SPECIFIC ALPINE ENVIRONMENT LAND COVER CLASSIFICATION METHODOLOGY: GOOGLE EARTH ENGINE PROCESSING FOR SENTINEL-2 DATA

Abstract. Land Cover (LC) plays a key role in many disciplines and its classification from optical imagery is one of the prevalent applications of remote sensing. Besides years of researches and innovation on LC, the classification of some areas of the World is still challenging due to environmental and climatic constraints, such as the one of the mountainous chains. In this contribution, we propose a specific methodology for the classification of the Land Cover in mountainous areas using Sentinel 2, 1C-level imagery. The classification considers some specific high-altitude mountainous classes: clustered bare soils that are particularly prone to erosion, glaciers, and solid-rocky areas. It consists of a pixel-based multi-epochs classification using random forest algorithm performed in Google Earth Engine (GEE). The study area is located in the western Alps between Italy and France and the analyzed dataset refers to 2017–2019 imagery captured in the summertime only. The dataset was pre-processed, enriched of derivative features (radiometric, histogram-based and textural). A workflow for the reduction of the computational effort for the classification, which includes correlation and importance analysis of input features, was developed. Each image of the dataset was separately classified using random forest classification algorithm and then aggregated each other by the most frequent pixel value. The results show the high impact of textural features in the separation of the mountainous-specific classes the overall accuracy of the final classification achieves 0.945

LAYING THE FOUNDATION FOR AN ARTIFICIAL NEURAL NETWORK FOR PHOTOGRAMMETRIC RIVERINE BATHYMETRY

Abstract. This work aims to test the effectiveness of artificial intelligence for correcting water refraction in shallow inland water using very high-resolution images collected by Unmanned Aerial Systems (UAS) and processed through a total FOSS workflow. The tests focus on using synthetic information extracted from the visible component of the electromagnetic spectrum. An artificial neural network is created using data of three morphologically similar alpine rivers. The RGB information, the SfM depth and seven radiometric indices are calculated and stacked in an 11-bands raster (input dataset). The depths are calculated as the difference between the Up component of the bathymetry cross-sections and the water surface quotas and constitute the dependent variable of the regression. The dataset is then scaled. The observations of one of the analyzed case studies are used as the unseen dataset to test the generalization capability of the model. The remaining observations are divided into test (20%) and training (80%) datasets. The generated NN is a 3-layer MLP model with one hidden layer and the Rectified Linear Unit (ReLU) and sigmoid activation functions. The weights are initialized to small Gaussian random values, and kernel regularizers, L1 and L2, are added to reduce the overfitting. Weights are updated with the Adam search technique, and the mean squared error is the loss function. The importance and significance of 11 variables are assessed. The model has a 0.70 r-squared score on the test dataset and 0.77 on the training dataset. The MAE is 0.06 and the RMSE 0.08, similar results obtained from the unseen dataset. Although the good metrics, the model shows some difficulties generalizing swallow depths

Towards a FOSS Automatic Classification of Defects for Bridges Structural Health Monitoring

Bridges are among the most important structures of any road network. During their service life, they are subject to deterioration which may reduce their safety and functionality. The detection of bridge damage is necessary for proper maintenance activities. To date, assessing the health status of the bridge and all its elements is carried out by identifying a series of data obtained from visual inspections, which allows the mapping of the deterioration situation of the work and its conservation status. There are, however, situations where visual inspection may be difficult or impossible, especially in critical areas of bridges, such as the ceiling and corners. In this contribution, the authors acquire images using a prototype drone with a low-cost camera mounted upward over the body of the drone. The proposed solution was tested on a bridge in the city of Turin (Italy). The captured data was processed via photogrammetric process using the open-source Micmac solution. Subsequently, a procedure was developed with FOSS tools for the segmentation of the orthophoto of the intrados of the bridge and the automatic classification of some defects found on the analyzed structure. The paper describes the adopted approach showing the effectiveness of the proposed methodology

Riparian ecosystems mapping at fine scale: a density approach based on multi-temporal UAV photogrammetric point clouds

