Geoscience and Remote Sensing

In this research, a small-scale drone with the required magnetometer sensor technologies and novel intelligent automated techniques -- Maggy, was built to automate and ease the procedures of cleaning landmines and UXO/IDE. The dataset with an MP4-formated video demonstrates how a magnetometer-integrated autonomous drone can map the magnetic field of a landmine region effectively and efficiently using an intelligent application through near real-time data streaming.

Ground Penetrating Radar (GPR) facilitates the detection and localisation of subsurface structural anomalies in critical transport infrastructure (e.g. tunnels), better informing targeted maintenance strategies. However, conventional fixed-directional systems suffer from limited coverage - especially of less-accessible structural aspects (e.g. crowns) - alongside unclear visual output of anomaly spatial profiles, both for physical and simulated datasets.

The detection of the collapse of landslides trigerred by intense natural hazards, such as earthquakes and rainfall, allows rapid response to hazards which turned into disasters. The use of remote sensing imagery is mostly considered to cover wide areas and assess even more rapidly the threats. Yet, since optical images are sensitive to cloud coverage, their use is limited in case of emergency response. The proposed dataset is thus multimodal and targets the early detection of landslides following the disastrous earthquake which occurred in Haiti in 2021.

The dataset aims to compile images of buildings with structural damage for analysis. The images can be classified by the severity of damage to building facades after seismic events using deep learning techniques, particularly pre-trained convolutional neural networks and transfer learning. The analysis can precisely identify structural damage levels, aiding in effective evaluation and response strategies.