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

Hotspot analysis: a first prototype Python plugin enabling exploratory spatial data analysis into QGIS

ABSTRACT The growing popularity of Free and Open Source (FOSS) GIS software is -without doubts-due to the possibility to build and customize geospatial applications to meet specific requirements for any users. From this point of view, QGIS is one of the most flexible as well as fashionable GIS software environment which enables users to develop powerful geospatial applications using Python. Exploiting this feature, we present here a first prototype plugin for QGIS dedicated to Hotspot analysis, one of the techniques included in the Exploratory Spatial Data Analysis (ESDA). These statistics aim to perform analysis of geospatial data when spatial autocorrelation is not neglectable and they are available inside different Python libraries, but still not integrated within the QGIS core functionalities. The main plugin features, including installation requirements and computational procedures, are described together with an example of the possible applications of the Hotspot analysis

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

Looking through the changes: an analysis of the buried watercourses of Como

Studying territorial evolutions and investigating their underlying processes is essential to ensure continuity in well-done land management decisions. The case of Como City can be considered as a perfect small-scale example of how human influence acted on natural environment. Several watercourses hidden under the road network of the city represent one of the meaningful consequences. FOSS4G and geospatial data from different epochs of Como City historical development allowed to trace the evolution of the territorial setting and the original position of the watercourses. We quantified the variations in their peak flood discharges as a consequence of watersheds urbanization. A Web viewer was created for an easy access to the outcomes of the study

Implementation of a GEOAI model to assess the impact of agricultural land on the spatial distribution of PM2.5 concentration

: Air pollution is considered one of the major environmental risk to health worldwide. Researchers are making significant efforts to study it, thanks to state-of-art technologies in data collection and processing, and to mitigate its effect. In this context, while a lot is known about the role of urbanization, industries, and transport, the impact of agricultural activities on the spatial distribution of pollution is less studied, despite knowledge about emissions suggest it is not a secondary factor. Therefore, the aim of this study was to assess this impact, and to compare it with that of traditional polluting sources, harvesting the capabilities of GEOAI (Geomatics and Earth Observation Artificial Intelligence). The analysis targeted the highly polluted territory of Lombardy, Italy, considering fine particulate matter (PM2.5). PM2.5 data were obtained from the Copernicus-Atmosphere-Monitoring-Service and processed to infer time-invariant spatial parameters (frequency, intensity and exposure) of concentration across the whole period. An ensemble architecture was implemented, with three blocks: correlation-based features selection, Multiscale-Geographically-Weighted-Regression for spatial enhancement, and a final random forest classifier. Finally, the SHapley Additive exPlanation algorithm was applied to compute the relevance of the different land-use classes on the model. The impact of land-use classes was found significantly higher compared to other published models, showing that the insignificant correlations found in literature are probably due to an unfit experimental setup. The impact of agricultural activities on the spatial distribution of PM2.5 concentration was comparable to the other considered sources, even when focusing only on the most densely inhabited urban areas. In particular, the agriculture's contribution resulted in pollution spikes rather than in a baseline increase. These results allow to state that public policymakers should consider also agricultural activities for evidence-based decision-making about pollution mitigation

State-of-art in modelling particulate matter (PM) concentration: a scoping review of aims and methods

Air pollution is the one of the most significant environmental risks to health worldwide. An accurate assessment of population exposure would require a continuous distribution of measuring ground-stations, which is not feasible. Therefore, significant efforts are spent in implementing air-quality models. However, a complex scenario emerges, with the spread of many different solutions, and a consequent struggle in comparison, evaluation and replication, hindering the definition of the state-of-art. Accordingly, aim of this scoping review was to analyze the latest scientific research on air-quality modelling, focusing on particulate matter, identifying the most widespread solutions and trying to compare them. The review was mainly focused, but not limited to, machine learning applications. An initial set of 940 results published in 2022 were returned by search engines, 142 of which resulted significant and were analyzed. Three main modelling scopes were identified: correlation analysis, interpolation and forecast. Most of the studies were relevant to east and southeast Asia. The majority of models were multivariate, including (besides ground stations) meteorological information, satellite data, land use and/or topography, and more. 232 different algorithms were tested across studies (either as single-blocks or within ensemble architectures), of which only 60 were tested more than once. A performance comparison showed stronger evidence towards the use of Random Forest modelling, in particular when included in ensemble architectures. However, it must be noticed that results varied significantly according to the experimental set-up, indicating that no overall best solution can be identified, and a case-specific assessment is necessary

Humanitarian Mapping within a Student Association: PoliMappers

The lack of availability and accessibility of open geospatial data, especially in developing countries is addressed by various volunteer mapping associations. PoliMappers, a chapter of the YouthMappers network and a student association of Politecnico di Milano, was established with this purpose in December 2016. PoliMappers aims to contribute data to the OpenStreetMap (OSM) database by promoting the use of Free and Open Source Software (FOSS). Hence, it focuses on creating awareness on the lack of open geospatial data and on how individuals can have an impact on contributing to open geospatial databases using FOSS. The activities of PoliMappers focus on teaching and promoting the use of such geospatial technologies to run OSM-based mapathons and mapping parties

City Focus: A web-based interactive 2D and 3D GIS application to find the best place in a city, using open data and open source software

City Focus is a webbased interactive 2D and 3D GIS application to find the best place in a city to live as well as to pass shorter staying. The user can select among different criteria and decide their importance by assigning weights to each of them. The application provides thematic maps displaying insights on the places which better fit the user’s preferences. The resulting map is computed through map algebra by means of Web Coverage Processing Service WCPS provided by RASDAMAN Database Management System. Data visualization is mainly based on NASA Web WorldWind opensource virtual globe. The app exploits exclusively open data as well as Free and Open Source Software (FOSS) for its implementation by enabling continuous improvements while minimizing development costs

PoliMappers: activities and objectives

PoliMappers is a student association of Politecnico di Milano, recently founded in December 2016. The mission of the group is to do mapping across different realities within the university as well as among primary and secondary schools, furthermore to train and motivate the next generation of volunteer mappers. PoliMappers is the first European chapter of the International Association YouthMappers, founded in 2014 in the United States of America with the support of United States Agency for International Development (USAID) to cultivate a new generation of leaders in the field of open geospatial data and technologies, with the purpose of creating through them resilient communities of the future. YouthMappers is a global network that currently includes some 52 chapters in 18 countries. In accordance with the terms of participation of YouthMappers, the association's activities include at least two mapping activities per year: mapping the local area and participating in a YouthMappers network promoted campaign