Use of a differential pressure transducer for the monitoring of soil volume change in cyclic triaxial test on unsaturated soils

A new experimental set-up using a differential pressure transducer was developed, that enables the monitoring of volume changes in cyclic triaxial tests on unsaturated soils. Calibration tests were performed in order to analyze the performance of the set-up, especially in terms of loading frequencies. Based on calibration results, a low frequency of 0.05 Hz was adopted for the tests carried out on the unsaturated loess from northern France. Five water contents were considered in the tests. The obtained results have confirmed the efficiency of the new system for volume change monitoring under cyclic loading. The effect of water content on the cyclic behavior of loess was clearly evidenced. Finally, some suggestions were made to improve the accuracy of the system

The DeepFaune initiative: a collaborative effort towards the automatic identification of European fauna in camera trap images

Camera traps have revolutionized how ecologists monitor wildlife, but their full potential is realized only when the hundreds of thousands of collected images can be readily classified with minimal human intervention. Deep-learning classification models have allowed extraordinary progress towards this end, but trained models remain rare and are only now emerging for European fauna. We report on the first milestone of the DeepFaune initiative (https://www.deepfaune.cnrs.fr), a large-scale collaboration between more than 50 partners involved in wildlife research, conservation and management in France. We developed a classification model trained to recognize 26 species or higher-level taxa that are common in Europe, with an emphasis on mammals. The classification model achieved 0.97 validation accuracy and often >0.95 precision and recall for many classes. These performances were generally higher than 0.90 when tested on independent out-of-sample datasets for which we used image redundancy contained in sequences of images. We implemented our model in a software to classify images stored locally on a personal computer, so as to provide a free, user-friendly and high-performance tool for wildlife practitioners to automatically classify camera trap images. The DeepFaune initiative is an ongoing project, with new partners joining regularly, which allows us to continuously add new species to the classification model