Unmanned aerial vehicle (UAV) systems are heavily adopted nowadays to collect high-resolution imagery with the purpose of documenting and mapping environment and cultural heritage. Such data are currently processed by programs based on the Structure from Motion (SfM) concept, coming from the computer vision community, rather than from classical photogrammetry. It is interesting to check whether some widely accepted rules coming from old-fashioned photogrammetry still holds: the relation between accuracy and ground sampling distance (GSD), the ratio between the vertical and horizontal accuracy, accuracy estimated on ground control points (GCPs) vs. that estimated with check points (CPs) also in relation to their ratio and distribution. To face the envisaged aspects, the paper adopts a comparative approach, as several programs are used and numerous configurations considered. The paper illustrates the dataset adopted, the carefully tuned processing strategies and bundle block adjustment (BBA) results in terms of accuracy for both GCPs and CPs. Finally, a leave-one-out (LOO) cross-validation strategy is proposed to assess the accuracy for one of the proposed configurations. Some of the reported results were previously presented in the 5th GISTAM Conference.

This paper is about the geometric and radiometric consistency of diverse and overlapping datasets acquired with the Parrot Sequoia camera. The multispectral imagery datasets were acquired above agricultural fields in Northern Italy and radiometric calibration images were taken before each flight. Processing was performed with the Pix4Dmapper suite following a single-block approach: images acquired in different flight missions were processed in as many projects, where different block orientation strategies were adopted and compared. Results were assessed in terms of geometric and radiometric consistency in the overlapping areas. The geometric consistency was evaluated in terms of point cloud distance using iterative closest point (ICP), while the radiometric consistency was analyzed by computing the differences between the reflectance maps and vegetation indices produced according to adopted processing strategies. For normalized difference vegetation index (NDVI), a comparison with Sentinel-2 was also made. This paper will present results obtained for two (out of several) overlapped blocks. The geometric consistency is good (root mean square error (RMSE) in the order of 0.1 m), except for when direct georeferencing is considered. Radiometric consistency instead presents larger problems, especially in some bands and in vegetation indices that have differences above 20%. The comparison with Sentinel-2 products shows a general overestimation of Sequoia data but with similar spatial variations (Pearson’s correlation coefficient of about 0.7, p-value < 2.2 × 10⁻¹⁶).

The global healthcare landscape is continuously changing throughout the world as technology advances, leading to a gradual change in lifestyle. Several diseases such as asthma and cardiovascular conditions are becoming more diffuse, due to a rise in pollution exposure and a more sedentary lifestyle. Healthcare providers deal with increasing new challenges, and thanks to fast-developing big data technologies, they can be faced with systems that provide direct support to citizens. In this context, within the EU-funded Participatory Urban Living for Sustainable Environments (PULSE) project, we are implementing a data analytic platform designed to provide public health decision makers with advanced approaches, to jointly analyze maps and geospatial information with healthcare and air pollution data. In this paper we describe a component of such platforms, which couples deep learning analysis of urban geospatial images with healthcare indexes collected by the 500 Cities project. By applying a pre-learned deep Neural Network architecture, satellite images of New York City are analyzed and latent feature variables are extracted. These features are used to derive clusters, which are correlated with healthcare indicators by means of a multivariate classification model. Thanks to this pipeline, it is possible to show that, in New York City, health care indexes are significantly correlated to the urban landscape. This pipeline can serve as a basis to ease urban planning, since the same interventions can be organized on similar areas, even if geographically distant.

The digital twin is one of the most promising technologies for realizing smart cities in terms of planning and management. For this purpose, Milan, Italy, has started a project to acquire aerial nadir and oblique images and LiDAR and terrestrial mobile mapping data. The Leica CityMapper-2 hybrid sensor has been used for aerial surveys as it can capture precise and high-resolution multiple data (imagery and LiDAR). The surveying activities are completed, and quality checks are in progress. This paper concerns assessing aerial LiDAR data of a significant part of the metropolitan area, particularly evaluating the accuracy, precision, and congruency between strips and the point density estimation. The analysis has been conducted by exploiting a ground control network of GNSS and terrestrial LiDAR measurements created explicitly for this purpose. The vertical component has an accuracy root mean square error (RMSE) of around 5 cm, and a horizontal component of around 12 cm. Meanwhile, the precision RMSE ranges from 2 to 8 cm. These values are suitable for generating products such as DSM/DTM.

Rice farming in Italy accounts for about 50% of the EU’s rice area and production. Precision agriculture has entered the scene to enhance sustainability, cut pollution, and ensure food security. Various studies have used remote sensing tools like satellites and drones for multispectral imaging. While Sentinel-2 is highly regarded for precision agriculture, it falls short for specific applications, like at the “Riserva San Massimo” (Gropello Cairoli, Lombardia, Northern Italy) rice farm, where irregularly shaped crops need higher resolution and frequent revisits to deal with cloud cover. A prior study that compared Sentinel-2 and the higher-resolution PlanetScope constellation for vegetative indices found a seasonal miscalibration in the Normalized Difference Vegetation Index (NDVI) and in the Normalized Difference Red Edge Index (NDRE). Dr. Agr. G.N. Rognoni, a seasoned agronomist working with this farm, stresses the importance of studying the radiometric intercalibration between the PlanetScope and Sentinel-2 vegetative indices to leverage the knowledge gained from Sentinel-2 for him to apply variable rate application (VRA). A high-resolution SkySat image, taken almost simultaneously with a pair of Sentinel-2 and PlanetScope images, offered a chance to examine if the irregular distribution of vegetation and barren land within rice fields might be a factor in the observed miscalibration. Using an unsupervised pixel-based image classification technique on SkySat imagery, it is feasible to split rice into two subclasses and intercalibrate them separately. The results indicated that combining histograms and agronomists’ expertise could confirm SkySat classification. Moreover, the uneven spatial distribution of rice does not affect the seasonal miscalibration object of past studies, which can be adjusted using the methods described here, even with images taken four days apart: the first method emphasizes accuracy using linear regression, histogram shifting, and histogram matching; whereas the second method is faster and utilizes only histogram matching.

Prior research has shown that for specific periods, vegetation indices from PlanetScope and Sentinel-2 (used as a reference) must be aligned to benefit from the experience of Sentinel-2 and utilize techniques such as data fusion. Even during the worst-case scenario, it is possible through histogram matching to calibrate PlanetScope indices to achieve the same values as Sentinel-2 (useful also for proxy). Based on these findings, the authors examined the effectiveness of linear regression in aligning individual bands prior to computing indices to determine if the bands are shifted differently. The research was conducted on five important bands: Red, Green, Blue, NIR, and RedEdge. These bands allow for the computation of well-known vegetation indices like NDVI and NDRE, and soil indices like Iron Oxide Ratio and Coloration Index. Previous research showed that linear regression is not sufficient by itself to align indices in the worst-case scenario. However, this paper demonstrates its efficiency in achieving accurate band alignment. This finding highlights the importance of considering specific scaling requirements for bands obtained from different satellite sensors, such as PlanetScope and Sentinel-2. Contemporary images acquired by the two sensors during May and July demonstrated different behaviors in their bands; however, linear regression can align the datasets even during the problematic month of May.