Advances in Remote Sensing-based Disaster Monitoring and Assessment

Remote sensing data and techniques have been widely used for disaster monitoring and assessment. In particular, recent advances in sensor technologies and artificial intelligence-based modeling are very promising for disaster monitoring and readying responses aimed at reducing the damage caused by disasters. This book contains eleven scientific papers that have studied novel approaches applied to a range of natural disasters such as forest fire, urban land subsidence, flood, and tropical cyclones

Real-time anomaly detection of gamma-ray bursts for the Cherenkov Telescope Array using deep learning

The Cherenkov Telescope Array (CTA) will be the next-generation ground-based observatory to study the universe in the very-high-energy domain. The observatory will rely on a Science Alert Generation (SAG) system to analyze the real-time data from the telescopes and generate science alerts. The SAG system will play a crucial role in the search and follow-up of transients from external alerts, enabling multi-wavelength and multi-messenger collaborations. It will maximize the potential for the detection of the rarest phenomena, such as gamma-ray bursts (GRBs), which are the science case for this study. This study presents an anomaly detection method based on deep learning for detecting gamma-ray burst events in real-time. The performance of the proposed method is evaluated and compared against the Li&Ma standard technique in two use cases of serendipitous discoveries and follow-up observations, using short exposure times. The method shows promising results in detecting GRBs and is flexible enough to allow real-time search for transient events on multiple time scales. The method does not assume background nor source models and doe not require a minimum number of photon counts to perform analysis, making it well-suited for real-time analysis. Future improvements involve further tests, relaxing some of the assumptions made in this study as well as post-trials correction of the detection significance. Moreover, the ability to detect other transient classes in different scenarios must be investigated for completeness. The system can be integrated within the SAG system of CTA and deployed on the onsite computing clusters. This would provide valuable insights into the method's performance in a real-world setting and be another valuable tool for discovering new transient events in real-time. Overall, this study makes a significant contribution to the field of astrophysics by demonstrating the effectiveness of deep learning-based anomaly detection techniques for real-time source detection in gamma-ray astronomy.Il Cherenkov Telescope Array (CTA) sarà l'osservatorio terrestre di prossima generazione per lo studio dell'universo nel dominio delle altissime energie. L'osservatorio sfrutterà il sistema software di Science Alert Generation (SAG) per l'analisi in tempo reale dei dati osservativi e per generare automaticamente allerte scientifiche. Il sistema SAG svolgerà un ruolo da protagonista nella ricerca e nel follow-up di fenomeni transienti a seguito di allerte esterne, consentendo collaborazioni multi-wavelength e multi-messenger. Massimizzerà la capacità di rilevare i fenomeni più rari, come i lampi di raggi gamma (GRBs), che sono il caso scientifico di questo studio. Questo studio presenta una tecnica di anomaly detection basata sul deep learning per la rilevazione in tempo reale di GRBs. Le prestazioni della tecnica proposta sono valutate e confrontate con la tecnica standard di Li&Ma, nei due casi d'uso scientifici di serendipitous discoveries e follow-up observations, considerando brevi tempi di esposizione. La tecnica proposta mostra risultati promettenti e è abbastanza flessibile da consentire la ricerca di eventi transienti su più tempi scala. Non necessita di fare ipotesi sui modelli del background e della sorgente e non richiede un numero minimo di conteggi di fotoni per eseguire l'analisi, rendendola adatta per l'analisi in tempo reale. Miglioramenti futuri includono ulteriori test, accantonando alcune delle ipotesi semplificative assunte in questo studio, così come la correzione post-trial della significatività di rilevazione. Inoltre, dovrà essere testata la capacità di rilevare altre classi di transienti oltre ai GRBs. L'integrazione all'interno del sistema SAG e la messa in produzione nei centri di calcolo onsite, fornirebbe preziose informazioni sulle prestazioni del metodo con dati non simulati. Nel complesso, questo studio fornisce un contributo significativo al campo dell'astrofisica delle alte energie e dimostra l'efficacia della tecnica di anomaly detection per la rilevazione in tempo reale di fenomeni transienti

A review of the internet of floods : near real-time detection of a flood event and its impact

Worldwide, flood events frequently have a dramatic impact on urban societies. Time is key during a flood event in order to evacuate vulnerable people at risk, minimize the socio-economic, ecologic and cultural impact of the event and restore a society from this hazard as quickly as possible. Therefore, detecting a flood in near real-time and assessing the risks relating to these flood events on the fly is of great importance. Therefore, there is a need to search for the optimal way to collect data in order to detect floods in real time. Internet of Things (IoT) is the ideal method to bring together data of sensing equipment or identifying tools with networking and processing capabilities, allow them to communicate with one another and with other devices and services over the Internet to accomplish the detection of floods in near real-time. The main objective of this paper is to report on the current state of research on the IoT in the domain of flood detection. Current trends in IoT are identified, and academic literature is examined. The integration of IoT would greatly enhance disaster management and, therefore, will be of greater importance into the future

“Design, Development and Characterization of a Thermal Sensor Brick System for Modular Robotics

