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Here we summarise a series of combined workflows for the remote detection and monitoring of archaeological features. We integrate satellite constellations with new field data acquired from multi-sensor drones. Our methods make use of the multitemporal and multisource nature of these images. We present case studies from South Asia to the Mediterranean that are representative of other global drylands where vulnerable sites such as earthen mounds or buried features are obscured by agricultural expansion and encroachment. © 2022 IEEE.

The future success of rice farming will lie in developing productive, sustainable, and resilient farming systems in relation to coexistent ecosystems. Thus, accurate information on agricultural practices and grain yield at optimum temporal and spatial scales is crucial. This study evaluates the potential application of Sentinel-2 (S2) to monitor the dynamics of rice fields in two consecutive seasons (2018 and 2019) in the Ebro Delta growing area. For this purpose, time series of four different spectral indices (NDVI, NDWIMF, NDWIGAO, and BSI), derived from smoothed S2 data at 20 m spatial resolution, were generated. Then, a combination of the first and second derivative analysis on the temporal profiles of spectral indices was used to automatically identify key phenology and management features from regional to field scale; and for estimating crop yield at fields. Features extracted from NDVI and NDWIGAO were used for identifying significant phenological stage dates (i.e. Tillering, Heading Date, and Maturity), and field status (i.e. hydroperiod), although the performance of the proposed method at field-scale was limited by S2 data gaps. The absolute minimum of NDWIMF showed great potential for estimating rice yield, including different cultivars (r = - 0.8), and less sensibility to the number of valid images. Sentinel-2 alone cannot assure a consistent phenology monitoring at all fields but demonstrated strong capabilities for studying the performance of rice fields, thus must be considered in the development of new strategies for the management of rice-growing areas. info:eu-repo/semantics/acceptedVersion

The management of emergencies require the use of multiple resources that must be coordinated to achieve the best possible results. For a good decision-making process, the availability of timely and reliable information about the variables on which such process rely is crucial. Among these variables, the ability to track the position in the field - either outdoors or indoors - of the members of the emergency teams it is of special importance. The IOPES project targets at improving an existing, already operational emergency management system where the tracking of operative staff is integrated. This paper concentrates only in the positioning aspect of IOPES - which encompasses other subsystems, such as portable communications or fast mapping - and describes the approach adopted by the project to perform such integration. This includes the concept itself, the hardware selected and well as the algorithms used to implement a portable, lightweight positioning device able to provide seamless indoor / outdoor positioning that will make possible the real-time tracking of personnel in the field. Promising preliminary results for mixed indoor-outdoor trajectories are as well presented. This contribution is part of the results of IOPES project, co-funded by the European Commission, Directorate-General Humanitarian Aid and Civil Protection (ECHO), under the call UCPM-2019-PP-AG

Accessible cities with accessible services are an old claim of people with reduced mobility. But this demand is still far away of becoming a reality as lot of work is required to be done yet. First step towards accessible cities is to know about real situation of the cities and its pavement infrastructure. Detailed maps or databases on street slopes, access to sidewalks, mobility in public parks and gardens, etc. are required. In this paper, we propose to use smartphone based photogrammetric point clouds, as a starting point to create accessible maps or databases. This paper analyses the performance of these point clouds and the complexity of the image acquisition procedure required to obtain them. The paper proves, through two test cases, that smartphone technology is an economical and feasible solution to get the required information, which is quite often seek by city planners to generate accessible maps. The proposed approach paves the way to generate, in a near term, accessibility maps through the use of point clouds derived from crowdsourced smartphone imagery., NO ACKNOWLEDGEMENTS

Shellfish aquaculture has a major socioeconomic impact on coastal areas, thus it is necessary to develop support tools for its management. In this sense, phytoplankton monitoring is crucial, as it is the main source of food for shellfish farming. The aim of this study was to assess the applicability of Sentinel 2 multispectral imagery (MSI) to monitor the phytoplankton biomass at Ebro Delta bays and to assess its potential as a tool for shellfish management. In situ chlorophyll-a data from Ebro Delta bays (NE Spain) were coupled with several band combination and band ratio spectral indices derived from Sentinel 2A levels 1C and 2A for time-series mapping. The best results (AIC = 72.17, APD < 10%, and MAE < 0.7 mg/m3) were obtained with a simple blue-to-green ratio applied over Rayleigh from Sentinel 2A levels 1C and 2A for time-series mapping. The best results (AIC = 72.17, APD < 10%, and MAE < 0.7 mg/m3) were obtained with a simple blue-to-green ratio applied over Rayleigh corrected images. Sentinel 2–derived maps provided coverage of the farm sites at both bays allowing relating the spatiotemporal distribution of phytoplankton with the environmental forcing under different states of the bays. The applied methodology will be further improved but the results show the potential of using Sentinel 2 MSI imagery as a tool for assessing phytoplankton spatiotemporal dynamics and to encourage better future practices in the management of the aquaculture in Ebro

