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Remote sensing is the process of detecting and monitoring the physical characteristics of an area by measuring its reflected and emitted radiation at a distance. It is being broadly used to monitor ecosystems, mainly for their preservation. Ever-growing reports of invasive species have affected the natural balance of ecosystems. Exotic invasive species have a critical impact when introduced into new ecosystems and may lead to the extinction of native species. In this study, we focus on Ludwigia peploides, considered by the European Union as an aquatic invasive species. Its presence can negatively impact the surrounding ecosystem and human activities such as agriculture, fishing, and navigation. Our goal was to develop a method to identify the presence of the species. We used images collected by a drone-mounted multispectral sensor to achieve this, creating our LudVision data set. To identify the targeted species on the collected images, we propose a new method for detecting Ludwigia p. in multispectral images. The method is based on existing state-of-the-art semantic segmentation methods modified to handle multispectral data. The proposed method achieved a producer's accuracy of 79.9% and a user's accuracy of 95.5%.

This paper presents the details of a system capable of recording on video a traditional class. By traditional class it is meant a teacher, a blackboard and a white canvas where course notes are projected. The system is able to track the movements of the lecturer, while recording it on video at the required frame rate (e.g., 25 fps). The system is also capable of understanding five arm gestures made by the lecturer with the intent of controlling which scenario is recorded: himself, the blackboard or the white canvas. The remaining two gestures are for start/stop the recorder. The system is composed by a Kinect sensor, a video camera, a microphone, one pan-tilt system and one pan system, using a total of three step motors., Comment: 12 pages