Author/s: Maryam Lotfian and Jens IngensandPresenter: Maryam Lotfian
Nowadays there are many citizen science applications to collect biodiversity observations, and this has resulted in freely accessible large species datasets. However, it is essential to understand how to validate such large datasets taking into account species characteristics.We have developed a citizen science application to collect biodiversity observations, which includes a real-time automatic filtering functionality to flag rare or erroneous observations with the aid of machine learning techniques.The objective of this automatic filtering and real-time feedback is on the one hand to improve data quality, and on the other, to sustain participation by giving informative feedback to the volunteers.
Author/s: Marijn van der Velde, Raphaël d'Andrimont, Momchil Iordanov, Laura Sanchez Martinez and Guido LemoinePresenters: Marijn van der Velde, Laura Martinez Sanchez
For this purpose, experts collected SLI using side-looking action cameras for a fixed route survey in Flevoland (NL) during eight dates in the 2018 growing season. This resulted in a SLI dataset of nearly 200,000 full HD resolution images. At more than 200 parcels detailed phenological observations were made for a range of crops. This information provides training data to extract crop type and phenology from the SLI images with machine learning algorithms.
Author/s: Eugenio Gervasini, Celia López Cañizares and Ana Cristina CardosoPresenter: Eugenio Gervasini
Invasive Alien Species (IAS) affect all environments including cities. Citizens can greatly contribute to their detection, monitoring and management, supporting the implementation of the European policy on biodiversity. The European Alien Species Information Network (EASIN), disseminates updated scientific information on alien species in Europe, and is committed to Citizen Science, via a dedicated App, a communication strategy, including Social Media, to create a European-wide community of interest on IAS. Interactions with the public allow a better and personalized feedback, help improving communication, update the App features, and promotes people’s long term engagement in early detection, monitoring, and management of IAS.