---
title: "SoilNet App: AI-Assisted Expert-level Annotations of Soil Horizons"
authors:
  - "Vipin Singh"
  - "Joey Pruessing"
  - "Teodor Chiaburu"
  - "Einar Eberhardt"
  - "Sina Hesse"
  - "Stefan Broda"
  - "Frank Haußer"
  - "Felix Biessmann"
doi: "10.24963/ijcai.2026/993"
url: "https://ijcai.org/proceedings/2026/993"
pdf: "https://ijcai.org/proceedings/2026/0993.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "8518-8521"
track: "Demo Track"
keywords:
  - "AI: Multidisciplinary Topics and Applications"
  - "AI: Computer Vision"
  - "AI: Humans and AI"
---

# SoilNet App: AI-Assisted Expert-level Annotations of Soil Horizons

Vipin Singh, Joey Pruessing, Teodor Chiaburu, Einar Eberhardt, Sina Hesse, Stefan Broda, Frank Haußer, Felix Biessmann

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 8518-8521. DOI: [10.24963/ijcai.2026/993](https://doi.org/10.24963/ijcai.2026/993)

## Abstract

Precise descriptions of soil horizons are required for policy makers, agriculture and many applications in civil engineering.  Up to date correct soil horizon annotations require human experts as they follow complex hierarchical taxonomies. We present the SoilNet App, a web-based demonstrator that guides experts through relevant tasks for expert-level soil horizon annotations from soil profile images. To demonstrate the reliability of the SoilNet app we present results of a user study with soil horizon annotation experts, which highlights the difficulty of image-only-based annotation and suggests that collaborating with our model not only increases expert performance but also improves inter-annotator consistency. Our app is publicly accessible (https://soilnet.demo.calgo-lab.de).