utilization of precious samples, a global hyper-pixel-wise sample pair queues-based contrastive learning method is developed, which includes the construction of global queues that store hyper-pixel-wise samples and the updating scheme of a momentum encoder, reliably enhancing the extraction ability of semantic features. The proposed HPCL-Net is evaluated on the Loess Plateau relic landslide dataset and experimental results verify that the proposed HPCL-Net greatly outperforms existing models, where the mIoU is increased from 0.620 to 0.651, the Landslide IoU is improved from 0.334 to 0.394 and the F1score is enhanced from 0.501 to 0.565.

Related papers

• MRIFE: A Mask-Recovering and Interactive-Feature-Enhancing Semantic Segmentation Network For Relic Landslide Detection [7.6822321138894765]
  Relic landslide, formed over a long period, possess the potential for reactivation, making them a hazardous geological phenomenon. High-resolution remote sensing images for relic landslides face many challenges, including the object visual blur problem. A semantic segmentation model, termed mask-recovering and interactive-feature-enhancing (MRIFE), is proposed for more efficient feature extraction and separation. The proposed MRIFE is evaluated on a real relic landslide dataset, and experimental results show that it greatly improves the performance of relic landslide detection.
  arXiv  Detail & Related papers  (2024-11-26T07:15:50Z)
• Towards Resource-Efficient Federated Learning in Industrial IoT for Multivariate Time Series Analysis [50.18156030818883]
  Anomaly and missing data constitute a thorny problem in industrial applications. Deep learning enabled anomaly detection has emerged as a critical direction. The data collected in edge devices contain user privacy.
  arXiv  Detail & Related papers  (2024-11-06T15:38:31Z)
• SIRST-5K: Exploring Massive Negatives Synthesis with Self-supervised Learning for Robust Infrared Small Target Detection [53.19618419772467]
  Single-frame infrared small target (SIRST) detection aims to recognize small targets from clutter backgrounds. With the development of Transformer, the scale of SIRST models is constantly increasing. With a rich diversity of infrared small target data, our algorithm significantly improves the model performance and convergence speed.
  arXiv  Detail & Related papers  (2024-03-08T16:14:54Z)
• Leveraging Neural Radiance Fields for Uncertainty-Aware Visual Localization [56.95046107046027]
  We propose to leverage Neural Radiance Fields (NeRF) to generate training samples for scene coordinate regression. Despite NeRF's efficiency in rendering, many of the rendered data are polluted by artifacts or only contain minimal information gain.
  arXiv  Detail & Related papers  (2023-10-10T20:11:13Z)
• 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)
• A Computer Vision Enabled damage detection model with improved YOLOv5 based on Transformer Prediction Head [0.0]
  Current state-of-the-art deep learning (DL)-based damage detection models often lack superior feature extraction capability in complex and noisy environments. DenseSPH-YOLOv5 is a real-time DL-based high-performance damage detection model where DenseNet blocks have been integrated with the backbone. DenseSPH-YOLOv5 obtains a mean average precision (mAP) value of 85.25 %, F1-score of 81.18 %, and precision (P) value of 89.51 % outperforming current state-of-the-art models.
  arXiv  Detail & Related papers  (2023-03-07T22:53:36Z)
• An Iterative Classification and Semantic Segmentation Network for Old Landslide Detection Using High-Resolution Remote Sensing Images [6.584865979714256]
  An iterative classification and semantic segmentation network (ICSSN) is developed, which can greatly enhance both object-level and pixel-level classification performance. An iterative training strategy is elaborated to fuse features in semantic space such that both object-level and pixel-level classification performance are improved. The experimental results show that ICSSN can greatly improve the classification and segmentation accuracy of old landslide detection.
  arXiv  Detail & Related papers  (2023-02-24T02:51:09Z)
• Adversarial Feature Augmentation and Normalization for Visual Recognition [109.6834687220478]
  Recent advances in computer vision take advantage of adversarial data augmentation to ameliorate the generalization ability of classification models. Here, we present an effective and efficient alternative that advocates adversarial augmentation on intermediate feature embeddings. We validate the proposed approach across diverse visual recognition tasks with representative backbone networks.
  arXiv  Detail & Related papers  (2021-03-22T20:36:34Z)
• A New Mask R-CNN Based Method for Improved Landslide Detection [54.7905160534631]
  This paper presents a novel method of landslide detection by exploiting the Mask R-CNN capability of identifying an object layout. A data set of 160 elements is created containing landslide and non-landslide images. The proposed algorithm can be potentially useful for land use planners and policy makers of hilly areas.
  arXiv  Detail & Related papers  (2020-10-04T07:46:37Z)
• Locality-Aware Rotated Ship Detection in High-Resolution Remote Sensing Imagery Based on Multi-Scale Convolutional Network [7.984128966509492]
  We propose a locality-aware rotated ship detection (LARSD) framework based on a multi-scale convolutional neural network (CNN) The proposed framework applies a UNet-like multi-scale CNN to generate multi-scale feature maps with high-level information in high resolution. To enlarge the detection dataset, we build a new high-resolution ship detection (HRSD) dataset, where 2499 images and 9269 instances were collected from Google Earth with different resolutions.
  arXiv  Detail & Related papers  (2020-07-24T03:01:42Z)

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.