# TagLab: A human-centric AI system for interactive semantic segmentation

Source: [TagLab: A human-centric AI system for interactive semantic segmentation](<https://www.sota2.com/research/paper/68fa6b7a1e209131cea2e66e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:49:06.844Z

Authors: Gaia Pavoni, Massimiliano Corsini, Federico Ponchio, Alessandro Muntoni, Paolo Cignoni

Published: 2021-12-23T16:50:06.000Z

## Abstract

Fully automatic semantic segmentation of highly specific semantic classes and complex shapes may not meet the accuracy standards demanded by scientists. In such cases, human-centered AI solutions, able to assist operators while preserving human control over complex tasks, are a good trade-off to speed up image labeling while maintaining high accuracy levels. TagLab is an open-source AI-assisted software for annotating large orthoimages which takes advantage of different degrees of automation; it speeds up image annotation from scratch through assisted tools, creates custom fully automatic semantic segmentation models, and, finally, allows the quick edits of automatic predictions. Since the orthoimages analysis applies to several scientific disciplines, TagLab has been designed with a flexible labeling pipeline. We report our results in two different scenarios, marine ecology, and architectural heritage.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2112.12702>)

[PDF](<https://arxiv.org/pdf/2112.12702.pdf>)