Multisense

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.