Open geospatial data and tools are an increasingly important paradigm offering the opportunity to promote the democratization of geographical information, the transparency of governments and institutions, as well as social, economic and environmental opportunities. During the past decade, developments in the area of open geospatial data and open-source geospatial software have greatly improved. Many parts of the research community believe that combining free and open software, open data, as well as open standards, leads to the creation of a sustainable ecosystem to accelerate new discoveries to help solve global cross-disciplinary societal challenges, from climate change mitigation to sustainable cities. The consistent prevalence of open source GIS studies motivated this thematic collection. The contributions are divided into two main categories. In the first category, seven concrete studies on open-source tools and technologies for urban and environmental studies are briefly presented. Each one has been implemented for and applied to a certain use case, and at the same time it may be applied to other use cases due to the reproducibility nature of open source software. The second category presents and discusses the usability of open source geospatial solutions for laser scanning technology and its applications.

In the Sahara and the Sahel, groundwater is a limited and indispensable resource for pastoral livestock farming. The daily life and work of the herders are organised around the location of the wells and the depth of the water table. To ensure the sustainable development of these regions, it is therefore essential to develop accurate piezometric maps, even in the areas that are most difficult to access.Thanks to high-resolution satellite images, the tracks made by cattle, goats and camels in the Sahara and Sahel could become a key indicator of the depth of the water table.In the northern Sahel, pastoralists water their livestock from deep wells. To draw water, they hitch oxen or camels to a rope whose length is an accurate measure of the depth of the piezometric surface of the water table. When pulling on this rope, the animals leave deep tracks on the ground that can be observed and measured on satellite images.We have developed a remote sensing technique that allows us to (a) identify pastoral wells, (b) isolate the tracks left by the animals used to draw water, and (c) use these animal tracks to estimate the water depth.After carefully calibrating the method, we were able to use open data (Landsat) and satellites images freely accessible data thanks to Google Earth Pro (SPOT and Worldview) to draw up, in just a few weeks, the piezometric map of a large aquifer (200,000km2) that is not easily accessible by other means due to the prevailing insecurity that has persisted in this part of the Sahel region for several years. This same method was then subsequently tested and validated on two other aquifers, one in Nigeria and one in Niger.

Diffs, a concept known from source code version control systems such as git, is interesting for geospatial, event-based workflows. We investigate how the native mathematical structure of vector geometries can be utilized in order to create a diffing algorithm tailored to geospatial vector data. Diffing algorithms are a well-researched area which dates to the 1970ies; however, we find that geospatial diffing operations tends to be carried out using generic algorithms combined with a pre- and post-processing step. We created GeomDiff, an algorithm and storage format tailored to geospatial vector data. The creation time, apply/undo time, and patch size of GeomDiff was compared to three other generic algorithms by running an online experiment using 2.5 million real-world geometry pairs from OpenStreetMap. We found that the GeomDiff algorithm performs better than or on-par with the alternatives on point-geometries, and complex geometries with a small (< 500) vertex count. We argue that there are both computation time and storage space improvements to be gained by using a tailored diffing algorithm for geospatial vector data. These promising first results encourages further refinement of the algorithm in order to handle complex geometries efficiently as well.

Of the many features that smart cities offer, safe and comfortable mobility of pedestrians within the built environment is of particular importance. Safe and comfortable mobility requires that the built environments of smart cities be accessible to all pedestrians, mobility abled and mobility impaired, given their various mobility needs and preferences. This, coupled with advanced technologies such as wayfinding applications, pedestrians can get assistance in finding the best pathways at different locations and times. Wayfinding applications comprise two components, a database component containing accessibility data, and appropriate algorithms that can utilize accessibility data to meet the mobility needs and preferences of all individuals. While wayfinding applications that provide accessibility on both permanent (e.g., steps) and temporary (e.g., snow) pathways are becoming available, there is a gap in current solutions. There are two elements in the gap, one is that the accessibility data used for finding accessible pathways for people with disabilities are not compliant to the widely agreed upon and available standards, another is that the accessibility data are not available in free and open platforms so that they can be used by developers to develop personalized wayfinding applications and services. To fill this gap, in this paper, we propose a new extension in CityGML with accessibility data. We demonstrate the benefits of the new extension by testing various route options within a city. These route options clearly show the differences between commonly (shortest and fastest) requested and produced pathways and accessible pathways that are feasible and preferred by people who are mobility impaired, such as wheelchair users.

This paper is the editorial of the Special Issue “Open Source Geospatial Software”, which features 10 published papers. The editorial introduces the concept of openness and, within the geospatial context, declines it into the three main components of software, data and standards. According to this classification, the papers published in the Special Issue are briefly summarized and a future research agenda in the open geospatial domain is finally outlined.

