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talk · Friday 25 September · 08:45–⁠09:00 · FORUM

From Standardized Field Methods to AI-Ready Knowledge: The LEPMON Project with the LAUP Infrastructure Stack for Automated Moth Monitoring

Peter Grobe · AI Driven Monitoring and Species Identification

Recording time 49:05–1:07:15Open on Vimeo ↗

Summaries and key moments are machine-produced. Check the recording for the speaker’s exact words.

The short versionLEPMON's LAUP portal shows an end-to-end infrastructure for camera-based moth monitoring that builds Camtrap DP into routine data management, making millions of observations downloadable and analysable.

Overview

What this was about

Peter Grobe presented the German LEPMON project, a nationwide automated monitoring programme for nocturnal moths using standardised UV light-screen cameras (ARNI) along urbanisation gradients in eight cities, across habitats and via a citizen-science rollout, totalling around 104 devices. After two seasons it has 2.6 million raw images from over 17,000 nightly runs, yielding 4.3 million crop observations. He described the LAUP (LepMon Annotation and Upload) portal, which couples AI localisation and classification, citizen-science annotation and expert validation, and demonstrated a new map-based filter and download that exports data as a Camtrap DP package, with crops referenced as IIIF URLs rather than stored derivatives.

Why it matters. It is a working, large-scale example of the insect data standardisation challenges raised in the previous talk, showing how standardised field hardware, integrated annotation workflows and Camtrap DP export can make automated monitoring data usable for biodiversity-change research.

Key ideas

In the room

  • Lepidoptera make up about 16% of known insect species; Germany has about 2,200 microlepidoptera (mostly too small for cameras) and 1,440 macrolepidoptera, about 1,300 of them nocturnal, which are the project focus.
  • Deployment design: five traps per city along a sealing gradient from ~100% (centre) to 0% (outskirts) in eight cities, plus a 25-trap habitat programme, 17 externally funded traps and a citizen-science rollout, about 104 devices running March to October.
  • ARNI (Automatic Recorder of Nocturnal Insects) devices, in Pro and citizen-science versions largely 3D-printed, have a UV light, defined-size screen, environmental sensors and a dimmable flash, imaging every two minutes at the same resolution so images are comparable.
  • Time-lapse from Jena shows moth numbers peaking around 1 AM and declining by 4-5 AM; moths rarely rest, making tracking difficult.
  • Workflow: AI localisation and species classification, then citizen-science annotation of bounding boxes, then expert validation, since amateurs cannot reliably identify.
  • LAUP (LepMon Annotation and Upload) manages projects, devices and images and supports map-based filtering and download of data as a zipped Camtrap DP package (deployments, media, observations plus datapackage descriptor).
  • Crops are not stored: bounding boxes are stored as coordinates on the original image and each crop is addressed via a IIIF URL, saving space; observation identifiers link back to media and deployments.
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Programme abstract

From Standardized Field Methods to AI-Ready Knowledge: The LEPMON Project with the LAUP Infrastructure Stack for Automated Moth Monitoring

Peter Grobe

Automated biodiversity monitoring can deliver repeatable, high-throughput observations across sites and years, but only if standardization extends beyond field methods into the data infrastructure itself. In practice, images, locality information, detections, taxonomic predictions, human annotations, expert validations, and quality-control decisions are often fragmented across files, spreadsheets, and project-specific workflows. This fragmentation increases harmonization costs, weakens comparability, and limits the creation of trustworthy AI-ready biodiversity data. The LAUP software stack, developed for the German LEPMON project, addresses this by linking deployments, localities, media records, detections, AI-assisted classifications, human annotations, expert review, and publication outputs in one traceable workflow. Rather than treating standards as a final export step, LAUP makes structured curation, provenance tracking, and quality control part of routine operations. This preserves context, supports corrections, and documents how records change over time. A key extension is the integration of Camtrap DP as a core standard for data exchange and reuse. The aim is not merely to generate a Camtrap DP export at the end of the workflow, but to reflect its key concepts in the data infrastructure itself: by aligning deployments, media, and organism records with Camtrap DP concepts LAUP produces machine-actionable outputs that are easy to integrate into downstream analytical pipelines. We present LAUP as a practical bridge between standardized field monitoring, non-standardized data realities, and AI-ready biodiversity knowledge infrastructure, showing how domain-specific systems can turn raw image streams into curated, comparable, and reusable biodiversity evidence.

Submitted to the conference programme before the talk, so the talk itself may differ.