Abstract
Abstract. This study explores the possibility of using Artificial Intelligence (AI) as a means to support water monitoring. More precisely, it addresses the issue of the quality and reliability of Citizen Science data. The paper addresses the tools and data of the SIMILE (Informative System for the Integrated Monitoring of Insubric Lakes and their Ecosystems) project in order to develop an open pre-filtering system for Volunteer Geographic Information (VGI) of lake water monitoring at the global scale. The goal is to automatically determine the presence of harmful phenomena (algae and foams) in the images uploaded by citizen scientists to reduce the time required for a manual check of the contributions. The task is challenging because of the heterogeneity of the data that consist in geotagged pictures taken without specific instructions. For this purpose, different tools and deep learning techniques have been tested (Clarifai platform, a Convolutional Neural Network (CNN), and an object detection algorithm called faster Region-based CNN (R-CNN). The original dataset composed by the observations of SIMILE – Lake Monitoring application, has been integrated with the results of both keyword and image searches on web engines (Google, Bing, etc) and crawling Flickr data. The performances of the different algorithms are presented for their capability of detecting the presence and correctly labelling the phenomenon together with some possible strategies to improving them in the future.
Highlights
1.1 SIMILE project and its contextWaterbodies play a key role in the mitigation of the impact of climate change
Convolutional neural network (CNN): The results obtained by using CNN show that the model has learned relatively well on the training dataset
Advances in Machine Learning (ML) and in computer vision have resulted in many studies that integrate such techniques in their research
Summary
Waterbodies play a key role in the mitigation of the impact of climate change. They represent an essential resource available to billions of people for multiple uses. SIMILE (Informative System for the Integrated Monitoring of Insubric Lakes and their Ecosystems) is a project that involves academia (Politecnico di Milano – Lecco Campus; Fondazione Politecnico; SUPSI - University of Applied Sciences and Arts of Southern Switzerland), research bodies (Water Research Institute National Research Council) and institutions (Lombardy Region; Ticino Canton in Switzerland), which are cooperating partners in the preservation of water quality in the Lugano-, Maggiore- and Como lakes. SIMILE – Lake Monitoring (Biraghi et al, 2020; Pessina et al, 2020) is a recently released cross-platform, open-source, mobile application, which has been developed to support the activities of the project related to Citizen Science (CS). The mobile application enables private citizens to share their observations of the lake environment through geo-referenced images of algae, This contribution has been peer-reviewed
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