No abstract

In this paper, we demonstrate the multiple points of innovation when combining blockchain technology with Internet of Things (IoT) and security frameworks. The deployment and use of IoT device networks in smart city environments has produced an enormous amount of data. The fact that those data are possessed by multiple sources that use independent systems for data collection, storage, and use impedes the exploitation of their value. Blockchains, as distributed ledgers, can be used for addressing the development of a universal system for data collection and distribution. Smart contracts can be used to automate all the processes of such a network, while at the same time, blockchain and the InterPlanetary File System (IPFS) protect sensitive data through anonymity and distributed storage. An innovative and open IoT blockchain market of applications, data, and services is proposed that: (i) provides the framework upon which objects and people can exchange value in form of virtual currencies, for assets (data and services) received; (ii) defines the motivation incentives according to social and business context for humans and smart objects to interact. The specific marketplace is piloted through a cross-border trial between Santander and Fujisawa, in the context of the M-Sec project, validating thus the interoperability, efficiency, and data protection principles.

No abstract

<p>As part of a project aiming to support FSC certified logging concessions in their tasks of forest inventory and management, we collected aerial imagery over 9000 ha of tropical forests in Northern Congo using long range Unmanned Aerial Vehicles (UAVs). Once processed into orthomosaics, the aerial imagery is used in combination with reference training samples to train a deep learning object detection model (FasterRCNN) capable of detecting and predicting tree species. The remoteness and diversity of these forests make both data acquisition and generation of a training dataset challenging. Unlike natural images containing common objects like cars, bicycles, cats and dogs, there is no easy way to create a training dataset of tree species from overhead imagery of tropical forests. The first reason is that a human operator cannot as easily recognize and label objects. The second reason is that the polymorphism of tree species, phenological variations and uncertainty associated with visual recognition makes the exhaustive labeling of all instances of each class very difficult. Such exhaustive labeling is required to successfully train any object detection model. To overcome these challenges we built an interactive and ergonomic interface that allows a human operator to work in a spatial context, being guided by the approximate geographic location of already inventoried trees. We solved the issue of non-exhaustive instance labeling by building synthetic images, hence allowing full control of the training data. In addition to these specific developments related to training data generation, we will present details of the UAV missions, modelling results on synthetic images, and finally preliminary results of model transfer to aerial imagery.</p>