This communication presents an approach to generate high resolution orthophotos and a digital surface model combining RPAS imagery and ground control support derived from aerial and publicly accessible data instead of organizing a specific surveying campaign. The scope of the presented research is the multi-resolution geospatial data management and this research seeks to contribute to this topic by assuring geo-referencing consistency between RPAS imagery and aerial and historical orthophotos. The approach has been experimentally tested and validated in the Roman city of Pollentia (Alcúdia, Mallorca, Spain) and might be further applied to any other archeological site where aerial imagery and auxiliary data are available., © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

In this manuscript, a NDVI point cloud generator tool based on low-cost active RGB-D sensor is presented. Taking advantage of currently available ROS point cloud generation tools and RGB-D sensor technology (like Microsoft Kinect), that includes an inbuilt active IR camera and a RGB camera, 3D NDVI maps can be quickly and easily generated for vegetation monitoring purposes. When using low-cost sensors for vegetation index estimation, it is necessary to apply a rigorous methodology for extracting reliable information. In this paper, the methodology for NDVI generation using a low-cost sensor as well as experiments to evaluate its performance is presented. The experiments performed show that it is possible to obtain a reliable NDVI point cloud from a Kinect V2.

Modern mobile mapping systems include one or several laser scanners and cameras. The main outputs of these systems are oriented camera images and 3D point clouds. These point clouds can be derived from pairs of overlapping images or using the laser raw data together with platform trajectory. A mobile mapping campaign may include several overlapping areas, generally, the derived point clouds of the same area are not properly registered, due to partial or total GNSS occlusions, multipath and inertial drift and noise. Nowadays, the standard procedure for co-registration between laser and laser and between camera and laser, includes several steps. The first ones are the system calibration where the lever arm and boresight between laser and IMU, and between camera and IMU must be determined. After the calibration steps, a camera and LiDAR point cloud can be derived. Then, a co-registration between LIDAR points clouds and between camera point cloud and LiDAR point cloud are computed. In contrast to the standard approach, in this paper we propose to solve the orientation and calibration of laser and camera data in a single, combined adjustment. Solving the orientation and calibration allows us to implicitly deal with the co-registration problem. The proposed method is based on the identification of common tie features between images and point clouds and their use in a combined adjustment. This common tie features are straight line segments. The preliminary results indicate the feasibility and the potential of the approach.

Rough roads influence the safety of the road users as accident rate increases with increasing unevenness of the road surface. Road roughness regions are required to be efficiently detected and located in order to ensure their maintenance. Mobile Laser Scanning (MLS) systems provide a rapid and cost-effective alternative by providing accurate and dense point cloud data along route corridor. In this paper, an automated algorithm is presented for detecting road roughness from MLS data. The presented algorithm is based on interpolating smooth intensity raster surface from LiDAR point cloud data using point thinning process. The interpolated surface is further processed using morphological and multi-level Otsu thresholding operations to identify candidate road roughness regions. The candidate regions are finally filtered based on spatial density and standard deviation of elevation criteria to detect the roughness along the road surface. The test results of road roughness detection algorithm on two road sections are presented. The developed approach can be used to provide comprehensive information to road authorities in order to schedule maintenance and ensure maximum safety conditions for road users.

This paper presents a method based on the use of straight lines as tie features for simultaneous orientation and calibration of Frame Camera (FC) and Airborne Laser Scanner (ALS) images. For the orientation and calibration of FC images, various methods have been investigated in the past. From those, FC Integrated Sensor Orientation (ISO) has established as the most accurate and robust one. More recently, ISO block adjustment techniques have been successfully applied to ALS orientation and calibration. As of today, for the combined ALS and FC image data sets there are no well-defined procedures as compared to the other two scenarios. Our proposed method essentially reduces to the use of straight line segments as tie features between FC and ALS images. The concept is similar and compatible to the use of tie points in FC ISO and of planar surfaces in ALS ISO thus allowing for the simultaneous orientation and calibration of ALS and FC images that involve, point, line and planar surface tie features. In the paper we describe and derive the two observation equations that relate (1) FC image point measurements to straight lines and (2) distance-parallelism measures between straight lines and planes. The concept and models are validated with a small FC and ALS block with real data. The preliminary results indicate the potential of the approach and confirm its feasibility.

The Real-Aperture-Radar (RAR) interferometry technique consolidated in the last decade as an operational tool for the monitoring of large civil engineering structures as bridges, towers, and buildings. In literature, experimental campaigns collected through a well-known commercial equipment have been widely documented, while the cases where different types of sensors have been tested are a few. On the bases of some experimental tests, a new sensor working at high frequency, providing some improved performances, is here discussed. The core of the proposed system is an off-the-shelf, linear frequency modulated continuous wave device. The development of this apparatus is aimed at achieving a proof-of-concept, tackling operative aspects related to the development of a low cost and reliable system. The capability to detect the natural frequencies of a lightpole has been verified; comparing the results of the proposed sensor with those ones obtained through a commercial system based on the same technique, a more detailed description of the vibrating structure has been achieved. The results of this investigation confirmed that the development of sensors working at higher frequencies, although deserving deeper studies, is very promising and could open new applications demanding higher spatial resolutions at close ranges.