Oil Spill Detection Analyzing “Sentinel 2“ Satellite Images: A Persian Gulf Case Study

Oil spills near exploitation areas and oil loading ports are often related to the ambitions of governments to get more oil market share and the negligence at the time of the loading in large tankers or ships. The present study investigates one oil spill event using multi sensor satellite images in the Al Khafji (between Kuwait and Saudi Arabia) zone. Oil slicks have been characterized with multi sensor satellite images over the Persian Gulf and then analyzed in order to detect and classify oil spills in this zone. In particular this paper discusses oil pollution detection in the Persian Gulf by using multi sensor satellite images data. Oil spill images have been selected by using Sentinel 2 images pinpointing oil spill zones. ENVI software for analysing satellite images and ADIOS (Automated Data Inquiry for Oil Spills) for oil weathering modelling have been used. The obtained results in Al Khafji zone show that the oil spill moves towards the coastline firstly increasing its surface and then decreasing it until reaching the coastline

Functional requirements document for the Earth Observing System Data and Information System (EOSDIS) Scientific Computing Facilities (SCF) of the NASA/MSFC Earth Science and Applications Division, 1992

Five scientists at MSFC/ESAD have EOS SCF investigator status. Each SCF has unique tasks which require the establishment of a computing facility dedicated to accomplishing those tasks. A SCF Working Group was established at ESAD with the charter of defining the computing requirements of the individual SCFs and recommending options for meeting these requirements. The primary goal of the working group was to determine which computing needs can be satisfied using either shared resources or separate but compatible resources, and which needs require unique individual resources. The requirements investigated included CPU-intensive vector and scalar processing, visualization, data storage, connectivity, and I/O peripherals. A review of computer industry directions and a market survey of computing hardware provided information regarding important industry standards and candidate computing platforms. It was determined that the total SCF computing requirements might be most effectively met using a hierarchy consisting of shared and individual resources. This hierarchy is composed of five major system types: (1) a supercomputer class vector processor; (2) a high-end scalar multiprocessor workstation; (3) a file server; (4) a few medium- to high-end visualization workstations; and (5) several low- to medium-range personal graphics workstations. Specific recommendations for meeting the needs of each of these types are presented

An harmonic radar prototype for insect tracking in harsh environments

Harmonic entomological radars have been used in the last decades to track small and lightweight passive tags carried by various insects, usually flying at low altitude and over flat terrain. Despite being exploited in many applications, not a lot of progress was achieved in terms of performances over the years. This paper reviews the research work done in this topic throughout the European LIFE project STOPVESPA, from 2015 to 2019. The main objective of LIFE STOPVESPA was to contain the invasive Asian hornet (Vespa velutina) and prevent it from further invading Italy. Among the foreseen activities, a new harmonic radar has been developed as an effective tool to locate the hornets nests to be destroyed. A preliminary prototype, based on a magnetron generator, was tested in 2015, showing a detection range of about 125 m. A first upgrade of this prototype was released in 2016, allowing to increase the detection range up to 150 m. A new approach, based on a solid state power amplifier and a digitally modulated signal, was then adopted for the second prototype developed in 2017 and extensively run in 2018; the detection range raised to 500 m. A last engineered prototype was eventually built for the 2019 summer campaign with additional improvements. This tool has been extensively validated over the last years with the Asian hornet but it has potential for tracking and monitoring many other flying insects

Dense Motion Estimation for Smoke

Motion estimation for highly dynamic phenomena such as smoke is an open challenge for Computer Vision. Traditional dense motion estimation algorithms have difficulties with non-rigid and large motions, both of which are frequently observed in smoke motion. We propose an algorithm for dense motion estimation of smoke. Our algorithm is robust, fast, and has better performance over different types of smoke compared to other dense motion estimation algorithms, including state of the art and neural network approaches. The key to our contribution is to use skeletal flow, without explicit point matching, to provide a sparse flow. This sparse flow is upgraded to a dense flow. In this paper we describe our algorithm in greater detail, and provide experimental evidence to support our claims.Comment: ACCV201