Flooding has serious environmental and humanitarian effects. To track these effects, previous work has used remote sensing to achieve global monitoring at low to moderate resolutions or regional monitoring at high resolutions. We proposed that implementing a new sensorweb that had previously been prototyped only regionally in Thailand could combine moderate-resolution flood detection with targeted high-resolution observations to enable worldwide high-resolution flood monitoring. Furthermore, we aimed to integrate both commercial and government satellites into this sensorweb to improve upon previous efforts, which only used government satellites. To this end, we first gather data from several moderate-resolution sensors to identify large, flooded regions. We then task high-resolution sensors to observe these floods and analyze the resulting data, thus enabling global high-resolution flood monitoring. Overall, our approach improves worldwide analysis of floods, which can be further improved by our ongoing efforts to incorporate more flood detection sensors and generate more products.

We collect alerts of volcanic activity from space remote sensing, volcano observatories, and meteorological organizations [1], [2]. We use these alerts to autonomously generate <1 m/pixel observation requests for Planet SkySat [3] and 70m/pixel thermal infrared data from the ECOSTRESS instrument on the ISS. We also automatically download Dove imagery corresponding to alerts to supplement the SkySat and ECOSTRESS imagery. One new feature is that we can then automatically generate thermal products from the Planet image data using newly-developed band-comparison classifiers.This system has been running continuously for nearly 1.5 years without a dedicated operations team. It has generated over 650,000 alerts, tasked over 80 SkySat observations, and resulted in more than 6,000 scenes scheduled for ECOSTRESS.

A new observing strategy for floods was demonstrated and evaluated in a testbed environment. The strategy coordinates several observing platforms, including in situ and space based, to observe a flood from multiple vantage points and dynamically target predicted flood events with highresolution observations. The coordinated observations were assimilated back into the model to continuously improve forecasts and future observation selection. The demonstration shows the potential for coordinated, model-driven observing strategies and the feasibility of the NOS Testbed for demonstrating and evaluating new observing strategies.

With the rise of new AI technologies, autonomous systems are moving towards a paradigm in which increasing levels of responsibility are shifted from the human to the system, creating a transition from human-in-the-loop systems to human-on-the-loop (HoTL) systems. This has a significant impact on the safety analysis of such systems, as new types of errors occurring at the boundaries of human-machine interactions need to be taken into consideration. Traditional safety analysis typically focuses on system-level hazards with little focus on user-related or user-induced hazards that can cause critical system failures. To address this issue, we construct domain-level safety analysis assets for sUAS (small unmanned aerial systems) applications and describe the process we followed to explicitly, and systematically identify Human Interaction Points (HiPs), Hazard Factors and Mitigations from system hazards. We evaluate our approach by first investigating the extent to which recent sUAS incidents are covered by our hazard trees, and second by performing a study with six domain experts using our hazard trees to identify and document hazards for sUAS usage scenarios. Our study showed that our hazard trees provided effective coverage for a wide variety of sUAS application scenarios and were useful for stimulating safety thinking and helping users to identify and potentially mitigate human-interaction hazards.

Increased space and ground sensing is enabling dramatic new measurements of a wide range of Earth Science and Applied Earth Science phenomena, including: volcanism, flooding, wildfires, weather, and many other phenomena. New challenges exist to rapidly assimilate available data and to optimize measurements (e.g. direct assets) to best observe these complex and dynamic spatiotemporal phenomena. Artificial Intelligence offers the potential to assist in data interpretation and resource allocation to best allocate sensing assets. We describe efforts to build and experiment with such “sensorweb” systems and offer some direction for the future sensorweb observation systems.

This paper explores the process of aligning a Workforce Development Program with Software Process Improvement strategies in order to accelerate the adoption of Software Engineering practices in a major software organization $the Securities Industry Automation Corporation (SIAC). Some of the ideas and conclusions presented here are drawn from "Using SWEBOK for Education Programs in Industry and Academia" (Frailey and Mason, 2002).

Panel Summary For more than twenty years, the IEEE Computer Society has played a leading role in improving the professionalism of software engineers. Its activities include development of a large collection of software engineering standards, publication of many seminal software engineering journals, texts, and reference books, co-sponsorship of undergraduate software engineering curriculum and accreditation activities, and co-sponsorship of the Software Engineering Body of Knowledge (SWEBOK) project. In 1999, the Society launched an effort to certify the knowledge of practicing software engineers. This effort led to the development of the Society’s CSDP certification examination, which was given to a test group in 2001. The examination will be offered to the public through test centers around the world in Spring 2003. The Society has also encouraged the development of face-toface and online training opportunities for prospective test-takers. These opportunities will also be available in Spring 2003.

Discusses how an industrial and an academic software engineering education program were developed and improved using the Software Engineering Body of Knowledge (SWEBOK).