Fully convolutional Siamese neural networks for buildings damage
assessment from satellite images
- URL: http://arxiv.org/abs/2111.00508v1
- Date: Sun, 31 Oct 2021 14:18:59 GMT
- Title: Fully convolutional Siamese neural networks for buildings damage
assessment from satellite images
- Authors: Eugene Khvedchenya and Tatiana Gabruseva
- Abstract summary: Damage assessment after natural disasters is needed to distribute aid and forces to recovery from damage dealt optimally.
We develop a computational approach for an automated comparison of the same region's satellite images before and after the disaster.
We include an extensive ablation study and compare different encoders, decoders, loss functions, augmentations, and several methods to combine two images.
- Score: 1.90365714903665
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Damage assessment after natural disasters is needed to distribute aid and
forces to recovery from damage dealt optimally. This process involves acquiring
satellite imagery for the region of interest, localization of buildings, and
classification of the amount of damage caused by nature or urban factors to
buildings. In case of natural disasters, this means processing many square
kilometers of the area to judge whether a particular building had suffered from
the damaging factors.
In this work, we develop a computational approach for an automated comparison
of the same region's satellite images before and after the disaster, and
classify different levels of damage in buildings. Our solution is based on
Siamese neural networks with encoder-decoder architecture. We include an
extensive ablation study and compare different encoders, decoders, loss
functions, augmentations, and several methods to combine two images. The
solution achieved one of the best results in the Computer Vision for Building
Damage Assessment competition.
Related papers
- Creating A Coefficient of Change in the Built Environment After a
Natural Disaster [0.0]
This study proposes a novel method to assess damages in the built environment using a deep learning workflow to quantify it.
Aerial images from before and after a natural disaster of 50 epicenters worldwide were obtained from Google Earth.
arXiv Detail & Related papers (2021-10-31T20:46:31Z) - Spatially-Adaptive Image Restoration using Distortion-Guided Networks [51.89245800461537]
We present a learning-based solution for restoring images suffering from spatially-varying degradations.
We propose SPAIR, a network design that harnesses distortion-localization information and dynamically adjusts to difficult regions in the image.
arXiv Detail & Related papers (2021-08-19T11:02:25Z) - BDANet: Multiscale Convolutional Neural Network with Cross-directional
Attention for Building Damage Assessment from Satellite Images [24.989412626461213]
Building damage assessment from satellite imagery is critical before relief effort is deployed.
Deep neural networks have been successfully applied to building damage assessment.
We propose a novel two-stage convolutional neural network for Building Damage Assessment, called BDANet.
arXiv Detail & Related papers (2021-05-16T06:13:28Z) - Physically-Consistent Generative Adversarial Networks for Coastal Flood
Visualization [60.690929022840685]
We propose the first deep learning pipeline to ensure physical-consistency in synthetic visual satellite imagery.
By evaluating the imagery relative to physics-based flood maps, we find that our proposed framework outperforms baseline models in both physical-consistency and photorealism.
We publish a dataset of over 25k labelled image-pairs to study image-to-image translation in Earth observation.
arXiv Detail & Related papers (2021-04-10T15:00:15Z) - Post-Hurricane Damage Assessment Using Satellite Imagery and Geolocation
Features [0.2538209532048866]
We propose a mixed data approach, which leverages publicly available satellite imagery and geolocation features of the affected area to identify damaged buildings after a hurricane.
The method demonstrated significant improvement from performing a similar task using only imagery features, based on a case study of Hurricane Harvey affecting Greater Houston area in 2017.
In this work, a creative choice of the geolocation features was made to provide extra information to the imagery features, but it is up to the users to decide which other features can be included to model the physical behavior of the events, depending on their domain knowledge and the type of disaster.
arXiv Detail & Related papers (2020-12-15T21:30:19Z) - 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) - Physics-informed GANs for Coastal Flood Visualization [65.54626149826066]
We create a deep learning pipeline that generates visual satellite images of current and future coastal flooding.
By evaluating the imagery relative to physics-based flood maps, we find that our proposed framework outperforms baseline models in both physical-consistency and photorealism.
While this work focused on the visualization of coastal floods, we envision the creation of a global visualization of how climate change will shape our earth.
arXiv Detail & Related papers (2020-10-16T02:15:34Z) - MSNet: A Multilevel Instance Segmentation Network for Natural Disaster
Damage Assessment in Aerial Videos [74.22132693931145]
We study the problem of efficiently assessing building damage after natural disasters like hurricanes, floods or fires.
The first contribution is a new dataset, consisting of user-generated aerial videos from social media with annotations of instance-level building damage masks.
The second contribution is a new model, namely MSNet, which contains novel region proposal network designs.
arXiv Detail & Related papers (2020-06-30T02:23:05Z) - 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) - An Attention-Based System for Damage Assessment Using Satellite Imagery [18.43310705820528]
We present Siam-U-Net-Attn model - a multi-class deep learning model with an attention mechanism - to assess damage levels of buildings.
We evaluate the proposed method on xView2, a large-scale building damage assessment dataset, and demonstrate that the proposed approach achieves accurate damage scale classification and building segmentation results simultaneously.
arXiv Detail & Related papers (2020-04-14T16:37:55Z)
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.