This review aims at introducing laser scanning technology and providing an overview of the contribution of open source projects for supporting the utilization and analysis of laser scanning data. Lidar technology is pushing to new frontiers in mapping and surveying topographic data. The open source community has supported this by providing libraries, standards, interfaces, modules all the way to full software. Such open solutions provide scientists and end-users valuable tools to access and work with lidar data, fostering new cutting-edge investigation and improvements of existing methods.The first part of this work provides an introduction on laser scanning principles, with references for further reading. It is followed by sections respectively reporting on open standards and formats for lidar data, tools and finally web-based solutions for accessing lidar data. It is not intended to provide a thorough review of state of the art regarding lidar technology itself, but to provide an overview of the open source toolkits available to the community to access, visualize, edit and process point clouds. A range of open source features for lidar data access and analysis is provided, providing an overview of what can be done with alternatives to commercial end-to-end solutions. Data standards and formats are also discussed, showing what are the challenges for storing and accessing massive point clouds.The desiderata are to provide scientists that have not yet worked with lidar data an overview of how this technology works and what open source tools can be a valid solution for their needs in analysing such data. Researchers that are already involved with lidar data will hopefully get ideas on integrating and improving their workflow through open source solutions.

PyWPS 4 is a re–make of the Python implementation of the WPS standard. It is the result of the work of over a dozen individual contributors, during a period of almost three years. One of the goals driving this re–implementation was to embrace modern Python technologies and the possibilities they open.This technical note reviews some of the more advanced possibilities this new PyWPS implementation opens. Request activity is now logged into a structured database, relying on a generic Object–Relational Mapping engine. The adoption of WSGI (Web Server Gateway Interface) opens new ways for load balancing request execution and application encapsulation, that are exemplified with modern Python technologies. Furthermore, PyWPS 4 is designed with containerisation in mind, expediting both development and deployment and improving security.

Crowdsourced, open geospatial data such as the data compiled through OpenStreetMap have proven useful in addressing humanitarian, disaster and development needs. However, the existing ways in which volunteers engage in OpenStreetMap have inherent limitations that lead to critical data gaps in economically underdeveloped countries and regions. Various initiatives that target specific geospatial data gaps and engage volunteers for longer periods have emerged to overcome these limitations, yet there has been limited in-depth study of such targeted mapping initiatives. This article reports the findings from Digital Internship and Leadership (DIAL), a programme designed to fill data gaps in rural Nepal by engaging young people in mapping rural Nepal by integrating targeted mapping, a virtual internship strategy and youth leadership development. The findings suggest the potential benefits of targeted mapping initiatives embedded in youth leadership internship programmes to address those critical data gaps.

OSM2LULC is a software package developed to automatically convert OpenStreetMap (OSM) data into Land Use Land Cover (LULC) maps using Free and Open Source Software for Geospatial (FOSS4G) tools. It needs to be highly efficient given the increasing detail of OSM data and the need to apply it to large extent regions. In this article, a comparison between the implementation of OSM2LULC in different available GIS platforms is made using both vector and raster data structures, which resulted in different versions. A description of the differences of each version is made and, to assess their performance, they were applied to four different study areas with different characteristics, in terms of available OSM data and area size. The performance of each version was evaluated taking into account: the overall processing time required to obtain LULC maps; and differences in the results obtained when different data structures (vector and raster) were used. Results showed that the adoption of a strategy that favors interoperability between FOSS4G and the combined use of both vector and raster data promotes a performance increase. After analysing the topological relationships of OSM data, the conversion to raster data format and the execution of procedural parts with such data indicated significant performance gains, without any positional distortions that significantly compromise the applicability of the final result in further case scenarios.

The study of past sea levels relies largely on the interpretation and quantification of sea-level indicators. These are fossil coastal landforms, bioconstructions or deposits that have a quantifiable relation to paleo sea level (called the indicative meaning) and can be assigned an age of formation. The calculation of the indicative meaning should always rely on the quantitative comparison between the paleo sea-level indicator and the elevational range of the same feature in the modern environment with respect to modern sea level. This may prove difficult to quantify when no site-specific data is available, for example when compiling databases with large geographic scope. In this paper, we provide a method to quantify the indicative meaning of sea-level indicators using simple hydro- and morphodynamic equations with inputs from global wave and tide datasets. We apply and compare our approach exemplary to data published in a relative sea-level database on the Marine Isotope Stage 5e highstand and show how this approach can help in estimating the indicative meaning if no site-specific data on the modern analog is available. For the easier usability of the described approach in this paper, we also present a stand-alone Java-app (IMCalc), which can be used as a calculator of the indicative meaning.