The ICARUS EU-FP7 project deals with the development of a set of integrated components to assist search and rescue teams in dealing with the difficult and dangerous, but lifesaving task of finding human survivors. The ICARUS tools consist of assistive unmanned air, ground and sea vehicles, equipped with victim detection sensors. The unmanned vehicles collaborate as a coordinated team, communicating via ad-hoc cognitive radio networking. To ensure optimal human-robot collaboration, these tools are seamlessly integrated into the C4I (command, control, communications, computers, and intelligence) equipment of the human crisis managers and a set of training and support tools is provided to them to learn to use the ICARUS system.

Search‐and‐rescue operations have recently been confronted with the introduction of robotic tools that assist the human search‐and‐rescue workers in their dangerous but life‐saving job of searching for human survivors after major catastrophes. However, the world of search and rescue is highly reliant on strict procedures for the transfer of messages, alarms, data, and command and control over the deployed assets. The introduction of robotic tools into this world causes an important structural change in this procedural toolchain. Moreover, the introduction of search‐and‐rescue robots acting as data gatherers could potentially lead to an information overload toward the human search‐and‐rescue workers, if the data acquired by these robotic tools are not managed in an intelligent way. With that in mind, we present in this paper an integrated data combination and data management architecture that is able to accommodate real‐time data gathered by a fleet of robotic vehicles on a crisis site, and we present and publish these data in a way that is easy to understand by end‐users. In the scope of this paper, a fleet of unmanned ground and aerial search‐and‐rescue vehicles is considered, developed within the scope of the European ICARUS project. As a first step toward the integrated data‐management methodology, the different robotic systems require an interoperable framework in order to pass data from one to another and toward the unified command and control station. As a second step, a data fusion methodology will be presented, combining the data acquired by the different heterogenic robotic systems. The computation needed for this process is done in a novel mobile data center and then (as a third step) published in a software as a service (SaaS) model. The SaaS model helps in providing access to robotic data over ubiquitous Ethernet connections. As a final step, we show how the presented data‐management architecture allows for reusing recorded exercises with real robots and rescue teams for training purposes and teaching search‐and‐rescue personnel how to handle the different robotic tools. The system was validated in two experiments. First, in the controlled environment of a military testing base, a fleet of unmanned ground and aerial vehicles was deployed in an earthquake‐response scenario. The data gathered by the different interoperable robotic systems were combined by a novel mobile data center and presented to the end‐user public. Second, an unmanned aerial system was deployed on an actual mission with an international relief team to help with the relief operations after major flooding in Bosnia in the spring of 2014. Due to the nature of the event (floods), no ground vehicles were deployed here, but all data acquired by the aerial system (mainly three‐dimensional maps) were stored in the ICARUS data center, where they were securely published for authorized personnel all over the world. This mission (which is, to our knowledge, the first recorded deployment of an unmanned aerial system by an official governmental i...

Unmanned Aerial Vehicles (UAV) are becoming an essential tool for evaluating the status and the changes in forest ecosystems. This is especially important in Japan due to the sheer magnitude and complexity of the forest area, made up mostly of natural mixed broadleaf deciduous forests. Additionally, Deep Learning (DL) is becoming more popular for forestry applications because it allows for the inclusion of expert human knowledge into the automatic image processing pipeline. In this paper we study and quantify issues related to the use of DL with our own UAV-acquired images in forestry applications such as: the effect of Transfer Learning (TL) and the Deep Learning architecture chosen or whether a simple patch-based framework may produce results in different practical problems. We use two different Deep Learning architectures (ResNet50 and UNet), two in-house datasets (winter and coastal forest) and focus on two separate problem formalizations (Multi-Label Patch or MLP classification and semantic segmentation). Our results show that Transfer Learning is necessary to obtain satisfactory outcome in the problem of MLP classification of deciduous vs evergreen trees in the winter orthomosaic dataset (with a 9.78% improvement from no transfer learning to transfer learning from a a general-purpose dataset). We also observe a further 2.7% improvement when Transfer Learning is performed from a dataset that is closer to our type of images. Finally, we demonstrate the applicability of the patch-based framework with the ResNet50 architecture in a different and complex example: Detection of the invasive broadleaf deciduous black locust (Robinia pseudoacacia) in an evergreen coniferous black pine (Pinus thunbergii) coastal forest typical of Japan. In this case we detect images containing the invasive species with a 75% of True Positives (TP) and 9% False Positives (FP) while the detection of native trees was 95% TP and 10% FP.

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