Open source R for applying machine learning to RPAS remote sensing images

The increase in the number of remote sensing platforms, ranging from satellites to close-range Remotely Piloted Aircraft System (RPAS), is leading to a growing demand for new image processing and classification tools. This article presents a comparison of the Random Forest (RF) and Support Vector Machine (SVM) machine-learning algorithms for extracting land-use classes in RPAS-derived orthomosaic using open source R packages. The camera used in this work captures the reflectance of the Red, Blue, Green and Near Infrared channels of a target. The full dataset is therefore a 4-channel raster image. The classification performance of the two methods is tested at varying sizes of training sets. The SVM and RF are evaluated using Kappa index, classification accuracy and classification error as accuracy metrics. The training sets are randomly obtained as subset of 2 to 20% of the total number of raster cells, with stratified sampling according to the land-use classes. Ten runs are done for each training set to calculate the variance in results. The control dataset consists of an independent classification obtained by photointerpretation. The validation is carried out(i) using the K-Fold cross validation, (ii) using the pixels from the validation test set, and (iii) using the pixels from the full test set. Validation with K-fold and with the validation dataset show SVM give better results, but RF prove to be more performing when training size is larger. Classification error and classification accuracy follow the trend of Kappa index

Benchmark of machine learning methods for classification of a Sentinel-2 image

Thanks to mainly ESA and USGS, a large bulk of free images of the Earth is readily available nowadays. One of the main goals of remote sensing is to label images according to a set of semantic categories, i.e. image classification. This is a very challenging issue since land cover of a specific class may present a large spatial and spectral variability and objects may appear at different scales and orientations. In this study, we report the results of benchmarking 9 machine learning algorithms tested for accuracy and speed in training and classification of land-cover classes in a Sentinel-2 dataset. The following machine learning methods (MLM) have been tested: linear discriminant analysis, k-nearest neighbour, random forests, support vector machines, multi layered perceptron, multi layered perceptron ensemble, ctree, boosting, logarithmic regression. The validation is carried out using a control dataset which consists of an independent classification in 11 land-cover classes of an area about 60 km2, obtained by manual visual interpretation of high resolution images (20 cm ground sampling distance) by experts. In this study five out of the eleven classes are used since the others have too few samples (pixels) for testing and validating subsets. The classes used are the following: (i) urban (ii) sowable areas (iii) water (iv) tree plantations (v) grasslands. Validation is carried out using three different approaches: (i) using pixels from the training dataset (train), (ii) using pixels from the training dataset and applying cross-validation with the k-fold method (kfold) and (iii) using all pixels from the control dataset. Five accuracy indices are calculated for the comparison between the values predicted with each model and control values over three sets of data: the training dataset (train), the whole control dataset (full) and with k-fold cross-validation (kfold) with ten folds. Results from validation of predictions of the whole dataset (full) show the random forests method with the highest values; kappa index ranging from 0.55 to 0.42 respectively with the most and least number pixels for training. The two neural networks (multi layered perceptron and its ensemble) and the support vector machines - with default radial basis function kernel - methods follow closely with comparable performanc

A geodatabase for multisource data applied to cultural heritage: The case study of Villa Revedin Bolasco

In this paper we present the results of the development of a Web-based archiving and documenting system aimed to the management of multisource and multitemporal data related to cultural heritage. As case study we selected the building complex of Villa Revedin Bolasco in Castefranco Veneto (Treviso, Italy) and its park. Buildings and park were built in XIX century after several restorations of the original XIV century area. The data management system relies on a geodatabase framework, in which different kinds of datasets were stored. More specifically, the geodatabase elements consist of historical information, documents, descriptions of artistic characteristics of the building and the park, in the form of text and images. In addition, we used also floorplans, sections and views of the outer facades of the building extracted by a TLS-based 3D model of the whole Villa. In order to manage and explore these rich dataset, we developed a geodatabase using PostgreSQL and PostGIS as spatial plugin. The Web-GIS platform, based on HTML5 and PHP programming languages, implements the NASA Web World Wind virtual globe, a 3D virtual globe we used to enable the navigation and interactive exploration of the park. Furthermore, through a specific timeline function, the user can explore the historical evolution of the building complex

Analysis of geospatial behaviour of visitors of urban gardens: is positioning via smartphones a valid solution?

