Post Wildfire Burnt-up Detection using Siamese UNet - Archive ouverte HAL Access content directly
Conference Papers Year : 2023

Post Wildfire Burnt-up Detection using Siamese UNet

Abstract

In this article, we present an approach for detecting burnt area due to wild fire in Sentinel-2 images by leveraging the power of Siamese neural networks. By employing a Siamese network, we are able to efficiently encode the feature extraction process for pairs of images. This is achieved by utilizing two branches within the Siamese network, which capture and combine information at different resolutions to make predictions. The weights are shared between these two branches in siamese networks. This design allows to effectively analyze the changes between two remote sensing images, enabling precise identification of areas impacted by forest wildfires in the state of California as part of ChaBuD challenge thereby assisting local authorities in effectively monitoring the impacted regions and facilitating the restoration process. We experimented with various model architectures to train ChaBuD dataset and carefully evaluated the performance. Through rigorous testing and analysis, we have achieved promising results, ultimately obtaining a final private score (IoU) of 0.7495 on the hidden test dataset. The code is available at https://github.com/kavyagupta/chabud. We also deploy the final model as a point solution for anyone to use at https://firemap.io.
Fichier principal
Vignette du fichier
Chabud_Ecml_pkdd.pdf (12.36 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-04225474 , version 1 (02-10-2023)

Licence

Attribution

Identifiers

  • HAL Id : hal-04225474 , version 1

Cite

Sagar Verma, Kavya Gupta. Post Wildfire Burnt-up Detection using Siamese UNet. ECML PKDD 2023 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2023, Turin, Italy. ⟨hal-04225474⟩
51 View
6 Download

Share

Gmail Facebook X LinkedIn More