In recent years, numerous directives worldwide have addressed the conservation and restoration of riparian corridors, activities that rely on continuous vegetation mapping to understand its volumetric features and health status. Mapping riparian corridors requires not only fine-scale resolution but also the coverage of relatively large areas. The use of Unmanned Aerial Vehicles (UAV) allows for meeting both conditions, although the cost-effectiveness of their use is highly influenced by the type of sensor mounted on them. Few works have so far investigated the use of photogrammetric sensors for individual tree crown detection, despite being cheaper than the most common Light Detection and Ranging (LiDAR) ones. This work aims to improve the individual crown detection from UAV-photogrammetric datasets in a twofold way. Firstly, the effectiveness of a new approach that has already achieved interesting results in LiDAR applications was tested for photogrammetric point clouds. The test was carried out by comparing the accuracy achieved by the new approach, which is based on the point density features of the analysed dataset, with those related to the more common local maxima and textural methods. The results indicated the potentiality of the density-based method, which achieved accuracy values (0.76 F-score) consistent with the traditional methods (0.49–0.80 F-score range) but was less affected by under- and over-fitting. Secondly, the potential improvement of working on intra-annual multi-temporal datasets was assessed by applying the density-based approach to seven different scenarios, three of which were constituted by single-epoch datasets and the remaining given by the joining of the others. The F-score increased from 0.67 to 0.76 when passing from single- to multi-epoch datasets, aligning with the accuracy achieved by the new method when applied to LiDAR data. The results demonstrate the potential of multi-temporal acquisitions when performing individual crown detection from photogrammetric data

INTEGRATION OF UAV-LIDAR AND UAV-PHOTOGRAMMETRY FOR INFRASTRUCTURE MONITORING AND BRIDGE ASSESSMENT

The health assessment of strategic infrastructures and bridges represents a critical variable for planning appropriate maintenance operations. The high costs and complexity of traditional periodical monitoring with elevating platforms have driven the search for more efficient and flexible methods. Indeed, recent years have seen the growing diffusion and adoption of non-invasive approaches consisting in the use of Unmanned Aerial Vehicles (UAVs) for applications that range from visual inspection with optical sensors to LiDAR technologies for rapid mapping of the territory. This study defines two different methodologies for bridge inspection. A first approach involving the integration of traditional topographic and GNSS techniques with TLS and photogrammetry with cameras mounted on UAV was compared with a UAV-LiDAR method based on the use of a DJI Matrice 300 equipped with a LiDAR DJI Zenmuse L1 sensor for a manual flight and an automatic one. While the first workflow resulted in a centimetric accurate but time-consuming model, the UAV-LiDAR resulting point cloud’s georeferencing accuracy resulted to be less accurate in the case of the manual flight under the bridge for GNSS signal obstruction. However, a photogrammetric model reconstruction phase made with Ground Control Points and photos taken by the L1-embedded camera improved the overall accuracy of the workflow, that could be employed for flexible low-cost mapping of bridges when medium level accuracy (5–10 cm) is accepted. In conclusion, a solution for integrating interactively final 3D products in a Bridge Management System environment is presented

Raspberry pi 3 multispectral low-cost sensor for uav based remote sensing. case study in south-west Niger

The technology of UAV (Unmanned Aerial Vehicles) is rapidly improving and UAV-integrated sensors have kept up with it, providing more efficient and effective solutions. One of the most sought-after characteristics of on-board sensors is the low costing associated to good quality of the collected data. This paper proposes a very low-cost multiband sensor developed on a Raspberry device and two Raspberry Pi 3 cameras that can be used in photogrammetry from drone applications. The UAV-integrated radiometric sensor and its performance were tested in in two villages of South-west Niger for the detection of temporary surface water bodies (or Ephemeral water bodies): zones of seasonal stagnant water within villages threatening the viability and people's health. The Raspberry Pi 3 cameras employed were a regular RGB Pi camera 2 (Red, Green, Blue) and a NoIR Pi 3 camera v2 (regular RGB without IR filter) with 8MPX resolution. The cameras were geometrically calibrated and radiometrically tested before the survey in the field. The results of the photogrammetry elaborations were 4 orthophotos (a RGB and NoIRGB orthophoto for each village). The Normalized Difference Water Index (NDWI) was calculated. The index allowed the localization and the contouring of the temporary surface water bodies present in the villages. The data were checked against the data collected with a Sony (ILCE-5100). Very high correspondence between the different data was detected. Raspberry-based sensors demonstrated to be a valid tool for the data collection in critical areas