Map of Land Cover Agreement: Ensambling Existing Datasets for Large-Scale Training Data Provision

Land cover information plays a critical role in supporting sustainable development and informed decision-making. Recent advancements in satellite data accessibility, computing power, and satellite technologies have boosted large-extent high-resolution land cover mapping. However, retrieving a sufficient amount of reliable training data for the production of such land cover maps is typically a demanding task, especially using modern deep learning classification techniques that require larger training sample sizes compared to traditional machine learning methods. In view of the above, this study developed a new benchmark dataset called the Map of Land Cover Agreement (MOLCA). MOLCA was created by integrating multiple existing high-resolution land cover datasets through a consensus-based approach. Covering Sub-Saharan Africa, the Amazon, and Siberia, this dataset encompasses approximately 117 billion 10m pixels across three macro-regions. The MOLCA legend aligns with most of the global high-resolution datasets and consists of nine distinct land cover classes. Noteworthy advantages of MOLCA include a higher number of pixels as well as coverage for typically underrepresented regions in terms of training data availability. With an estimated overall accuracy of 96%, MOLCA holds great potential as a valuable resource for the production of future high-resolution land cover maps

MEASURING THE INFLUENCE OF A MOTORWAY CONSTRUCTION ON LAND SURFACE TEMPERATURE USING LANDSAT THERMAL DATA: A CASE STUDY IN THE METROPOLITAN CITY OF MILAN

Cities have been identified as a landmark for climate change, being among the direct targets of its negative feedbacks. The combined effect of climate change and rapidly growing urbanization is exacerbating the urban heat island phenomenon in cities worldwide. The availability of multiple geo-data sources including satellite remote sensing products is significantly empowering the investigation of its driving factors. This is a crucial step to implement ad hoc mitigation and adaptation strategies. In view of the above, the goal of this study is to measure the effect of a motorway on the Land Surface Temperature (LST) space-time patterns by leveraging Landsat 5 and 8 thermal data of the period from 2006 to 2022. The study area is around the motorway A58 and connected roads in the Metropolitan City of Milan (northern Italy). LST patterns are investigated along the motorway track and in the neighbouring areas before and after the motorway construction, in both the cold and warm seasons. Results show that the motorway significantly affects the LST distribution during summer with a median increase of 2.5 °C along the road track with respect to the surrounding area. The warming effect is also recorded in the road buffers with decreasing LST with increasing distance from the road. On the contrary, no meaningful variation in terms of LST is measured in winter. These experiments provide insightful measures of the effect of a highway on the local climate conditions in an urban area, thus representing crucial pieces of information for driving evidence-based urban planning activities

Sensing slow mobility and interesting locations for lombardy region (Italy): A case study using pointwise geolocated open data

During the past years Web 2.0 technologies have caused the emergence of platforms where users can share data related to their activities which in some cases are then publicly released with open licenses. Popular categories for this include community platforms where users can upload GPS tracks collected during slow travel activities (e.g. hiking, biking and horse riding) and platforms where users share their geolocated photos. However, due to the high heterogeneity of the information available on the Web, the sole use of these user-generated contents makes it an ambitious challenge to understand slow mobility flows as well as to detect the most visited locations in a region. Exploiting the available data on community sharing websites allows to collect near real-time open data streams and enables rigorous spatial-temporal analysis. This work presents an approach for collecting, unifying and analysing pointwise geolocated open data available from different sources with the aim of identifying the main locations and destinations of slow mobility activities. For this purpose, we collected pointwise open data from the Wikiloc platform, Twitter, Flickr and Foursquare. The analysis was confined to the data uploaded in Lombardy Region (Northern Italy) - corresponding to millions of pointwise data. Collected data was processed through the use of Free and Open Source Software (FOSS) in order to organize them into a suitable database. This allowed to run statistical analyses on data distribution in both time and space by enabling the detection of users' slow mobility preferences as well as places of interest at a regional scale

An automated GRASS-based procedure to assess the geometrical accuracy of the OpenStreetMap Paris road network

OpenStreetMap (OSM) is the largest spatial database of the world. One of the most frequently occurring geospatial elements within this database is the road network, whose quality is crucial for applications such as routing and navigation. Several methods have been proposed for the assessment of OSM road network quality, however they are often tightly coupled to the characteristics of the authoritative dataset involved in the comparison. This makes it hard to replicate and extend these methods. This study relies on an automated procedure which was recently developed for comparing OSM with any road network dataset. It is based on three Python modules for the open source GRASS GIS software and provides measures of OSM road network spatial accuracy and completeness. Provided that the user is familiar with the authoritative dataset used, he can adjust the values of the parameters involved thanks to the flexibility of the procedure. The method is applied to assess the quality of the Paris OSM road network dataset through a comparison against the French official dataset provided by the French National Institute of Geographic and Forest Information (IGN). The results show that the Paris OSM road network has both a high completeness and spatial accuracy. It has a greater length than the IGN road network, and is found to be suitable for applications requiring spatial accuracies up to 5-6 m. Also, the results confirm the flexibility of the procedure for supporting users in carrying out their own comparisons between OSM and reference road datasets

A FOSS4G-based procedure to compare OpenStreetMap and authoritative road network datasets

OpenStreetMap (OSM), the most popular VGI crowdsourcing project, is an excellent example of an open-license spatial database. But what is the quality of OSM road network datasets compared to authoritative counterparts? Several comparisons of this kind are detailed in literature but these cannot be easily adapted to other scenarios. Developing a generic automated procedure is very challenging. This paper proposes a FOSS4G-based procedure for automated quality comparison of OSM and any authoritative road network datasets. We detail work-in-progress which has great potential. Our procedure is currently implemented into a GRASS command with future plans to extend this to a QGIS plugin and a FOSS4G-based WPS

A FOSS Based Web Geo- Service Architecture For Data Management In Complex Water Resources Contexts

Advances in environmental monitoring systems from remote sensing to pervasive real and virtual sensor networks are enlarging the amount and types of data available at local and global scale at increasingly higher temporal and spatial resolution. However, accessing and integrating these data for modeling and operational purposes can be challenging and highly time consuming, particularly in complex physical and institutional contexts, where data are from different sources. This research focuses on the design of a web geo- service architecture, based on Free and Open Source Software (FOSS), to enable collection and sharing of data coming from complex water resources domains and managed by multiple institutions. The heterogeneous nature of these data requires the combination of different geospatial data servers (Catalog Service for the Web, Web Map Service, Web Feature service, Web Coverage Service, Sensor Observations Service,) and interface technologies that enable interoperability of all complex resources data types. This is a key feature of web geo- service tools in multidata and multiowners environment. Besides the storage of the available hydrological data according to the Open Geospatial Consortium standards, the architecture provides a platform for comparatively analyzing alternative water supply and demand management strategies. The architecture is developed for the Lake Como system (Italy), a regulated lake serving multiple and often competing water uses (irrigation, hydropower, flood control) in northern Italy. . This research gives important insights on currently operating GEOSS (Global Earth Observation System of Systems) architectures, demonstrating that Spatial Data Infrastructures using FOSS are a feasible and effective alternative to data and metadata collection, storage, sharing and visualization in complex water resources management contexts, using open international standards

Geospatial openness: from software to standards & data

Abstract 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