Magnifying change: Rapid burn scar mapping with multi-resolution, multi-source satellite imagery

TL;DR

This paper introduces BAM-MRCD, a deep learning model that combines multi-resolution satellite data to rapidly and accurately map wildfire burn scars, even small-scale fires, addressing the limitations of existing methods in operational scenarios.

Contribution

The paper presents a novel multi-resolution, multi-source deep learning model for timely and detailed burn scar mapping using MODIS and Sentinel-2 data, improving accuracy and speed.

Findings

Outperforms existing change detection models in burn scar delineation

Detects small-scale wildfires with high accuracy

Provides high-resolution maps quickly after wildfires

Abstract

Delineating wildfire affected areas using satellite imagery remains challenging due to irregular and spatially heterogeneous spectral changes across the electromagnetic spectrum. While recent deep learning approaches achieve high accuracy when high-resolution multispectral data are available, their applicability in operational settings, where a quick delineation of the burn scar shortly after a wildfire incident is required, is limited by the trade-off between spatial resolution and temporal revisit frequency of current satellite systems. To address this limitation, we propose a novel deep learning model, namely BAM-MRCD, which employs multi-resolution, multi-source satellite imagery (MODIS and Sentinel-2) for the timely production of detailed burnt area maps with high spatial and temporal resolution. Our model manages to detect even small scale wildfires with high accuracy, surpassing similar change detection models as well as solid baselines. All data and code are available in the GitHub repository: https://github.com/Orion-AI-Lab/BAM-MRCD.