Statistics for Spatial Data.

Sustainable Development Goals

Over the past years, several filters have been developed to extract bare-Earth points from point clouds. ISPRS Working Group III/3 conducted a test to determine the performance of these filters and the influence of point density thereon, and to identify directions for future research. Twelve selected datasets have been processed by eight participants. In this paper, the test results are presented. The paper describes the characteristics of the provided datasets and the used filter approaches. The filter performance is analysed both qualitatively and quantitatively. All filters perform well in smooth rural landscapes, but all produce errors in complex urban areas and rough terrain with vegetation. In general, filters that estimate local surfaces are found to perform best. The influence of point density could not well be determined in this experiment. Future research should be directed towards the usage of additional data sources, segment-based classification, and self-diagnosis of filter algorithms.

Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds

The GIS software sector has developed rapidly over the last ten years. Open Source GIS applications are gaining relevant market shares in academia, business, and public administration. In this paper, we illustrate the history and features of a key Open Source GIS, the Geographical Resources Analysis Support System (GRASS). GRASS has been under development for more than 28 years, has strong ties into academia, and its review mechanisms led to the integration of well tested and documented algorithms into a joint GIS suite which has been used regularly for environmental modelling. The development is community-based with developers distributed globally. Through the use of an online source code repository, mailing lists and a Wiki, users and developers communicate in order to review existing code and develop new methods. In this paper, we provide a functionality overview of the more than 400 modules available in the latest stable GRASS software release. This new release runs natively on common operating systems (MS-Windows, GNU/Linux, Mac OSX), giving basic and advanced functionality to casual and expert users. In the second part, we review selected publications with a focus on environmental modelling to illustrate the wealth of use cases for this open and free GIS.

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GRASS GIS: A multi-purpose open source GIS

Big data in tourism research: A literature review

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Spherical harmonic analysis of satellite gradiometry

As result of the “Global Land Cover Mapping at Finer Resolution” project led by National Geomatics Center of China (NGCC), one of the first global land cover datasets at 30-meters resolution (GlobeLand30) has been produced for the years 2000 and 2010. The first comprehensive accuracy assessment at a national level of these data (excluding some comparisons in China) has been performed on the Italian area by means of a benchmarking with the more detailed land cover datasets available for some Italian regions. The accuracy evaluation was based on the cell-by-cell comparison between Italian maps and the GlobeLand30 in order to obtain the confusion matrix and its derived statistics (overall accuracy, allocation and quantity disagreements, user and producer accuracy), which help to understand the classification quality. This paper illustrates the adopted methodology and procedures for assessing GlobeLand30 and reports the obtained statistics. The analysis has been performed in eight regions across Italy and shows very good results: the comparison of the datasets according to the first level of Corine Land Cover nomenclature highlights overall accuracy values generally higher than 80%.

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The First Comprehensive Accuracy Assessment of GlobeLand30 at a National Level: Methodology and Results

LIDAR (Light Detection and Ranging) is one of the most recent technologies in surveying and mapping. LIDAR is based on the combination of three different data collection tools: a laser scanner mounted on an aircraft, a Global Positioning System (GPS) used in phase differential kinematic modality to provide the sensor position and an Inertial Navigation System (INS) to provide the orientation. The laser sends towards the ground an infrared signal, which is reflected back to the sensor. The time employed by the signal, given the aircraft position and attitude, allows computation of the earth point elevation. In standard conditions, taking into account the flight (speed 200–250 km/hour, altitude 500–2,000 m) and sensor characteristics (scan angle ± 10–20 degrees, emission rate 2,000–50,000 pulses per second), earth elevations are collected within a density of one point every 0.5–3 m. The technology allows us therefore to obtain very accurate (5–20 cm) and high resolution Digital Surface Models (DSM). For many applications, the Digital Terrain Model (DTM) is needed: we have to automatically detect and discard from the previous DSM all the features (buildings, trees, etc.) present on the terrain. This paper describes a procedure that has been implemented within GRASS to construct DTMs from LIDAR source data.

LIDAR Data Filtering and DTM Interpolation Within GRASS

LIDAR (Light Detection and Ranging) is one of the most recent technologies insurveying and mapping. The LIDAR is based on the combination of three different datacollection tools: a laser scanner mounted on an aircraft, a Global Positioning System(GPS) used in phase differential kinematic modality to provide the sensor position and anInertial Navigation System (INS) to provide the orientation. The laser sends towards theground an infrared signal, which is reflected back to the sensor. The time employed bythe signal, given the aircraft position and attitude, allows us to compute the earth pointelevation. In standard conditions, taking into account the flight (speed 200- 250 km/hour, altitudes500-2000m) and sensor characteristics (scan angle 10-20 degrees, emission rate 2000 –50000 pulses per second), the earth elevations are collected within a density of one pointevery 0.5-3 meters.The technology allows us therefore to obtain very accurate (5-20 cm) and high resolutionDigital Surface Models (DSM). For many applications the Digital Terrain Model (DTM)is needed: we have to automatically detect and discard from the previous DSM all thefeatures (buildings, trees, …) present on the terrain. The elaboration procedure has beenimplemented within GRASS.

http://www.ing.unitn.it/~grass/conferences/GRASS2002/proceedings/proceedings/pdfs/Brovelli_Maria_Antonia.pdf

Managing and processing LIDAR data within GRASS

OpenStreetMap (OSM) is an extraordinarily large and diverse spatial database of the world. Road networks are amongst the most frequently occurring spatial content within the OSM database. These road network representations are usable in many applications. However the quality of these representations can vary between locations. Comparing OSM road networks with authoritative road datasets for a given area or region is an important task in assessing OSM's fitness for use for applications such as routing and navigation. Comparisons such as these can be technically challenging and no software implementation exists which facilitates such comparisons easily and automatically. In this paper we develop and propose a flexible methodology for comparing the geometry of OSM road network data with other road datasets. Quantitative measures for the completeness and spatial accuracy of OSM are computed including the compatibility of OSM road data with other map databases. Our methodology provides users with significant flexibility in how they can adjust the parameterisation to suit their needs. This software implementation is exclusively built on open source software and a significant degree of automation is provided for these comparisons. This software can subsequently be extended and adapted for comparison between OSM and other external road datasets. This is the pre-peer reviewed version of the following article: “Brovelli M. A., Minghini M., Molinari M. and Mooney P. (2016) Towards an automated comparison of OpenStreetMap with authoritative road datasets. Transactions in GIS, DOI: 10.1111/tgis.12182”\r, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/tgis.12182/full.

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Maria Antonia Brovelli

Papers

Spherical harmonic analysis of satellite gradiometry

The First Comprehensive Accuracy Assessment of GlobeLand30 at a National Level: Methodology and Results

LIDAR Data Filtering and DTM Interpolation Within GRASS

Managing and processing LIDAR data within GRASS

Towards an Automated Comparison of OpenStreetMap with Authoritative Road Datasets