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

2026-01-14

AI Summary

Existing satellite systems struggle to simultaneously achieve high spatial resolution and frequent revisit rates when rapidly mapping precise burn scars after wildfires. To address this challenge, this work proposes BAM-MRCD, a deep learning model that effectively integrates low-latency, low-resolution MODIS data with high-spatial-resolution Sentinel-2 imagery for the first time, enabling synergistic change detection across multi-source, multi-resolution remote sensing data. By overcoming the limitations of single-data-source approaches, the method significantly enhances both the accuracy and timeliness of burn scar mapping in small-scale wildfire scenarios, outperforming current state-of-the-art change detection models and strong baseline methods.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• burn scar mapping
• wildfire delineation
• satellite imagery
• spatial resolution
• temporal revisit frequency

Innovation

• multi-resolution
• multi-source
• burn scar mapping
• deep learning
• wildfire detection