DEFORESTATION MAPPING USING SENTINEL-1 AND OBJECT-BASED RANDOM FOREST CLASSIFICATION ON GOOGLE EARTH ENGINE

Abstract. Deforestation can be defined as the conversion of forest land cover to another type. It is a process that has massively accelerated its rate and extent in the last several decades. Mainly due to human activities related to socio-economic processes as population growth, expansion of agricultural land, wood extraction, etc. In the meantime, there are great efforts by governments and agencies to reduce these deforestation processes by implementing regulations, which cannot always be properly monitored whether are followed or not. In this work is proposed an approach that can provide forest loss estimations for a short period of time, by using Synthetic Aperture Radar imagery for an area in the Brazilian Amazon. SAR are providing data with almost no alteration due to weather conditions, however they may present other limitations. To mitigate the speckle effect, here was applied the dry coefficient, which is the mean of image values under the first quartile while preserving the spatial resolution. While for obtaining land cover maps containing only forest and non-forest areas an object-based machine learning classification on the Google Earth Engine platform was applied. The preliminary tests were carried out in a bitemporal manner between 2015 and 2019, followed by applying the approach monthly for the year of 2020. The outputs yielded very satisfactory and accurate results, allowing to estimate the forest dynamics for the area under consideration for each month

MOVING TOWARD OPEN GEOSPATIAL SYSTEMS: THE UN OPEN GIS INITIATIVE

Abstract. The UN Open GIS Initiative is an ongoing Partnership Initiative leaded by the United Nations Geospatial Operations. The Initiative, established in March 2016, is supported by several UN Member States, UN Field Missions, UN Agencies and technology contributing partners (international organizations, academia, NGOs, and the private sector) and takes full advantage of their expertise.The target is the creation of an extended spatial data infrastructure that meets the requirements of the UN Secretariat (including UN field missions and regional commissions), and then expands to UN agencies, UN operating partners and developing countries. The paper presents the activities done in the past year and the status of the Initiative

A combination of the Hashin-Shtrikman bounds aimed at modelling electrical conductivity and permittivity of variably saturated porous media

In this paper, we propose a novel theoretical model for the dielectric response of variably saturated porous media. The model is first constructed for fully saturated systems as a combination of the well-established Hashin and Shtrikman bounds and Archie's first law. One of the key advantages of the new constitutive model is that it explains both electrical conductivity-when surface conductivity is small and negligible-and permittivity using the same parametrization. The model for partially saturated media is derived as an extension of the fully saturated model, where the permittivity of the pore space is obtained as a combination of the permittivity of the aqueous and non-aqueous phases. Model parameters have a well-defined physical meaning, can be independently measured, and can be used to characterize the pore-scale geometrical features of the medium. Both the fully and the partially saturated models are successfully tested against measured values of relative permittivity for a wide range of porous media and saturating fluids. The model is also compared against existing models using the same parametrization, showing better agreement with the data when all the parameters are independently estimated. An example is also presented to demonstrate how the model can be used to predict the relative permittivity when only electrical conductivity is measured, or vice vers

High resolution satellite imagery orientation accuracy assessment by leave-one-out method: accuracy index selection and accuracy uncertainty

The Leave-one-out cross-validation (LOOCV) was recently applied to the evaluation of High Resolution Satellite Imagery orientation accuracy and it has proven to be an effective method alternative with respect to the most common Hold-out-validation (HOV), in which ground points are split into two sets, Ground Control Points used for the orientation model estimation and Check Points used for the model accuracy assessment. On the contrary, the LOOCV applied to HRSI implies the iterative application of the orientationmodel using all the known ground points as GCPs except one, different in each iteration, used as a CP. In every iteration the residual between imagery derived coordinates with respect to CP coordinates (prediction error of the model on CP coordinates) is calculated; the overall spatial accuracy achievable from the oriented image may be estimated by computing the usual RMSE or, better, a robust accuracy index like the mAD (median Absolute Deviation) of prediction errors on all the iterations. In this way it is possible to overcome some drawbacks of the HOV: LOOCVis a reliable and robustmethod, not dependent on a particular set of CPs and on possible outliers, and it allows us to use each known ground point both as a GCP and as a CP, capitalising all the available ground information. This is a crucial problem in current situations, when the number of GCPs to be collected must be reduced as much as possible for obvious budget problems. The fundamentalmatter to deal with was to assess howwell LOOCVindexes (mADand RMSE) are able to represent the overall accuracy, that is howmuch they are stable and close to the corresponding HOV RMSE assumed as reference. Anyway, in the first tests the indexes comparison was performed in a qualitative way, neglecting their uncertainty. In this work the analysis has been refined on the basis of Monte Carlo simulations, starting from the actual accuracy of ground points and images coordinates, estimating the desired accuracy indexes (e.g. mAD and RMSE) in several trials, computing their uncertainty (standard deviation) and accounting for them in the comparison. Tests were performed on a QuickBird Basic image implementing an ad hoc procedure within the SISAR software developed by the Geodesy and Geomatics Team at the Sapienza University of Rome. The LOOCV method with accuracy evaluated by mAD seemed promising and useful for practical case

