Advances in artificial intelligence (AI) and the extension of citizen science to various scientific areas, as well as the generation of big citizen science data, are resulting in AI and citizen science being good partners, and their combination benefits both fields. The integration of AI and citizen science has mostly been used in biodiversity projects, with the primary focus on using citizen science data to train machine learning (ML) algorithms for automatic species identification. In this article, we will look at how ML techniques can be used in citizen science and how they can influence volunteer engagement, data collection, and data validation. We reviewed several use cases from various domains and categorized them according to the ML technique used and the impact of ML on citizen science in each project. Furthermore, the benefits and risks of integrating ML in citizen science are explored, and some recommendations are provided on how to enhance the benefits while mitigating the risks of this integration. Finally, because this integration is still in its early phases, we have proposed some potential ideas and challenges that can be implemented in the future to leverage the power of the combination of citizen science and AI, with the key emphasis being on citizen science in this article.

Citizen science, the participation of the public in scientific projects, is growing significantly, especially with technological developments in recent years. Volunteers are the heart of citizen science projects; therefore, understanding their motivation and how to sustain their participation is the key to success in any citizen science project. Studies on participants of citizen science projects illustrate that there is an association between participant motivation and the type of contribution to projects. Thus, in this paper, we define a motivational framework, which classifies participant motivation taking into account the typologies of citizen science projects. Within this framework, we also take into account the importance of motivation in initiating and sustaining participation. This framework helps citizen science practitioners to have comprehensive knowledge about potential motivational factors that can be used to recruit participants, as well as sustaining participation in their projects.

Climate issues are nowadays one of the most pressing societal challenges, with cities being identified among the landmarks for climate change. This study investigates the effect of urban land cover composition on a relevant climate-related variable, i.e., the air temperature. The analysis exploits different big geo-data sources, namely high-resolution satellite imagery and in-situ air temperature observations, using the city of Milan (Northern Italy) as a case study. Satellite imagery from the Landsat 8, Sentinel-2, and RapidEye missions are used to derive Local Climate Zone (LCZ) maps depicting land cover compositions across the study area. Correlation tests are run to investigate and measure the influence of land cover composition on air temperature. Results show an underlying connection between the two variables by detecting an average temperature offset of about 1.5 ${}^{\circ }$ C between heavily urbanized and vegetated urban areas. The approach looks promising in investigating urban climate at a local scale and explaining effects through maps and exploratory graphs, which are valuable tools for urban planners to implement climate change mitigation strategies. The availability of worldwide coverage datasets, as well as the exclusive use of Free and Open Source Software (FOSS), provide the analysis with a potential to be empowered, replicated, and improved.

This paper aims at underling difficulties regarding the establishment of citizen engagement processes. The specificity of citizen engagement processes lies in their evolution over time where objectives, constraints, and latitudes of a given project influence the relevance of the tools offered to citizens. Three categories of urban projects (trans-urban, major metropolitan, architectural design) have been described. These classes range from a local space with short deadlines to a regional space spread over several decades. Furthermore, the use of 3D platforms for a broad public is influenced by the users’ preferences, perception, and expertise. Throughout this study, major challenges that have been experienced during the design a 3D participatory platform are identified. They range from the issues of implementing adequate tools according to the project (temporal and spatial scalability), the participation forms (passive, consultative or interactive), to the difficulties of convincing the authorities to use new bottom-up methods. Finally, a conceptual framework for the creation of a 3D participatory platform has been introduced. It can be summarized by three major steps: (1) Meeting the needs of a decision maker, (2) Designing the participation tool in accordance with the context, (3) Translating collected raw data in order to respond to the initial request.

Geomatics has long been recognized as an information-technology-oriented discipline whose objective is to integrate and deliver multiple sources of geolocated data to a wide range of environmental and urban sciences

Data quality is the primary concern for researchers working on citizen science projects. The collected data by citizen science participants are heterogeneous and therefore must be validated. There are several validation approaches depending on the theme and objective of the citizen science project, but the most common approach is the expert review. While expert validation is essential in citizen science projects, considering it as the only validation approach can be very difficult and complicated for the experts. In addition, volunteers can get demotivated to contribute if they do not receive any feedback regarding their submissions. This project aims at introducing an automatic filtering mechanism for a biodiversity citizen science project. The goals of this project are to first use an available historical database of the local species to filter out the unusual ones, and second to use machine learning and image recognition techniques to verify if the observation image corresponds with the right species type. The submissions that does not successfully pass the automatic filtering will be flagged as unusual and goes through expert review. The objective is on the one hand to simplify validation task by the experts, and on the other hand to increase participants’ motivation by giving them real-time feedback on their submissions. Finally, the flagged observations will be classified as valid, valid but uncommon, and invalid, and the observation outliers (rare species) can be identified for each specific region.