Apr 14, 2021 · Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery.

Oct 22, 2024 · Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral ...

May 24, 2021 · Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral ...

Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, ...

Abstract: Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area ...

Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery.

Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning. Author: Hu, Xikun. Ban, Yifang ; Nascetti, Andrea. Keywords: burned area mapping ...

Dec 9, 2024 · These results highlight the potential of detecting burned areas using the deep learning based approach.

Using uni-temporal Sentinel-2 imagery, we proposed a workflow based on deep learning (DL) semantic segmentation models to detect wildfires.

Oct 28, 2022 · In this paper, we introduce a unified data-driven framework capable of mapping areas damaged by fire by integrating time series of remotely sensed ...