Landslide mapping from Sentinel-2 imagery through change detection

• URL: http://arxiv.org/abs/2405.20161v1
• Date: Thu, 30 May 2024 15:33:32 GMT
• Title: Landslide mapping from Sentinel-2 imagery through change detection
• Authors: Tommaso Monopoli, Fabio Montello, Claudio Rossi,
• Abstract summary: 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.
• Score: 0.12289361708127873
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Landslides are one of the most critical and destructive geohazards. Widespread development of human activities and settlements combined with the effects of climate change on weather are resulting in a high increase in the frequency and destructive power of landslides, making them a major threat to human life and the economy. In this paper, we explore methodologies to map newly-occurred landslides using Sentinel-2 imagery automatically. All approaches presented are framed as a bi-temporal change detection problem, requiring only a pair of Sentinel-2 images, taken respectively before and after a landslide-triggering event. Furthermore, we introduce a novel deep learning architecture for fusing Sentinel-2 bi-temporal image pairs with Digital Elevation Model (DEM) data, showcasing its promising performances w.r.t. other change detection models in the literature. As a parallel task, we address limitations in existing datasets by creating a novel geodatabase, which includes manually validated open-access landslide inventories over heterogeneous ecoregions of the world. We release both code and dataset with an open-source license.

Related papers

• Automated Linear Disturbance Mapping via Semantic Segmentation of Sentinel-2 Imagery [0.0]
  Road, seismic exploration lines, and pipelines pose a significant threat to the boreal woodland caribou population. This research employs a deep convolutional neural network model based on the VGGNet16 architecture for semantic segmentation of lower resolution (10m) Sentinel-2 satellite imagery. The model is trained using ground-truth label maps sourced from the freely available Alberta Institute of Biodiversity Monitoring Human Footprint dataset.
  arXiv  Detail & Related papers  (2024-09-19T14:42:12Z)
• Robust Disaster Assessment from Aerial Imagery Using Text-to-Image Synthetic Data [66.49494950674402]
  We leverage emerging text-to-image generative models in creating large-scale synthetic supervision for the task of damage assessment from aerial images. We build an efficient and easily scalable pipeline to generate thousands of post-disaster images from low-resource domains. We validate the strength of our proposed framework under cross-geography domain transfer setting from xBD and SKAI images in both single-source and multi-source settings.
  arXiv  Detail & Related papers  (2024-05-22T16:07:05Z)
• Aerial Lifting: Neural Urban Semantic and Building Instance Lifting from Aerial Imagery [51.73680703579997]
  We present a neural radiance field method for urban-scale semantic and building-level instance segmentation from aerial images. objects in urban aerial images exhibit substantial variations in size, including buildings, cars, and roads. We introduce a scale-adaptive semantic label fusion strategy that enhances the segmentation of objects of varying sizes. We then introduce a novel cross-view instance label grouping strategy to mitigate the multi-view inconsistency problem in the 2D instance labels.
  arXiv  Detail & Related papers  (2024-03-18T14:15:39Z)
• FLOGA: A machine learning ready dataset, a benchmark and a novel deep learning model for burnt area mapping with Sentinel-2 [41.28284355136163]
  Wildfires pose significant threats to human and animal lives, ecosystems, and socio-economic stability. In this work, we create and introduce a machine-learning ready dataset we name FLOGA (Forest wiLdfire Observations for the Greek Area) This dataset is unique as it comprises of satellite imagery acquired before and after a wildfire event. We use FLOGA to provide a thorough comparison of multiple Machine Learning and Deep Learning algorithms for the automatic extraction of burnt areas.
  arXiv  Detail & Related papers  (2023-11-06T18:42:05Z)
• Landslide Topology Uncovers Failure Movements [1.08890978642722]
  We present an approach for identifying failure types based on their movements, e.g., slides and flows. We observe topological proxies reveal prevalent signatures of mass movement mechanics embedded in the landslide's morphology or shape. We can identify failure types in historic and event-specific landslide databases with 80 to 94 % accuracy.
  arXiv  Detail & Related papers  (2023-10-14T17:53:55Z)
• The Change You Want to See (Now in 3D) [65.61789642291636]
  The goal of this paper is to detect what has changed, if anything, between two "in the wild" images of the same 3D scene. We contribute a change detection model that is trained entirely on synthetic data and is class-agnostic. We release a new evaluation dataset consisting of real-world image pairs with human-annotated differences.
  arXiv  Detail & Related papers  (2023-08-21T01:59:45Z)
• Implicit neural representation for change detection [15.741202788959075]
  Most commonly used approaches to detecting changes in point clouds are based on supervised methods. We propose an unsupervised approach that comprises two components: Implicit Neural Representation (INR) for continuous shape reconstruction and a Gaussian Mixture Model for categorising changes. We apply our method to a benchmark dataset comprising simulated LiDAR point clouds for urban sprawling.
  arXiv  Detail & Related papers  (2023-07-28T09:26:00Z)
• Classification of structural building damage grades from multi-temporal photogrammetric point clouds using a machine learning model trained on virtual laser scanning data [58.720142291102135]
  We present a novel approach to automatically assess multi-class building damage from real-world point clouds. We use a machine learning model trained on virtual laser scanning (VLS) data. The model yields high multi-target classification accuracies (overall accuracy: 92.0% - 95.1%)
  arXiv  Detail & Related papers  (2023-02-24T12:04:46Z)
• Detection of Deepfake Videos Using Long Distance Attention [73.6659488380372]
  Most existing detection methods treat the problem as a vanilla binary classification problem. In this paper, the problem is treated as a special fine-grained classification problem since the differences between fake and real faces are very subtle. A spatial-temporal model is proposed which has two components for capturing spatial and temporal forgery traces in global perspective.
  arXiv  Detail & Related papers  (2021-06-24T08:33:32Z)
• City-scale Scene Change Detection using Point Clouds [71.73273007900717]
  We propose a method for detecting structural changes in a city using images captured from mounted cameras over two different times. A direct comparison of the two point clouds for change detection is not ideal due to inaccurate geo-location information. To circumvent this problem, we propose a deep learning-based non-rigid registration on the point clouds. Experiments show that our method is able to detect scene changes effectively, even in the presence of viewpoint and illumination differences.
  arXiv  Detail & Related papers  (2021-03-26T08:04:13Z)
• Improving Emergency Response during Hurricane Season using Computer Vision [0.06882042556551608]
  We have developed a framework for crisis response and management that incorporates the latest technologies in computer vision (CV), inland flood prediction, damage assessment and data visualization. Our computer-vision model analyzes spaceborne and airborne imagery to detect relevant features during and after a natural disaster. We have designed an ensemble of models to identify features including water, roads, buildings, and vegetation from the imagery.
  arXiv  Detail & Related papers  (2020-08-17T15:42:02Z)

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.