Georeferencing old maps: a polynomial-based approach for Como historical cadastres

Recent developments in digital technologies have opened new and previously unimagined possibilities for the exploitation of cartographic heritage. In particular, georeferencing converts them from pure archival documents to real geographic data. This study investigates the issue of georeferencing the historical maps which are currently preserved at the State Archive of Como. These maps, about 15000 at the scale of 1:2000, belong to different cadastral series: the Theresian Cadastre (XVIII century), the Lombardo-Veneto Cadastre (mid-XIX century) and the New Lands Cadastre (1905). Georeferenced maps should then be inserted in the Internet GIS system, developed within the Web C.A.R.T.E. project, for an interactive 2D- and 3D consultation. Due to the peculiar nature of maps, which are divided in several adjacent cadastral sheets for each municipality, a preliminary mosaicking of these sheets was performed. Using the digital cartography of current municipalities, Ground Control Points and Check Points were collimated on the historical maps. A polynomial transformation was chosen to georeference the maps. An ad hoc-built procedure based on statistical evaluation of GCPs and CPs residuals was implemented, in order to determine the optimal polynomial order to be used. Evaluation of georeferencing results was performed both qualitatively and quantitatively. The obtained accuracy is much higher, as the territories covered by the maps are smaller and more densely-built. The methodology is automated and can be proposed as a reference for georeferencing maps of comparable characteristics. Historical maps can thus be continuously navigated into a georeferenced framework and compared with current cartography. This clears the way for the usage of historical maps in a wide range of applications, such as territorial planning, urban and landscape changes analysis and archaeological research

ACADEMIC TRACK OF FOSS4G 2019 BUCHAREST – THE ASYMPTOTIC CONNECTION BETWEEN SOFTWARE AND DATA: PREFACE

Abstract. FOSS4G stands for Free and Open Source Software for Geospatial. It is the flagship event of OSGeo. Each FOSS4G has its special aura, kindly designed by each Local Organising Committe, sharing the local culture and spirit with the greater community. In 2019, geo-spatial.org, the OSGeo Local Chapter of Romania, won the honour of organising the geospatial event of the year. FOSS4G 2019 was held in Bucharest (Romania), in three of the most important buildings of this city: National Theatre of Bucharest, InterContinental Hotel and Faculty of Geography from the University of Bucharest.Following the established tradition of FOSS4G conferences, at the 2019 edition, an Academic Track ran in parallel with the General Track. The main purpose of this track was to bring together researchers, teachers, developers, users and practitioners carrying out research activities in geospatial domains, with an emphasis on the open source solutions. All types of topics such as results achieved, case studies, work in progress, academic endeavours to conceptualize, assess or teach open source geospatial software and data, were welcomed. The Academic Committee discouraged prevalent presentations of technologies or use cases without properly justifying originality to the scientific state of the art, emphasizing on particular novelty.At this edition, 53 papers were submitted to the Academic Track. These were blind reviewed by 3 reviewers. Finally 38 scientific papers were selected for publication in this volume of the ISPRS Archives. The editors would like to thank all the authors, the members from the Scientific Committee and the Organizing Committee for their valuable contributions. We hope you enjoy reading the proceedings.</p

Insights into the Effect of Urban Morphology and Land Cover on Land Surface and Air Temperatures in the Metropolitan City of Milan (Italy) Using Satellite Imagery and In Situ Measurements

