Multisense
Description
if you use this dataset please cite the following paper: GAZADeepDav: A High Resolution Geotagged Satellite Imagery Dataset For Analyzing War-Induced Damage M. Bouabid and M. Farah, "GAZADeepDav: A High Resolution Geotagged Satellite Imagery Dataset For Analyzing War-Induced Damage," IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 2024, pp. 8876-8879, doi: 10.1109/IGARSS53475.2024.10642306.keywords: {Training;Accuracy;Recurrent neural networks;Image resolution;Geoscience and remote sensing;Satellite images;Task analysis;Satellite images;Deep learning;Gaza War;damage Detection;SqueezeNet;BiLSTM}, This dataset, named MultiSense, is designed to enhance disaster response by providing comprehensive data from multiple sources. It comes in two versions: balanced and unbalanced. The dataset consists of five distinct classes, each representing different types of events or conditions: Syria Earthquake: This class includes imagery and video footage related to earthquake damage. The data captures the aftermath of seismic events, showcasing various degrees of destruction. Gaza War: This class contains data depicting war-related damage. It includes imagery and videos from conflict zones, highlighting the impact of warfare on infrastructure and urban areas. Hurricane Harvey: This class encompasses data related to hurricane damage. It includes imagery and footage showing the effects of strong winds, flooding, and storm surges associated with hurricanes. Libya Flood: This class features imagery and videos of flood damage. It documents areas affected by flooding, capturing the extent of water damage to buildings, roads, and landscapes. No Damage: This class provides imagery and footage of areas with no significant damage. It serves as a control group, representing normal conditions without the impact of natural disasters or conflicts.
Files
Steps to reproduce
The MultiSense dataset is a curated collection specifically designed to support disaster response and event detection through the integration of satellite imagery and drone footage. The dataset is organized into five distinct classes, each representing different types of events. These classes include: Flooding Earthquake Conflict Hurricane No Damage (Control) Each class contains two types of data sources: PlanetScope Satellite Tiles: High-resolution 8-band satellite imagery from Planet Labs, with each tile covering a 3m x 3m area of the event location. Drone Footage: Extracted keyframes from drone videos, captured over the same locations, providing close-up visual details of the events. The dataset includes data from real-world disaster events such as the Derna floods, the Syria earthquake, the Gaza conflict, and Hurricane Harvey. By combining satellite and drone imagery, the MultiSense dataset offers a comprehensive view of both large-scale and localized damage, enabling improved event detection and analysis for disaster response applications.