Planet data skynet imagery and disaster resilience
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Planet Data, Skynet Imagery, and Disaster Resilience
Introduction to Disaster Resilience and Big Data
Enhancing disaster resilience in urban systems is increasingly critical due to rapid urbanization and escalating climate risks. Traditional methods of measuring resilience often rely on static metrics, which fail to capture the dynamic and complex nature of disaster impacts and recovery processes. Recent research advocates for a shift towards data-driven, dynamical complex systems models that leverage large-scale and granular observations to better understand and enhance community resilience1.
Role of Satellite Imagery in Disaster Monitoring
Real-Time Disaster Monitoring with Emerging Technologies
Emerging technologies in satellite imagery are revolutionizing disaster monitoring and relief efforts. A proposed architecture for a satellite-based system emphasizes real-time, continuous high-resolution imagery, which can be controlled from primitive ground sites. This system, comprising elements at geosynchronous and L1 altitudes, provides focused high-value imagery and full-disk context, respectively. Such capabilities are invaluable for directing disaster relief efforts efficiently and effectively2.
Night-Time Light Imagery for Economic Recovery Analysis
Night-time light (NTL) remote sensing images offer a unique tool for observing human activities and measuring community resilience during natural disasters. By analyzing NTL data, researchers can model the recovery patterns of economic activity post-disaster. For instance, a study on Hurricane Katrina utilized NTL imagery to reveal the economic impact and spatial variation in recovery patterns. This dynamic approach to measuring resilience provides actionable insights for enhancing community resilience across different disaster phases3.
Advanced Techniques in Disaster Detection
Convolutional Neural Networks for Disaster Detection
The use of convolutional neural networks (CNNs) in analyzing remote sensing imagery has shown significant promise in detecting natural disasters such as landslides and floods. By training CNNs on pre-disaster and post-disaster aerial imagery, researchers have achieved high accuracy in identifying disaster-affected regions. This method not only improves detection accuracy but also mitigates the risk of human error, thereby enhancing the effectiveness of disaster relief operations4.
Conclusion
The integration of big data, satellite imagery, and advanced machine learning techniques is transforming the field of disaster resilience. By moving towards data-driven, dynamic models and leveraging real-time imagery and deep learning, researchers and policymakers can better understand, monitor, and respond to disasters. This paradigm shift holds the potential to significantly improve the resilience and sustainability of communities worldwide.
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Most relevant research papers on this topic
Toward data-driven, dynamical complex systems approaches to disaster resilience
Data-driven, dynamical complex systems models can enhance disaster resilience by capturing dynamic interdependencies and intrinsic resilience characteristics of communities.
Highly CitedThe role of emerging technologies in imagery for disaster monitoring and disaster relief assistance
Emerging technologies can enable low-cost satellite-based systems for real-time disaster monitoring and relief assistance, providing high-resolution imagery from geosynchronous and L1 altitudes.
Observing community resilience from space: Using nighttime lights to model economic disturbance and recovery pattern in natural disaster
Night-time light remote sensing images can effectively measure community resilience in natural disasters, providing actionable information for developing resilient and sustainable communities.
Highly CitedDisaster detection from aerial imagery with convolutional neural network
CNN-based automatic natural disaster detection from aerial imagery accurately detects landslides and floods with 80%-90% accuracy, aiding in disaster relief efforts.
OmbriaNet—Supervised Flood Mapping via Convolutional Neural Networks Using Multitemporal Sentinel-1 and Sentinel-2 Data Fusion
OmbriaNet, a deep neural network architecture, effectively detects changes between permanent and flooded water areas using multitemporal Sentinel-1 and Sentinel-2 data fusion, producing high-quality flood maps.
Disaster resilience through big data: Way to environmental sustainability
Big data technologies can enhance disaster resilience by improving information linkages and enhancing socio-ecological vulnerability management.
Highly CitedSpatial Assessment of Community Resilience from 2012 Hurricane Sandy Using Nighttime Light
Nighttime lights remote sensing images can provide valuable insights into community recovery and resilience in natural disasters, improving disaster awareness and preparedness.
Deep Learning-based Aerial Image Segmentation with Open Data for Disaster Impact Assessment
The proposed framework using deep learning and open data effectively segmented aerial images for disaster impact assessment, reducing manual annotation and requiring less parameters compared to state-of-the-art networks.
PreprintFlooding applications enabled by high resolution, high cadence imagery from the Planet constellation of satellites
High-resolution, high-caching satellite imagery from Planet Labs' constellation of satellites enhances flood risk forecasting, historical modeling, and risk management.
Comparative Study of Real-Time Semantic Segmentation Networks in Aerial Images During Flooding Events
Lightweight real-time semantic segmentation models show potential for efficient and accurate aerial image segmentation during flooding events, with some achieving over 60% test mIoU on the FloodNet dataset.
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