Tracking locations is practical and speditive with smartphones, as they are omnipresent devices, relatively cheap, and have the necessary sensors for positioning and networking integrated in the same box. Nowadays recent models have GNSS antennas capable of receiving multiple constellations. In the proposed work we test the hypothesis that GNSS positions directly recorded by smartphones can be a valid solution for spatial analysis of people's behaviour in an urban garden. Particular behaviours can be linked to therapeutic spots that promote health and well-being of visitors. Three parts are reported: (i) assessment of the accuracy of the positions relative to a reference track, (ii) implementation of a framework for automating transmission and processing of the location information, (iii) analysis of preferred spots via spatial analytics. Different devices were used to survey at different times and with different methods, i.e. in the pocket of the owner or on a rigid frame. Accuracy was estimated using distance of each located point to the reference track, and precision was estimated with static multiple measures. A chat-bot through the Telegram application was implemented to allow users to send their data to a centralized computing environment thus automating the spatial analysis. Results report a horizontal accuracy below ~2.3 m at 95% confidence level, without significant difference between surveys, and very little differences between devices. GNSS-only and assisted navigation with telephone cells also did not show significant difference. Autocorrelation of the residuals over time and space showed strong consistency of the residuals, thus proving a valid solution for spatial analysis of walking behaviour

Geo-Spatial Support for Assessment of Anthropic Impact on Biodiversity

This paper discusses a methodology where geo-spatial analysis tools are used to quantify risk derived from anthropic activities on habitats and species. The method has been developed with a focus on simplification and the quality of standard procedures set on flora and fauna protected by the European Directives. In this study case, the DPSIR (Drivers, Pressures, State, Impacts, Responses) is applied using spatial procedures in a geographical information system (GIS) framework. This approach can be inserted in a multidimensional space as the analysis is applied to each threat, pressure and activity and also to each habitat and species, at the spatial and temporal scale. Threats, pressures and activities, stress and indicators can be managed by means of a geo-database and analyzed using spatial analysis functions in a tested GIS workflow environment. The method applies a matrix with risk values, and the final product is a geo-spatial representation of impact indicators, which can be used as a support for decision-makers at various levels (regional, national and European)

Responding to Large-Scale Forest Damage in an Alpine Environment with Remote Sensing, Machine Learning, and Web-GIS

This paper reports a semi-automated workflow for detection and quantification of forest damage from windthrow in an Alpine region, in particular from the Vaia storm in October 2018. A web-GIS platform allows to select the damaged area by drawing polygons; several vegetation indices (VIs) are automatically calculated using remote sensing data (Sentinel-2A) and tested to identify the more suitable ones for quantifying forest damage using cross-validation with ground-truth data. Results show that the mean value of NDVI and NDMI decreased in the damaged areas, and have a strong negative correlation with severity. RGI has an opposite behavior in contrast with NDVI and NDMI, as it highlights the red component of the land surface. In all cases, variance of the VI increases after the event between 0.03 and 0.15. Understorey not damaged from the windthrow, if consisting of 40% or more of the total cover in the area, undermines significantly the sensibility of the VIs to detecting and predicting severity. Using aggregational statistics (average and standard deviation) of VIs over polygons as input to a machine learning algorithm, i.e., Random Forest, results in severity prediction with regression reaching a root mean square error (RMSE) of 9.96, on a severity scale of 0–100, using an ensemble of area averages and standard deviations of NDVI, NDMI, and RGI indices. The results show that combining more than one VI can significantly improve the estimation of severity, and web-GIS tools can support decisions with selected VIs. The reported results prove that Sentinel-2 imagery can be deployed and analysed via web-tools to estimate forest damage severity and that VIs can be used via machine learning for predicting severity of damage, with careful evaluation of the effect of understorey in each situation

An open source virtual globe rendering engine for 3D applications: NASA World Wind

