AI ANALYSIS REPORT
Near-real time fires detection using satellite imagery in Sudan conflict
The challenges of ongoing war in Sudan highlight the need for rapid monitoring and analysis of such conflicts. Advances in deep learning and readily available satellite remote sensing imagery allow for near real-time monitoring. This paper uses 4-band imagery from Planet Labs with a deep learning model to show that fire damage in armed conflicts can be monitored with minimal delay. We demonstrate the effectiveness of our approach using five case studies in Sudan. We show that, compared to a baseline, the automated method captures the active fires and charred areas more accurately. Our results indicate that using 8-band imagery or time series of such imagery only result in marginal gains.
EXECUTIVE SUMMARY
Executive Impact Summary
The ongoing armed conflict in Sudan began in April 2023, highlighting the critical need for rapid conflict monitoring. This paper leverages deep learning and readily available satellite imagery to monitor fire damage in armed conflicts with minimal delay, demonstrating an effective approach using five case studies in Sudan.
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Near real-time conflict monitoring
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Lightweight unsupervised DL model utilized
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Small fires detected (96m x 96m)
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Outperforms pixel-wise baseline method
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Marginal gains from 8-band/time series
Deep Analysis & Enterprise Applications
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Leveraging Deep Learning for Satellite Imagery Analysis
The research utilizes a lightweight unsupervised deep learning model, RaVAEn, based on Variational Auto-Encoders (VAE). This model is re-trained with 4-band Planet Labs imagery to detect active fires and burnt areas. Unlike traditional methods, deep learning models can capture complex latent features in higher-dimensional space, making them robust against noise and minor image variations, crucial for accurate conflict monitoring.
High-Frequency Satellite Imagery for Near Real-Time Monitoring
This study employs radiometrically calibrated 4-band ortho-analytic imagery from Planet Labs PBC, specifically from the PS2 instrument. The high-frequency revisit times of PlanetScope imagery (almost daily) are critical for near real-time conflict monitoring, allowing for rapid assessment of fire damage. The use of 4-band data (red, green, blue, and near-infrared) provides sufficient spectral information for effective fire detection, with only marginal gains observed from 8-band or time-series data.
Automated Damage Assessment in Conflict Zones
The paper demonstrates the effectiveness of AI-driven remote sensing for monitoring the ongoing Sudan conflict. By detecting active fires and burn scars, the approach provides crucial, near real-time intelligence on conflict-related damage. This automated method offers a systematic, non-intrusive way to gather evidence in high-risk settings, outperforming pixel-wise baselines in complex scenarios and even detecting small fires in areas as compact as 96m x 96m.
Near Real-time
Conflict Monitoring Achieved with High-Frequency Satellite Imagery
Enterprise Process Flow
Planet Labs 4-band Imagery
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Lightweight Unsupervised Deep Learning Model (RaVAEn)
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Active Fires & Burnt-Area Detection
96m x 96m
Smallest Fire Area Detected
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| AUPRC Score (Average across case studies) |
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Analyzing Marginal Gains from Advanced Imagery
While 4-band Planet Labs imagery proved highly effective for near-real time fire detection, experiments with 8-band imagery and temporal series of input images showed only marginal gains. This finding is crucial for practical deployment, indicating that the chosen 4-band approach strikes an optimal balance between data availability, computational efficiency, and detection accuracy for critical conflict monitoring scenarios. The focus remains on maximizing actionable intelligence with readily accessible resources.
BUSINESS IMPACT
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PATH TO VALUE
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