Garden of Eden: Software Packages for the Generation and Rendering of Visually Realistic Trees and Forests

Garden of Eden is an exercise in procedural generation of lifelike worlds. It randomly generates a forest scene of realistically shaped and proportioned asymmetric trees on top of a simple topographical map. This map is then rendered in an HTML5 3D canvas, with support for user navigation. The end result of this project is a sort of game, though without any goal, narrative, or creative purpose. It is simply a static rendering of a natural environment, open for exploration, closed to manipulation, exploring how users find visual pleasure and meaning in virtual environments. The passive interaction of the user is integral to this simulation, as it reflects how one would observe a natural environment; by forcing the user into the same perspective from which they view actual forest environments, Garden of Eden explores the concept of natural, the distinction between real and virtual, and the user\u27s sense of place. All software packages are offered open source, with detailed documentation, for users wishing to create their own arboreal experience

Carisma workshop: from 21 to 23 August 2017 in Helsinki, Finland

Abstraktit201

Animal-Inspired Agile Flight Using Optical Flow Sensing

There is evidence that flying animals such as pigeons, goshawks, and bats use optical flow sensing to enable high-speed flight through forest clutter. This paper discusses the elements of a theory of controlled flight through obstacle fields in which motion control laws are based on optical flow sensing. Performance comparison is made with feedback laws that use distance and bearing measurements, and practical challenges of implementation on an actual robotic air vehicle are described. The related question of fundamental performance limits due to clutter density is addressed.Comment: 20 pages, 7 figure

White paper – On the use of LiDAR data at AmeriFlux sites

Our aim is to inform the AmeriFlux community on existing and upcoming LiDAR technologies (atmospheric Doppler or Raman LiDAR often deployed at flux sites are not considered here), how it is currently used at flux sites, and how we believe it could, in the future, further contribute to the AmeriFlux vision. Heterogeneity in vegetation and ground properties at various spatial scales is omnipresent at flux sites, and 3D mapping of canopy, understory, and ground surface can help move the science forward

Automated Classification of Airborne Laser Scanning Point Clouds

Making sense of the physical world has always been at the core of mapping. Up until recently, this has always dependent on using the human eye. Using airborne lasers, it has become possible to quickly "see" more of the world in many more dimensions. The resulting enormous point clouds serve as data sources for applications far beyond the original mapping purposes ranging from flooding protection and forestry to threat mitigation. In order to process these large quantities of data, novel methods are required. In this contribution, we develop models to automatically classify ground cover and soil types. Using the logic of machine learning, we critically review the advantages of supervised and unsupervised methods. Focusing on decision trees, we improve accuracy by including beam vector components and using a genetic algorithm. We find that our approach delivers consistently high quality classifications, surpassing classical methods

WIFI BASED INDOOR POSITIONING - A MACHINE LEARNING APPROACH

Navigation has become much easier these days mainly due to advancement in satellite technology. The current navigation systems provide better positioning accuracy but are limited to outdoors. When it comes to the indoor spaces such as airports, shopping malls, hospitals or office buildings, to name a few, it will be challenging to get good positioning accuracy with satellite signals due to thick walls and roofs as obstacles. This gap led to a whole new area of research in the field of indoor positioning. Many researches have been conducting experiments on different technologies and successful outcomes have beenseen. Each technology providing indoor positioning capability has its own limitations. In this thesis, different radio frequency (RF) and non-radio frequency (Non-RF) technologies are discussed but focus is set on Wi-Fi for indoor positioning. A demo indoor positioning app is developed for the Technobothnia building at the University of Vaasa premises. This building is already equipped with Wi-Fi infrastructure. A floor plan of the building, radio maps and a fingerprinting database with Wi-Fi signal strength measurements is created with help of tools from HERE technology. The app provides real-time positioning and routing as a future visitor tool. With the exceeding amounts of available data, one of the highly popular fields is applying Machine Learning (ML) to data. It can be applied in many disciplines from medicine to space. In ML, algorithms learn from the data and make predictions. Due to the significant growth in various sensor technologies and computational power, large amounts of data can be stored and processed. Here, the ML approach is also taken to the indoor positioning challenge. An open-source Wi-Fi fingerprinting dataset is obtained from Tampere University and ML algorithms are applied on it for performing indoor positioning. Algorithms are trained with received signal strength (RSS) values with their respective reference coordinates and the user location can be predicted. The thesis provides a performance analysis of different algorithms suitable for future mobile implementations