Background NASA World Wind is an open source application-programming interface for developing geographic information systems based on a virtual globe rendering engine representing a planet. NASA World Wind provides the ideal environment for scientific data, their analysis, visual representation and interaction with users, in a single platform, which can be deployed both as a Java desktop application (NASA World Wind) or a JavaScript web application (ESA-NASA Web World Wind). Results We give here an overview of the project, reporting details regarding current development direction, with state of the art examples. The European Space Agency is now partnering with NASA on development of the "ESA-NASA Web World Wind"; this high degree of interest from other agencies will boost future project productivity. Conclusions With this contribution, we want to increase awareness of NASA World Wind as a unique opportunity to foster collaboration between scientists, developers and other stakeholders, enriching knowledge of our Earth’s complexity

Geomatics as support to remote sensing data analysis from UAV technology using GIS open source platforms

The recent years saw a growing usage of remote sensing platforms, such as satellites and unmanned aircraft vehicles (UAVs). The Copernicus observation programme is an example of a new satellite constellation improving and promoting easy free access dataset, timely updated. The high popularity of UAVs is related to the obtaining high-resolution data, quickly at a relatively low cost. As result, sensors with high spatial and temporal resolution produce a great number of data, and these data increase exponentially. Consequently, the software for image processing play a key role in the diffusion of this technology. The satellites take advantage of dedicated software for imagery analysis. The UAVs use structure from motion techniques for photogrammetric processing. However, the data analysis for both sensors is based on the classic pixel-based and object-based remote sensing techniques. Moreover, there is a growing demand for innovative tools to analyse huge dataset and to integrate the information for environmental analyses, monitor geomorphological aspects and land use studies, in particular in rural areas. This thesis aims to study how GIS software using open source libraries can integrate information extracted from the satellite and the UAVs imagery, using a machine learning approach in a multilevel remote sensing framework. The main research questions are: (1) Can the classic techniques of remote sensing be used to extract suitable land use/land cover (LULC) maps – suitable in terms of classification accuracy – for the very high-resolution imagery of UAVs? (2) Can information from images of UAVs be merged with data from satellite images in the same area to achieve better results? (3) Which methods are optimal to analyse imagery of UAVs, and which benefits can be achieved through the use of more sophisticated techniques, such as the integration of multisource spatial information? To answer the research questions, a multi-level framework has been developed to integrate the information derived from remote sensing techniques. The framework has been implemented using R cran libraries, and it includes a machine learning benchmark as an alternative to pixel-based and object-based approach. The benchmark allows for testing several algorithms, in terms of accuracy and processing time for classifying LULC maps. The thesis presents the result of five papers, and the main findings relating to the major research questions can be summarized as follows: (1) The classic remote sensing techniques can be applied to UAVs high-resolution imagery to obtain a fast image classification. The maximum likelihood algorithm has a better result than the minimum distance algorithm in terms of accuracy.8 (2) It is possible to integrate the satellite and UAVs temporal series. The scale affects the size of the training areas. Thus, to integrate the satellite and UAV information, the size of the regions of interest (ROIs) shall be larger than the ground sample distance (GSD) of the satellite. The use of large ROIs can avoid the noise from nearby areas. In addition, to limit the noise due to high-resolution images, the value of the digital number (DN) inside the ROIs should be homogenous. (3) The machine learning can be applied to both satellite and UAVs imagery and integrate spatial information. The dataset derived from high-resolution imagery can be considered as big data paradigm, in terms of data size and the processing time. Using a subset greater than the 8% of the total is possible to have a good results (kappa score ranges between 80% and 90%) and fast processing time. In addition, sensitivity analysis can help to define the contribution of each layer of the multi-level framewor

Solar Irradiance Modelling with NASA WW GIS Environment

In this work we present preliminary results regarding a proof-of-concept project which aims to provide tools for mapping the amount of solar radiation reaching surfaces of objects, accounting for obstructions between objects themselves. The implementation uses the NASA World Wind development platform (NASA WW) to model the different physical phenomena that participate in the process, from the calculation of the Sun’s position relative to the area that is being considered, to the interaction between atmosphere and solid obstructions, e.g., terrain or buildings. A more complete understanding of the distribution of energy from the Sun illuminating elements on the Earth’s surface adds value to applications ranging from planning the renewable energy potential of an area to ecological analyses