With a concentration of people, activities, and infrastructures, urban areas are particularly vulnerable to the negative effects of climate change. Among others, the intensification of the Urban Heat Island (UHI) effect is leading to an increased impact on citizen health and the urban ecosystem. In this context, this study aims to investigate the effect of urban morphology and land cover composition-which are established by exploiting the Local Climate Zone (LCZ) classification system-on two urban climate indicators, i.e., Land Surface Temperature (LST) and air temperature. The study area is the Metropolitan City of Milan (northern Italy). LCZ and LST maps are derived by leveraging satellite imagery and building height datasets. Both authoritative and crowdsourced in situ measurements are used for the analysis of air temperature. Several experiments are run to investigate the mutual relation between LCZ, LST, and air temperature by measuring LST and air temperature patterns in different LCZs and periods. Besides a strong temporal correlation between LST and air temperature, results point out vegetation and natural areas as major mitigating factors of both variables. On the other hand, higher buildings turn out to increase local air temperature while buffering LST values. A way lower influence of building density is measured, with compact building areas experiencing slightly higher air temperature yet no significant differences in terms of LST. These outcomes provide valuable tools to urban planners and stakeholders for implementing evidence-based UHI mitigation strategies

SYSTEM ARCHITECTURE FOR GEOSPATIAL VIRTUAL DATA INTEGRATION IN WEB-BASED APPLICATIONS

The wide availability of geospatial data from different sources makes it necessary to create systems that are able to use and integrate the data to generate added value. We propose a system architecture following FAIR principles (Findable, Accessible, Interoperable, Reusable) and state-of-the-art methodologies for a server-side web-based application that performs virtual data integration over data sources that implement geospatial information standards. The architecture extends the mediator-wrapper design pattern with additional components that provide the system with additional flexibility and modularity, much needed for modern web applications. The architecture is composed of the mask, which acts as the interface of the system towards external users; a mediator that handles processing and data integration logic; a set of wrappers that communicate with the external data sources; persistent storage to provide flexible configuration and metadata capabilities to the system; and messaging queue for enabling asynchronous processing. At the same time, the architecture’s components are divided into four layers, each one with a specific role: presentation, configuration, processing, and communication

AN OVERVIEW OF GEOINFORMATICS STATE-OF-THE-ART TECHNIQUES FOR LANDSLIDE MONITORING AND MAPPING

Abstract. Natural hazards such as landslides, whether they are driven by meteorologic or seismic processes, are constantly shaping Earth's surface. In large percentage of the slope failures, they are also causing huge human and economic losses. As the problem is complex in its nature, proper mitigation and prevention strategies are not straightforward to implement. One important step in the correct direction is the integration of different fields; as such, in this work, we are providing a general overview of approaches and techniques which are adopted and integrated for landslide monitoring and mapping, as both activities are important in the risk prevention strategies. Detailed landslide inventory is important for providing the correct information of the phenomena suitable for further modelling, analysing and implementing suitable mitigation measures. On the other hand, timely monitoring of active landslides could provide priceless insights which can be sufficient for reducing damages. Therefore, in this work popular methods are discussed that use remotely-sensed datasets with a particular focus on the implementation of machine learning into landslide detection, susceptibility modelling and its implementation in early-warning systems. Moreover, it is reviewed how Citizen Science is adopted by scholars for providing valuable landslide-specific information, as well as couple of well-known platforms for Volunteered Geographic Information which have the potential to contribute and be used also in the landslide studies. In addition to proving an overview of the most popular techniques, this paper aims to highlight the importance of implementing interdisciplinary approaches

BUILDING A DIGITAL TWIN OF THE ITALIAN COASTS

Abstract. The "Destination Earth" initiative of the European Union encompasses the creation of Digital Twin Earths (DTEs), high-precision digital models of the Earth integrating various aspects of the Earth's system to monitor and simulate natural phenomena and related human activities, being able to explore the past, understand the present, and build predictive models of the future. To achieve this goal, huge amounts of good-quality data are necessary, but also, means to combine and add further utility to them.To tackle this problem, we created a novel web application that implements the mediator-wrapper architecture as a data integration strategy and uses only open-source software to put together more than 60 geospatial layers from 3 different data sources. This application is a proof of concept of how data integration can be used to implement Digital Twins and is focused solely on the Italian coasts. It integrates data from Copernicus and WorldPop to provide tools for analysing and describing the interaction of marine, land, and demographic variables on coastal areas. It offers both visualization and analysis capabilities, which is a unique feature amongst similar applications, thanks to the implementation of virtual data integration and geospatial data standards