This thesis presents the work on thermal imaging sensor brick (TISB) system for modular robotics. The research demonstrates the design, development and characterization of the TISB system. The TISB system is based on the design philosophy of sensor bricks for modular robotics. In under vehicle surveillance for threat detection, which is a target application of this work we have demonstrated the advantages of the TISB system over purely vision-based systems. We have highlighted the advantages of the TISB system as an illumination invariant threat detection system for detecting hidden threat objects in the undercarriage of a car. We have compared the TISB system to the vision sensor brick system and the mirror on a stick. We have also illustrated the operational capability of the system on the SafeBot under vehicle robot to acquire and transmit the data wirelessly. The early designs of the TISB system, the evolution of the designs and the uniformity achieved while maintaining the modularity in building the different sensor bricks; the visual, the thermal and the range sensor brick is presented as part of this work. Each of these sensor brick systems designed and implemented at the Imaging Robotics and Intelligent Systems (IRIS) laboratory consist of four major blocks: Sensing and Image Acquisition Block, Pre-Processing and Fusion Block, Communication Block, and Power Block. The Sensing and Image Acquisition Block is to capture images or acquire data. The Pre-Processing and Fusion Block is to work on the acquired images or data. The Communication Block is for transferring data between the sensor brick and the remote host computer. The Power Block is to maintain power supply to the entire brick. The modular sensor bricks are self-sufficient plug and play systems. The SafeBot under vehicle robot designed and implemented at the IRIS laboratory has two tracked platforms one on each side with a payload bay area in the middle. Each of these tracked platforms is a mobility brick based on the same design philosophy as the modular sensor bricks. The robot can carry one brick at a time or even multiple bricks at the same time. The contributions of this thesis are: (1) designing and developing the hardware implementation of the TISB system, (2) designing and developing the software for the TISB system, and (3) characterizing the TISB system, where this characterization of the system is the major contribution of this thesis. The analysis of the thermal sensor brick system provides the user and future designers with sufficient information on parameters to be considered to make the right choice for future modifications, the kind of applications the TISB could handle and the load that the different blocks of the TISB system could manage. Under vehicle surveillance for threat detection, perimeter / area surveillance, scouting, and improvised explosive device (IED) detection using a car-mounted system are some of the applications that have been identified for this system

Investigation of low-cost infrared sensing for intelligent deployment of occupant restraints

In automotive transport, airbags and seatbelts are effective at restraining the driver and passenger in the event of a crash, with statistics showing a dramatic reduction in the number of casualties from road crashes. However, statistics also show that a small number of these people have been injured or even killed from striking the airbag, and that the elderly and small children are especially at risk of airbag-related injury. This is the result of the fact that in-car restraint systems were designed for the average male at an average speed of 50 km/hr, and people outside these norms are at risk. Therefore one of the future safety goals of the car manufacturers is to deploy sensors that would gain more information about the driver or passenger of their cars in order to tailor the safety systems specifically for that person, and this is the goal of this project. This thesis describes a novel approach to occupant detection, position measurement and monitoring using a low-cost thermal imaging based system, which is a departure from traditional video camera-based systems, and at an affordable price. Experiments were carried out using a specially designed test rig and a car driving simulator with members of the public. Results have shown that the thermal imager can detect a human in a car cabin mock up and provide crucial real-time position data, which could be used to support intelligent restraint deployment. Other valuable information has been detected such as whether the driver is smoking, drinking a hot or cold drink, using a mobile phone, which can help to infer the level of driver attentiveness or engagement

Dutkat: A Privacy-Preserving System for Automatic Catch Documentation and Illegal Activity Detection in the Fishing Industry

United Nations' Sustainable Development Goal 14 aims to conserve and sustainably use the oceans and their resources for the benefit of people and the planet. This includes protecting marine ecosystems, preventing pollution, and overfishing, and increasing scientific understanding of the oceans. Achieving this goal will help ensure the health and well-being of marine life and the millions of people who rely on the oceans for their livelihoods. In order to ensure sustainable fishing practices, it is important to have a system in place for automatic catch documentation. This thesis presents our research on the design and development of Dutkat, a privacy-preserving, edge-based system for catch documentation and detection of illegal activities in the fishing industry. Utilising machine learning techniques, Dutkat can analyse large amounts of data and identify patterns that may indicate illegal activities such as overfishing or illegal discard of catch. Additionally, the system can assist in catch documentation by automating the process of identifying and counting fish species, thus reducing potential human error and increasing efficiency. Specifically, our research has consisted of the development of various components of the Dutkat system, evaluation through experimentation, exploration of existing data, and organization of machine learning competitions. We have also implemented it from a compliance-by-design perspective to ensure that the system is in compliance with data protection laws and regulations such as GDPR. Our goal with Dutkat is to promote sustainable fishing practices, which aligns with the Sustainable Development Goal 14, while simultaneously protecting the privacy and rights of fishing crews

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

Tactical Satellite (TacSat) feasibility study a scenario driven approach

The objective of this project was to examine the feasibility of developing a tactically controlled, operationally responsive satellite system. A specific mission scenario, the Philippine Sea Scenario, was chosen to guide and bound the analysis. Within the bounds of this scenario, this high level space systems engineering exercise provided insights into operations and military utility as well as enough granularity to estimate costs for such a system. The operational approach and high level design concept is based on the Space Mission Analysis and Design (SMAD) process authored by Wiley J. Larson and Kames R. Wertz. The study shows that there are tactical scenarios in which space capabilities provide military utility and cost effectiveness above what is provided by traditional tactical assets such as UAVs. This is particularly true when large operational areas are involved and long periods of service are required.http://archive.org/details/tacticalsatellit109456927N