AMMOD: Automated Multisensor station for Monitoring Of species Diversity

AMMOD Logo AMMOD Station

Team: Paul Bodesheim, Dimitri Korsch, Daphne Auer, Julia Böhlke

Joint Research Project

Climate change is affecting ecosystems all over the world, causing irreversible loss of biodiversity. Since humans are the main contributors to the situation, it is our responsibility to closely monitor the effects of climate change in order to take action for the living beings in our environment in a targeted way.

The goal of all AMMOD project partners is therefore to monitor biological diversity in Germany using new technologies. To this end, prototypes for so-called AMMOD stations are to be set up in German forests within the 3-years project period. These are equipped with sensors for recording animal sounds and plant emissions, with animal cameras for birds, mammals and insects, and with automated insect and pollen sample collectors for monitoring by DNA barcoding. These recordings will be used to generate a solid data pool that will enable the analysis of change and possible trends. For instance, we want to use the detected species to determine their population and record the change over a long period of time.

The visual information will be analyzed, in part, by the the researchers at the Computer Vision Group in Jena. The Moth Scanner and the Novelty Detection, Life-Long Learning and Classification sub-tasks of the visual AMMOD System (visAMMOD) will be tackled here.

Find more information on the project homepage (offline as of 2024) of the joint research project. It is funded by the Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung).

Individual Projects
Moth Scanner Teaser Moth Scanner

Dimitri Korsch

One camera, the so-called SpeciesMothCam, will take high-resolution images of insects that rest on an illuminated screen at regular intervals. The goal of monitoring is not to detect every species, but to describe trends in local insect populations. Seasonal activity differences of single species, the correlation with weather conditions and with vegetation phenology are important for ecological analyses. The SpeciesMothCam will deliver this type of data. The aim is to implement methods that enable an efficient detection of regions of interest (ROIs) on the screen and a classification of the species in this fine-grained setting. Taxonomic experts will help in the confirmation of the correct identification.

Time frame: 2019 – 2022

Moth Scanner Teaser Novelty Detection, Life-long Learning and Classification

Daphne Auer, Julia Böhlke

Another camera, the so-called SpeciesSiteCam, will capture stereo images and videos mainly of terrestrial animals (mammals) using motion sensors. To enable image acquisition during night and twilight, infrared illumination is switched on. Our goal is to build robust classification models that can identify different species. Since there are only a few annotated training image databases, one goal is to compile a first workable system that exploits the image data present in the World Wide Web. Julia Böhlke has written her Bachelor thesis on this topic which is also summarized in a paper. Active learning will play an important role to integrate unlabeled sample sets. Furthermore, a core element of AMMOD is novelty detection, an alerting mechanism in case that a so far unknown species has been recorded by the monitoring devices.

Time frame: 2019 – 2022

Publications

2022
Dimitri Korsch, Paul Bodesheim, Gunnar Brehm, Joachim Denzler:
Automated Visual Monitoring of Nocturnal Insects with Light-based Camera Traps.
CVPR Workshop on Fine-grained Visual Classification (CVPR-WS). 2022.
[bibtex] [pdf] [web] [code] [abstract]
J. Wolfgang Wägele, Paul Bodesheim, Sarah J. Bourlat, Joachim Denzler, Michael Diepenbroek, Vera Fonseca, Karl-Heinz Frommolt, Matthias F. Geiger, Birgit Gemeinholzer, Frank Oliver Glöckner, Timm Haucke, Ameli Kirse, Alexander Kölpin, Ivaylo Kostadinov, Hjalmar S. Kühl, Frank Kurth, Mario Lasseck, Sascha Liedke, Florian Losch, Sandra Müller, Natalia Petrovskaya, Krzysztof Piotrowski, Bernd Radig, Christoph Scherber, Lukas Schoppmann, Jan Schulz, Volker Steinhage, Georg F. Tschan, Wolfgang Vautz, Domenico Velotto, Maximilian Weigend, Stefan Wildermann:
Towards a multisensor station for automated biodiversity monitoring.
Basic and Applied Ecology. 59 : pp. 105-138. 2022.
[bibtex] [web] [doi] [abstract]
2021
Bernd Radig, Paul Bodesheim, Dimitri Korsch, Joachim Denzler, Timm Haucke, Morris Klasen, Volker Steinhage:
Automated Visual Large Scale Monitoring of Faunal Biodiversity.
Pattern Recognition and Image Analysis. Advances in Mathematical Theory and Applications (PRIA). 31 (3) : pp. 477-488. 2021.
[bibtex] [pdf] [web] [doi] [abstract]
Daphne Auer, Paul Bodesheim, Christian Fiderer, Marco Heurich, Joachim Denzler:
Minimizing the Annotation Effort for Detecting Wildlife in Camera Trap Images with Active Learning.
Computer Science for Biodiversity Workshop (CS4Biodiversity), INFORMATIK 2021. Pages 547-564. 2021.
[bibtex] [pdf] [doi] [abstract]
Dimitri Korsch, Paul Bodesheim, Joachim Denzler:
Deep Learning Pipeline for Automated Visual Moth Monitoring: Insect Localization and Species Classification.
INFORMATIK 2021, Computer Science for Biodiversity Workshop (CS4Biodiversity). Pages 443-460. 2021.
[bibtex] [pdf] [web] [doi] [code] [abstract]
Julia Böhlke, Dimitri Korsch, Paul Bodesheim, Joachim Denzler:
Exploiting Web Images for Moth Species Classification.
Computer Science for Biodiversity Workshop (CS4Biodiversity), INFORMATIK 2021. Pages 481-498. 2021.
[bibtex] [pdf] [web] [doi] [abstract]
Julia Böhlke, Dimitri Korsch, Paul Bodesheim, Joachim Denzler:
Lightweight Filtering of Noisy Web Data: Augmenting Fine-grained Datasets with Selected Internet Images.
International Conference on Computer Vision Theory and Applications (VISAPP). Pages 466-477. 2021.
[bibtex] [pdf] [web] [doi] [abstract]