Integrated Satellite-IoT-Machine Learning Framework for Disaster Management
Abstract
"Published in November 2025, this dataset provides a multimodal framework integrating satellite imagery and IoT sensor data for environmental monitoring and disaster management. It is designed to support the development of machine learning models that synchronize remote sensing with ground-based IoT observations for real-time risk assessment."
Description
Overview
This dataset introduces an integrated framework combining Satellite and IoT data for enhanced disaster management and environmental tracking.
Technical Details
Includes synchronized time-series from ground IoT sensors and multi-spectral satellite imagery, facilitating cross-domain machine learning training.
Collection Setup
Data was collected via a distributed network of IoT sensors deployed in disaster-prone regions, coupled with corresponding satellite overpass data from late 2025.
Recommended Research Tasks
Research on early warning systems, multimodal data fusion for environmental monitoring, and satellite-IoT synchronization algorithms.
Access & License
Available on Zenodo. Access Dataset
📊 View Data Structure
To explore column names, data types, and sample rows, visit the official dataset page on Kaggle.
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Cite This Dataset
Nasim, S. F. (2024). Integrated Satellite-IoT-Machine Learning Framework for Disaster Management. [Dataset]. {Zenodo. https://doi.org/10.5281/zenodo.17550127
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Original source: {Zenodo (2024). Visit official page for more details.
Indexed by IoTDataset.com on Feb 06, 2026
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