Segmentation of Earth observation (EO) satellite data is critical for natural hazard analysis and disaster response. However, processing EO data at ground stations introduces delays due to data transmission bottlenecks and communication windows. Using segmentation models capable of near-real-time data analysis onboard satellites can therefore improve response times. This study presents a proof-of-concept using MobileSAM, a lightweight, pre-trained segmentation model, onboard Unibap iX10-100 satellite hardware. We demonstrate the segmentation of water bodies from Sentinel-2 satellite imagery and integrate MobileSAM with PASEOS, an open-source Python module that simulates satellite operations. This integration allows us to evaluate MobileSAM's performance under simulated conditions of a satellite constellation. Our research investigates the potential of fine-tuning MobileSAM in a decentralised way onboard multiple satellites in rapid response to a disaster. Our findings show that MobileSAM can be rapidly fine-tuned and benefits from decentralised learning, considering the constraints imposed by the simulated orbital environment. We observe improvements in segmentation performance with minimal training data and fast fine-tuning when satellites frequently communicate model updates. This study contributes to the field of onboard AI by emphasising the benefits of decentralised learning and fine-tuning pre-trained models for rapid response scenarios. Our work builds on recent related research at a critical time; as extreme weather events increase in frequency and magnitude, rapid response with onboard data analysis is essential.

In scenarios with numerous emergencies that arise and require the assistance of various rescue units (e.g., medical, fire, \& police forces), the rescue units would ideally be allocated quickly and distributedly while aiming to minimize casualties. This is one of many examples of distributed settings with service providers (the rescue units) and service requesters (the emergencies) which we term \textit{service oriented settings}. Allocating the service providers in a distributed manner while aiming for a global optimum is hard to model, let alone achieve, using the existing Distributed Constraint Optimization Problem (DCOP) framework. Hence, the need for a novel approach and corresponding algorithms. We present the Service Oriented Multi-Agent Optimization Problem (SOMAOP), a new framework that overcomes the shortcomings of DCOP in service oriented settings. We evaluate the framework using various algorithms based on auctions and matching algorithms (e.g., Gale Shapely). We empirically show that algorithms based on repeated auctions converge to a high quality solution very fast, while repeated matching problems converge slower, but produce higher quality solutions. We demonstrate the advantages of our approach over standard incomplete DCOP algorithms and a greedy centralized algorithm.

The 2021 Champlain Towers South Condominiums collapse in Surfside, Florida, resulted 98 deaths. Nine people are thought to have survived the initial collapse, and might have been rescued if rescue workers could have located them. Perhaps, if rescue workers had been able to use robots to search the interior of the rubble pile, outcomes might have been better. An improved understanding of the environment in which a robot would have to operate to be able to search the interior of a rubble pile would help roboticists develop better suited robotic platforms and control strategies. To this end, this work offers an approach to characterize and visualize the interior of a rubble pile and conduct a preliminary analysis of the occurrence of voids. Specifically, the analysis makes opportunistic use of four days of aerial imagery gathered from responders at Surfside to create a 3D volumetric aggregated model of the collapse in order to identify and characterize void spaces in the interior of the rubble. The preliminary results confirm expectations of small number and scale of these interior voids. The results can inform better selection and control of existing robots for disaster response, aid in determining the design specifications (specifically scale and form factor), and improve control of future robotic platforms developed for search operations in rubble.

This study took place these last few weeks. We have asked a selection of 50 CEOs and other leaders of the storage industry about their 2018 predictions and got finally 38 answers to discover some trends or at least repeatable patterns and topics. To participate companies had to produce three predictions with a short paragraph illustrating each point. We studied all answers and have found out some common topics among these players. CloudEndure - Gil Shai, CRO The transition to cloud disaster recovery will continue to strengthen and more and more enterprises will move their disaster site to the public cloud for the benefits of cost, flexibility and visibility (vs.

Mechanical masterminds have spawned the Bat Bot, a soaring, sweeping and diving robot that may eventually fly circles around other drones. Because it mimics the unique and more flexible way bats fly, this 3-ounce (85-gram) prototype could do a better and safer job getting into disaster sites and scoping out construction zones than bulky drones with spinning rotors, said the three authors of a study released Wednesday in the journal Science Robotics. For example, it would have been ideal for going inside the damaged Fukushima nuclear plant in Japan, said study co-author Seth Hutchinson, an engineering professor at the University of Illinois. The bat robot flaps its wings for better aerial maneuvers, glides to save energy and dive bombs when needed. Eventually, the researchers hope to have it perch upside down like the real thing, but that will have to wait for the robot's sequel.

Mechanical masterminds have spawned the Bat Bot, a soaring, sweeping and diving robot that may eventually fly circles around other drones. Because it mimics the unique and more flexible way bats fly, this 3-ounce prototype could do a better and safer job getting into disaster sites and scoping out construction zones than bulky drones with spinning rotors, said the three authors of a study released Wednesday in the journal Science Robotics. For example, it would have been ideal for going inside the damaged Fukushima nuclear plant in Japan, said study co-author Seth Hutchinson, an engineering professor at the University of Illinois. Bat Bot, a three-ounce flying robot can be more agile at getting into treacherous places than standard drones. The flying robot weighs just three ounces, and is equipped with nine joints. It measures about 8 inches from head to tail, and has a super-thin membrane that stretches to about a foot and a half.

Disaster rescue is one of the most serious social issues that involves very large numbers of heterogeneous agents in the hostile environment. The intention of the RoboCup Rescue project is to promote research and development in this socially significant domain at various levels, involving multiagent teamwork coordination, physical agents for search and rescue, information infrastructures, personal digital assistants, a standard simulator and decision-support systems, evaluation benchmarks for rescue strategies, and robotic systems that are all integrated into a comprehensive system in the future. For this effort, which was built on the success of the RoboCup Soccer project, we will provide forums of technical discussions and competitive evaluations for researchers and practitioners. Although the rescue domain is intuitively appealing as a large-scale multiagent and intelligent system domain, analysis has not yet revealed its domain characteristics. The first research evaluation meeting will be held at RoboCup-2001, in conjunction with the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-2001), as part of the RoboCup Rescue Simulation League and RoboCup/AAAI Rescue Robot Competition. In this article, we present a detailed analysis of the task domain and elucidate characteristics necessary for multiagent and intelligent systems for this domain. Then, we present an overview of the RoboCup Rescue project.