We present a methodology to use a UAV (unmanned aerial vehicle) to perform photogrammetric surveys and detailed geological mapping in mountain areas. This work is specially related to the presented case study with the aim to realize geomorphological maps from UAVs, since they can house different types of sensors and acquire data more rapidly and cheaply than traditional geological surveys directly obtained with field observations. This work explains how UAVs can obtain digital terrain models, orthophotos and 3D models in order to create slope and aspect maps for geological purposes. By integrating data from UAVs with geological surveys made on the field, geological maps can be produced where many of the geological elements are presented. This paper presents the integration of geomatics and geological techniques. Starting from UAV slope map and orthophotos, a new geological map was created in a faster and more detailed way compared to traditional geological survey on the ground. The application of this method regards a sector of the Western Alps (NW Italy), formed by glaciers and deep-seated gravitational slope deformations.

During the past years, UAVs (Unmanned Aerial Vehicles) became very popular as low-cost image acquisition platforms since they allow for high resolution and repetitive flights in a flexible way. One application is to monitor dynamic scenes. However, the fully automatic co-registration of the acquired multi-temporal data still remains an open issue. Most UAVs are not able to provide accurate direct image georeferencing and the co-registration process is mostly performed with the manual introduction of ground control points (GCPs), which is time consuming, costly and sometimes not possible at all. A new technique to automate the co-registration of multi-temporal high resolution image blocks without the use of GCPs is investigated in this paper. The image orientation is initially performed on a reference epoch and the registration of the following datasets is achieved including some anchor images from the reference data. The interior and exterior orientation parameters of the anchor images are then fixed in order to constrain the Bundle Block Adjustment of the slave epoch to be aligned with the reference one. The study involved the use of two different datasets acquired over a construction site and a post-earthquake damaged area. Different tests have been performed to assess the registration procedure using both a manual and an automatic approach for the selection of anchor images. The tests have shown that the procedure provides results comparable to the traditional GCP-based strategy and both the manual and automatic selection of the anchor images can provide reliable results.

In the past decades, technology-based agriculture, also known as Precision Agriculture (PA) or smart farming, has grown, developing new technologies and innovative tools to manage data for the whole agricultural processes. In this framework, geographic information, and spatial data and tools such as UAVs (Unmanned Aerial Vehicles) and multispectral optical sensors play a crucial role in the geomatics as support techniques. PA needs software to store and process spatial data and the Free and Open Software System (FOSS) community kept pace with PA’s needs: several FOSS software tools have been developed for data gathering, analysis, and restitution. The adoption of FOSS solutions, WebGIS platforms, open databases, and spatial data infrastructure to process and store spatial and nonspatial acquired data helps to share information among different actors with user-friendly solutions. Nevertheless, a comprehensive open-source platform that, besides processing UAV data, allows directly storing, visualising, sharing, and querying the final results and the related information does not exist. Indeed, today, the PA’s data elaboration and management with a FOSS approach still require several different software tools. Moreover, although some commercial solutions presented platforms to support management in PA activities, none of these present a complete workflow including data from acquisition phase to processed and stored information. In this scenario, the paper aims to provide UAV and PA users with a FOSS-replicable methodology that can fit farming activities’ operational and management needs. Therefore, this work focuses on developing a totally FOSS workflow to visualise, process, analyse, and manage PA data. In detail, a multidisciplinary approach is adopted for creating an operative web-sharing tool able to manage Very High Resolution (VHR) agricultural multispectral-derived information gathered by UAV systems. A vineyard in Northern Italy is used as an example to show the workflow of data generation and the data structure of the web tool. A UAV survey was carried out using a six-band multispectral camera and the data were elaborated through the Structure from Motion (SfM) technique, resulting in 3 cm resolution orthophoto. A supervised classifier identified the phenological stage of under-row weeds and the rows with a 95% overall accuracy. Then, a set of GIS-developed algorithms allowed Individual Tree Detection (ITD) and spectral indices for monitoring the plant-based phytosanitary conditions. A spatial data structure was implemented to gather the data at canopy scale. The last step of the workflow concerned publishing data in an interactive 3D webGIS, allowing users to update the spatial database. The webGIS can be operated from web browsers and desktop GIS. The final result is a shared open platform obtained with nonproprietary software that can store data of different sources and scales.

An accurate description of watercourses helps to easily understand their behaviour both for routine hydrological investigations and in disasters cases. Knowing the position and the dimension of the supply sources of the river is crucial, since they have a direct influence on its behaviour; however, they are not often easily identified, and almost always completely unknown. Aerial thermal data can be critical in order to rapidly reveal and map the river supply source points for extensive areas. Using unmanned aerial vehicles (UAVs) to collect very easily and quickly high-resolution information is thus very interesting. UAVs can house many sensors for performing investigations in different bands of the spectrum, including thermal data. This work explains how UAVs can be used to collect this kind of data (through RGB and thermal devices) and presents a strategy to easily integrate the information from these different sensors. Integration procedures, also applicable in emergency situations, were developed. These methods do not require points on the ground and can be performed using commercial devices.

In this paper the performances (in terms of accuracy) of an hybrid positioning technique, that brings together an image based recognition approach and MEMS (Micro Electro-Mechanical Systems) technology, will be investigated. The image recognition based (IRB) positioning has already been well investigated in the past and represents a good technology for navigation in GNSS denied environment, like indoor or urban canyon, where GNSS accuracy is poor (tens of meters accuracy). However, for practical exploitation of IRB positioning with smartphones the following main problems must be taken into account. The first one is the optimization of the battery, that implies a proper use of the frame rate. A second main issue is represented by latencies due to image processing algorithms and visual search solutions that may require a cloud architecture to manage the size of the database. To overcome the above problems in IRB, reduction of the frame rate and latencies compensation, inertial platform built with MEMS technology may be exploited. Two different approaches are taken into account for IRB positioning: the first one is represented by a real-time solution obtainable through single images; the second one is achieved from a commercial software that locates all the frames (not suitable for real time applications). The paper presents a methodology that fuses IRB with MEMS measurements. Results have shown that with single IRB fixes fused together with inertial navigation the standard errors with the 95th percentile are about 1.66 m, 2.36 m and 3.16 m if the interval between two IRB localizations are 1, 2 and 5 sec respectively, while these errors decrease up to 0.64 m, 1.79 m and 2.05 m if the commercial hybrid absolute and relative orientation solution is considered. We can affirm that the weakness in single photograms positioning approach resides in poor orientation estimation of photograms that can grow up to some tens of degrees when poor geometry is picked in the camera scene. With this study we have demonstrate that the hybrid IRB positioning technique coupled with an INS instrument is useful for indoor navigation because errors are less than 2 m and 1 m, with intervals of about 2 s and 1 s between two images, respectively.