Abstract. Internet decentralization nowadays represents a critical topic to be addressed. It protects the users’ privacy, promotes data ownership, eliminates single points of failure and data censorship. An element that has an important role in decentralization is blockchain technology. Although blockchain has revolutionised sectors like the financial one with Bitcoin, there are still some fields where it needs to be further developed. One of these is geospatial data sharing and citizen science, where features like decentralization, immutability and transparency are needed. This study focuses on the description of a decentralized application developed specifically for geospatial data-point sharing and validation. As an example, the Informative System for the Integrated Monitoring of Insubric Lakes and their Ecosystems (SIMILE) is used. This application is developed in the Velas blockchain infrastructure and implements a combination of a Discrete Global Grid System (DGGS) with smart contracts. Two types of smart contracts were created, a cell and a registry smart contract. The cell smart contracts are individual for each DGGS partition and contain the list of observations present in a specific area. The registry smart contracts keep track of all the DGGS cells added to the system. Currently, SIMILE observations are validated by public authorities, which requires time that is not always available. Therefore, a fully working prototype was developed to solve this. Here users can add and manage personal observations and validate the ones belonging to other users. This work demonstrates the feasibility of creating decentralized applications for geographical data validation as a citizen science solution.

Abstract. As geospatial data continuously grows in complexity and size, the application of Machine Learning and Data Mining techniques to geospatial analysis is increasingly essential to solve real-world problems. Although in the last two decades, the research in this field produced innovative methodologies, they are usually applied to specific situations and not automatized for general use. Therefore, both generalization and integration of these methods with Geographic Information Systems (GIS) are necessary to support researchers and organizations in data exploration, pattern recognition, and prediction in the various applications of geospatial data. In this work, we present Cluster Analysis, a Python plugin that we developed for the open-source software QGIS and offers functionalities for the entire clustering process. Or tool provides different improvements from the current solutions available in QGIS, but also in other widespread GIS software. The expanded features provided by the plugin allow the users to deal with some of the most challenging problems of geospatial data, such as high dimensional space, poor quality of data, and large size of data. To highlight both the potential of the plugin and its limitations in real-world scenarios, the development is integrated with a considerable experimental phase with data of different natures and granularities. Overall, the experimental phase shows good and adequate flexibility of the plugin, and outlines the possibilities for future developments that can be provided also by the QGIS community, given the open-source nature of the project.