# Human-Machine Collaboration for Fast Land Cover Mapping

arXiv: https://arxiv.org/abs/1906.04176 · HTML: https://mlsanity.com/paper/1906.04176/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2019.

## Built on by (most cited first, 9 of 9)

- [SatlasPretrain: A Large-Scale Dataset for Remote Sensing Image Understanding](https://mlsanity.com/paper/2211.15660/) (2022)
- [Mission Critical -- Satellite Data is a Distinct Modality in Machine Learning](https://mlsanity.com/paper/2402.01444/) (2024)
- [Resolving label uncertainty with implicit posterior models](https://mlsanity.com/paper/2202.14000/) (2022)
- [An Artificial Intelligence Dataset for Solar Energy Locations in India](https://mlsanity.com/paper/2202.01340/) (2022)
- [Boundary Aware U-Net for Glacier Segmentation](https://mlsanity.com/paper/2301.11454/) (2023)
- [Improving satellite imagery segmentation using multiple Sentinel-2 revisits](https://mlsanity.com/paper/2409.17363/) (2024)
- [FOCUS on Contamination: Hydrology-Informed Noise-Aware Learning for Geospatial PFAS Mapping](https://mlsanity.com/paper/2502.14894/) (2025)
- [HASTE: A Platform for Rapid Post-Disaster Building Damage Assessment](https://mlsanity.com/paper/2607.11838/) (2026)
- [FunnelAL: Retrieve-then-Rank Active Learning for Single-Class Discovery](https://mlsanity.com/paper/2607.25276/) (2026)

## Abstract (arXiv)

We propose incorporating human labelers in a model fine-tuning system that provides immediate user feedback. In our framework, human labelers can interactively query model predictions on unlabeled data, choose which data to label, and see the resulting effect on the model's predictions. This bi-directional feedback loop allows humans to learn how the model responds to new data. Our hypothesis is that this rich feedback allows human labelers to create mental models that enable them to better choose which biases to introduce to the model. We compare human-selected points to points selected using standard active learning methods. We further investigate how the fine-tuning methodology impacts the human labelers' performance. We implement this framework for fine-tuning high-resolution land cover segmentation models. Specifically, we fine-tune a deep neural network -- trained to segment high-resolution aerial imagery into different land cover classes in Maryland, USA -- to a new spatial area in New York, USA. The tight loop turns the algorithm and the human operator into a hybrid system that can produce land cover maps of a large area much more efficiently than the traditional workflows. Our framework has applications in geospatial machine learning settings where there is a practically limitless supply of unlabeled data, of which only a small fraction can feasibly be labeled through human efforts.